WorldWideScience

Sample records for neural system birth

  1. Evolvable synthetic neural system

    Science.gov (United States)

    Curtis, Steven A. (Inventor)

    2009-01-01

    An evolvable synthetic neural system includes an evolvable neural interface operably coupled to at least one neural basis function. Each neural basis function includes an evolvable neural interface operably coupled to a heuristic neural system to perform high-level functions and an autonomic neural system to perform low-level functions. In some embodiments, the evolvable synthetic neural system is operably coupled to one or more evolvable synthetic neural systems in a hierarchy.

  2. Neural Systems Laboratory

    Data.gov (United States)

    Federal Laboratory Consortium — As part of the Electrical and Computer Engineering Department and The Institute for System Research, the Neural Systems Laboratory studies the functionality of the...

  3. Evolvable Neural Software System

    Science.gov (United States)

    Curtis, Steven A.

    2009-01-01

    The Evolvable Neural Software System (ENSS) is composed of sets of Neural Basis Functions (NBFs), which can be totally autonomously created and removed according to the changing needs and requirements of the software system. The resulting structure is both hierarchical and self-similar in that a given set of NBFs may have a ruler NBF, which in turn communicates with other sets of NBFs. These sets of NBFs may function as nodes to a ruler node, which are also NBF constructs. In this manner, the synthetic neural system can exhibit the complexity, three-dimensional connectivity, and adaptability of biological neural systems. An added advantage of ENSS over a natural neural system is its ability to modify its core genetic code in response to environmental changes as reflected in needs and requirements. The neural system is fully adaptive and evolvable and is trainable before release. It continues to rewire itself while on the job. The NBF is a unique, bilevel intelligence neural system composed of a higher-level heuristic neural system (HNS) and a lower-level, autonomic neural system (ANS). Taken together, the HNS and the ANS give each NBF the complete capabilities of a biological neural system to match sensory inputs to actions. Another feature of the NBF is the Evolvable Neural Interface (ENI), which links the HNS and ANS. The ENI solves the interface problem between these two systems by actively adapting and evolving from a primitive initial state (a Neural Thread) to a complicated, operational ENI and successfully adapting to a training sequence of sensory input. This simulates the adaptation of a biological neural system in a developmental phase. Within the greater multi-NBF and multi-node ENSS, self-similar ENI s provide the basis for inter-NBF and inter-node connectivity.

  4. Learning-induced neural plasticity of speech processing before birth.

    Science.gov (United States)

    Partanen, Eino; Kujala, Teija; Näätänen, Risto; Liitola, Auli; Sambeth, Anke; Huotilainen, Minna

    2013-09-10

    Learning, the foundation of adaptive and intelligent behavior, is based on plastic changes in neural assemblies, reflected by the modulation of electric brain responses. In infancy, auditory learning implicates the formation and strengthening of neural long-term memory traces, improving discrimination skills, in particular those forming the prerequisites for speech perception and understanding. Although previous behavioral observations show that newborns react differentially to unfamiliar sounds vs. familiar sound material that they were exposed to as fetuses, the neural basis of fetal learning has not thus far been investigated. Here we demonstrate direct neural correlates of human fetal learning of speech-like auditory stimuli. We presented variants of words to fetuses; unlike infants with no exposure to these stimuli, the exposed fetuses showed enhanced brain activity (mismatch responses) in response to pitch changes for the trained variants after birth. Furthermore, a significant correlation existed between the amount of prenatal exposure and brain activity, with greater activity being associated with a higher amount of prenatal speech exposure. Moreover, the learning effect was generalized to other types of similar speech sounds not included in the training material. Consequently, our results indicate neural commitment specifically tuned to the speech features heard before birth and their memory representations.

  5. Systematic review of birth prevalence of neural tube defects in India.

    Science.gov (United States)

    Bhide, Prajkta; Sagoo, Gurdeep S; Moorthie, Sowmiya; Burton, Hilary; Kar, Anita

    2013-07-01

    Neural tube defects are one of the most prevalent congenital anomalies. Data on the total birth prevalence, live birth and stillbirth prevalence of neural tube defects in India are lacking. The objective of this study is to conduct a systematic review of birth prevalence of neural tube defects in India and compare it with existing estimates. A PubMed search identified 463 articles, of which 19 articles were eligible for inclusion in the review. Meta-analysis was used to estimate the overall birth prevalence of neural tube defects and to investigate the variation among studies identified by this review. The 19 articles reported a total of 308,387 births, among which 1310 cases of neural tube defects were reported, giving an overall birth prevalence of 4.1 per 1000 (95% confidence interval [CI], 3.1-5.4). The live birth and stillbirth prevalence of neural tube defects was 1.3 per 1000 births (95% CI, 0.9-1.8) and 1.7 per 1000 births (95% CI, 0.7-4.0), respectively. Among the neural tube defects, the reported prevalence of anencephaly was highest at 2.1 per 1000 births (95% CI, 1.6-2.8) followed by spina bifida at 1.9 per 1000 births (95% CI, 1.4-2.7). The systematic review suggests that neural tube defects contribute to a significant number of live births and stillbirths in India, suggesting that preconception folic acid supplementation should be an essential element of reproductive health services. Copyright © 2013 Wiley Periodicals, Inc.

  6. Transforming the peruvian birth information system

    OpenAIRE

    Curioso, Walter H.; Oficina General de Estadística e Informática (OGEI), Ministerio de Salud. Lima, Perú. Médico, doctor en Informática Biomédica y magíster en Salud Pública.; Pardo, Karim; Oficina General de Estadística e Informática (OGEI), Ministerio de Salud. Lima, Perú. médico, magíster en Salud Pública.; Loayza, Manuel; Oficina General de Estadística e Informática (OGEI), Ministerio de Salud. Lima, Perú. médico, magister Salud Pública, epidemiólogo.

    2014-01-01

    The On-Line Registration of Certificates of Live Births was developed in conjunction with the Ministry of Health of Peru and the National Registry of Identification and Civil Status. It is a free system that registers newborns in the delivery room itself which generates a real time certificate of live birth. This simplifies the procedures required to obtain the birth certificate and the National Identity Document for the newborn. This system has been implemented in 114 health centers in 21...

  7. [Transforming the Peruvian birth information system].

    Science.gov (United States)

    Curioso, Walter H; Pardo, Karim; Loayza, Manuel

    2013-04-01

    The On-Line Registration of Certificates of Live Births was developed in conjunction with the Ministry of Health of Peru and the National Registry of Identification and Civil Status. It is a free system that registers newborns in the delivery room itself which generates a real time certificate of live birth. This simplifies the procedures required to obtain the birth certificate and the National Identity Document for the newborn. This system has been implemented in 114 health centers in 21 regions from March 2012 to March 2013, and has registered 113,917 live births, providing accurate, timely and quality information about these births. This system is the main cornerstone of the health information system which helps obtain real time statistics for adequate and timely decision making in public health.

  8. Neural systems for control

    National Research Council Canada - National Science Library

    Omidvar, Omid; Elliott, David L

    1997-01-01

    ... is reprinted with permission from A. Barto, "Reinforcement Learning," Handbook of Brain Theory and Neural Networks, M.A. Arbib, ed.. The MIT Press, Cambridge, MA, pp. 804-809, 1995. Chapter 4, Figures 4-5 and 7-9 and Tables 2-5, are reprinted with permission, from S. Cho, "Map Formation in Proprioceptive Cortex," International Jour...

  9. Degenerate coding in neural systems.

    Science.gov (United States)

    Leonardo, Anthony

    2005-11-01

    When the dimensionality of a neural circuit is substantially larger than the dimensionality of the variable it encodes, many different degenerate network states can produce the same output. In this review I will discuss three different neural systems that are linked by this theme. The pyloric network of the lobster, the song control system of the zebra finch, and the odor encoding system of the locust, while different in design, all contain degeneracies between their internal parameters and the outputs they encode. Indeed, although the dynamics of song generation and odor identification are quite different, computationally, odor recognition can be thought of as running the song generation circuitry backwards. In both of these systems, degeneracy plays a vital role in mapping a sparse neural representation devoid of correlations onto external stimuli (odors or song structure) that are strongly correlated. I argue that degeneracy between input and output states is an inherent feature of many neural systems, which can be exploited as a fault-tolerant method of reliably learning, generating, and discriminating closely related patterns.

  10. [Study on a back propogation neural network-based predictive model for prevalence of birth defect].

    Science.gov (United States)

    Wang, Wei; Xu, Wei; Zheng, Ya-jun; Zhou, Bao-sen

    2007-05-01

    To evaluate the value of a back propogation (BP) network on prediction of birth defect and to give clues on its prevention. Data of birth defect in Shenyang from 1995 to 2005 were used as a training set to predict the prevalence rate of birth defect. Neural network tools box of Software MATLAB 6.5 was used to train and simulate BP Artificial Neural Network. When using data of the year 1995-2003 to predict the prevalence rate of birth defect in 2004-2005, the results showed that: the fitting average error of prevalence rate was 1.34%, RNL was 0.9874, and the prediction of average error was 1.78%. Using data of the year 1995-2005 to predict the prevalence rate of birth defect in 2006-2007, the results showed that: the fitting average error was 0.33%, RNL was 0.9954, the prevalence rates of birth defect in 2006-2007 were 11.00% and 11.29%. Compared to the conventional statistics method, BP not only showed better prediction precision, but had no limit to the type or distribution of relevant data, thus providing a powerful method in epidemiological prediction.

  11. Memory Storage and Neural Systems.

    Science.gov (United States)

    Alkon, Daniel L.

    1989-01-01

    Investigates memory storage and molecular nature of associative-memory formation by analyzing Pavlovian conditioning in marine snails and rabbits. Presented is the design of a computer-based memory system (neural networks) using the rules acquired in the investigation. Reports that the artificial network recognized patterns well. (YP)

  12. Systemic sclerosis, birth order and parity.

    Science.gov (United States)

    Russo, Paul A J; Lester, Susan; Roberts-Thomson, Peter J

    2014-06-01

    A recent study identified increasing birth order to be a risk factor for the development of systemic sclerosis (SSc). This finding supports the theory that transplacental microchimerism may be a key pathological event in the initiation of SSc. We investigated the relationship between birth order and parity and the age of onset of SSc in South Australia. A retrospective analysis of patient data in the South Australian Scleroderma Register was performed. Data were obtained from a mailed questionnaire. Control data was collected prospectively using a similar questionnaire. The relationship between birth order, family size or parity and risk of subsequent development of SSc was analyzed by mixed effects logistic regression analysis. Three hundred and eighty-seven index probands were identified and compared with 457 controls. Controls were well matched for gender, but not for age. No statistically significant relationship was identified between SSc and birth order, parity in females, family size, age at first pregnancy in females or gender of first child in parous females. Our data suggests that parity, age at first pregnancy and the gender of the first child are not relevant factors in our understanding of the epidemiology and pathogenesis of SSc. Birth order and family size in both genders also appears irrelevant. These results argue against microchimerism as being relevant in the pathogenesis of SSc and add further support to the theory that stochastic events may be important in the etiopathogenesis of SSc. © 2013 Asia Pacific League of Associations for Rheumatology and Wiley Publishing Asia Pty Ltd.

  13. Reduction of birth prevalence rates of neural tube defects after folic acid fortification in Chile.

    Science.gov (United States)

    López-Camelo, Jorge S; Orioli, Iêda M; da Graça Dutra, Maria; Nazer-Herrera, Julio; Rivera, Nelson; Ojeda, María Elena; Canessa, Aurora; Wettig, Elisabeth; Fontannaz, Ana María; Mellado, Cecília; Castilla, Eduardo E

    2005-06-01

    To verify whether the decreasing neural tube defects birth prevalence rates in Chile are due to folic acid fortification or to pre-existing decreasing trends, we performed a population survey using a network of Estudio Colaborativo Latino Americano de Malformaciones Congenitas (ECLAMC, Latin American Collaborative Study of Congenital Malformations) maternity hospitals in Chile, between the years 1982 and 2002. Within each maternity hospital, birth prevalence rates of spina bifida and anencephaly were calculated from two pre-fortification periods (1982-1989 and 1990-2000), and from one fortified period (2001-2002). There was no historical trend for spina bifida birth prevalence rates before folic acid fortification, and there was a 51% (minimum 27%, maximum 66%) decrease in the birth prevalence rates of this anomaly in the fortified period. The relative risks of spina bifida were homogeneous among hospitals in the two period comparisons. There was no historical trend for the birth prevalence of anencephaly comparing the two pre-fortified periods, but the relative risks were heterogeneous among hospitals in this comparison. There was a 42% (minimum 10%, maximum 63%) decrease in the birth prevalence rate of anencephaly in the fortified period as compared with the immediately pre-fortified period, with homogeneous relative risks among hospitals. Within the methodological constraints of this study we conclude that the birth prevalence rates for both spina bifida and anencephaly decreased as a result of folic acid fortification, without interference of decreasing secular trends. Copyright (c) 2005 Wiley-Liss, Inc.

  14. The LILARTI neural network system

    Energy Technology Data Exchange (ETDEWEB)

    Allen, J.D. Jr.; Schell, F.M.; Dodd, C.V.

    1992-10-01

    The material of this Technical Memorandum is intended to provide the reader with conceptual and technical background information on the LILARTI neural network system of detail sufficient to confer an understanding of the LILARTI method as it is presently allied and to facilitate application of the method to problems beyond the scope of this document. Of particular importance in this regard are the descriptive sections and the Appendices which include operating instructions, partial listings of program output and data files, and network construction information.

  15. Births resulting from assisted reproductive technology: comparing birth certificate and National ART Surveillance System Data, 2011.

    Science.gov (United States)

    Thoma, Marie E; Boulet, Sheree; Martin, Joyce A; Kissin, Dmitry

    2014-12-10

    This report compares data on births resulting from assisted reproductive technology (ART) procedures from 2011 birth certificates with data from the 2011 National ART Surveillance System (NASS) among the subset of jurisdictions that adopted the 2003 revised birth certificate as of January 1, 2011, with information on ART. Birth certificate data are based on 100% of births registered in 27 states and the District of Columbia. NASS data included all ART cycles initiated in 2010 or 2011 for which a live birth in 2011 was reported. The same reporting area was used for both data sources and represents 67% of all births in the United States in 2011. A ratio was computed by dividing the percentage of births resulting from ART procedures for NASS data by the percentage for birth certificate data. A ratio of 1.0 represents equivalent levels of reporting. Because this reporting area is not a random sample of births, the results are not generalizable to the United States as a whole. Overall, the percentage of births resulting from ART procedures was 2.06 times higher for NASS data (1.44%) compared with birth certificate data (0.70%). The ratio for each jurisdiction varied from 1.04 for Utah and Wisconsin to 7.50 for Florida. Higher-risk groups had more consistent reporting. between data sources [e.g., triplet or higher-order multiples (1.36) compared with singletons (2.11)]. Births resulting from ART procedures appear to be underreported on the birth certificate; however, the magnitude of underreporting varied by jurisdiction and maternal-infant health characteristics.

  16. Birth prevalence of neural tube defects and orofacial clefts in India: a systematic review and meta-analysis.

    Science.gov (United States)

    Allagh, Komal Preet; Shamanna, B R; Murthy, Gudlavalleti V S; Ness, Andy R; Doyle, Pat; Neogi, Sutapa B; Pant, Hira B

    2015-01-01

    In the last two decades, India has witnessed a substantial decrease in infant mortality attributed to infectious disease and malnutrition. However, the mortality attributed to birth defects remains constant. Studies on the prevalence of birth defects such as neural tube defects and orofacial clefts in India have reported inconsistent results. Therefore, we conducted a systematic review of observational studies to document the birth prevalence of neural tube defects and orofacial clefts. A comprehensive literature search for observational studies was conducted in MEDLINE and EMBASE databases using key MeSH terms (neural tube defects OR cleft lip OR cleft palate AND Prevalence AND India). Two reviewers independently reviewed the retrieved studies, and studies satisfying the eligibility were included. The quality of included studies was assessed using selected criteria from STROBE statement. The overall pooled birth prevalence (random effect) of neural tube defects in India is 4.5 per 1000 total births (95% CI 4.2 to 4.9). The overall pooled birth prevalence (random effect) of orofacial clefts is 1.3 per 1000 total births (95% CI 1.1 to 1.5). Subgroup analyses were performed by region, time period, consanguinity, and gender of newborn. The overall prevalence of neural tube defects from India is high compared to other regions of the world, while that of orofacial clefts is similar to other countries. The majority of studies included in the review were hospital based. The quality of these studies ranged from low to moderate. Further well-designed, high quality community-based observational studies are needed to accurately estimate the burden of neural tube defects and orofacial clefts in India.

  17. Birth prevalence of neural tube defects and orofacial clefts in India: a systematic review and meta-analysis.

    Directory of Open Access Journals (Sweden)

    Komal Preet Allagh

    Full Text Available In the last two decades, India has witnessed a substantial decrease in infant mortality attributed to infectious disease and malnutrition. However, the mortality attributed to birth defects remains constant. Studies on the prevalence of birth defects such as neural tube defects and orofacial clefts in India have reported inconsistent results. Therefore, we conducted a systematic review of observational studies to document the birth prevalence of neural tube defects and orofacial clefts.A comprehensive literature search for observational studies was conducted in MEDLINE and EMBASE databases using key MeSH terms (neural tube defects OR cleft lip OR cleft palate AND Prevalence AND India. Two reviewers independently reviewed the retrieved studies, and studies satisfying the eligibility were included. The quality of included studies was assessed using selected criteria from STROBE statement.The overall pooled birth prevalence (random effect of neural tube defects in India is 4.5 per 1000 total births (95% CI 4.2 to 4.9. The overall pooled birth prevalence (random effect of orofacial clefts is 1.3 per 1000 total births (95% CI 1.1 to 1.5. Subgroup analyses were performed by region, time period, consanguinity, and gender of newborn.The overall prevalence of neural tube defects from India is high compared to other regions of the world, while that of orofacial clefts is similar to other countries. The majority of studies included in the review were hospital based. The quality of these studies ranged from low to moderate. Further well-designed, high quality community-based observational studies are needed to accurately estimate the burden of neural tube defects and orofacial clefts in India.

  18. Birth Prevalence of Neural Tube Defects and Orofacial Clefts in India: A Systematic Review and Meta-Analysis

    Science.gov (United States)

    Allagh, Komal Preet; Shamanna, B. R.; Murthy, Gudlavalleti V. S.; Ness, Andy R.; Doyle, Pat; Neogi, Sutapa B.; Pant, Hira B.

    2015-01-01

    Background In the last two decades, India has witnessed a substantial decrease in infant mortality attributed to infectious disease and malnutrition. However, the mortality attributed to birth defects remains constant. Studies on the prevalence of birth defects such as neural tube defects and orofacial clefts in India have reported inconsistent results. Therefore, we conducted a systematic review of observational studies to document the birth prevalence of neural tube defects and orofacial clefts. Methods A comprehensive literature search for observational studies was conducted in MEDLINE and EMBASE databases using key MeSH terms (neural tube defects OR cleft lip OR cleft palate AND Prevalence AND India). Two reviewers independently reviewed the retrieved studies, and studies satisfying the eligibility were included. The quality of included studies was assessed using selected criteria from STROBE statement. Results The overall pooled birth prevalence (random effect) of neural tube defects in India is 4.5 per 1000 total births (95% CI 4.2 to 4.9). The overall pooled birth prevalence (random effect) of orofacial clefts is 1.3 per 1000 total births (95% CI 1.1 to 1.5). Subgroup analyses were performed by region, time period, consanguinity, and gender of newborn. Conclusion The overall prevalence of neural tube defects from India is high compared to other regions of the world, while that of orofacial clefts is similar to other countries. The majority of studies included in the review were hospital based. The quality of these studies ranged from low to moderate. Further well-designed, high quality community-based observational studies are needed to accurately estimate the burden of neural tube defects and orofacial clefts in India. PMID:25768737

  19. Neural network based system for equipment surveillance

    Science.gov (United States)

    Vilim, R.B.; Gross, K.C.; Wegerich, S.W.

    1998-04-28

    A method and system are disclosed for performing surveillance of transient signals of an industrial device to ascertain the operating state. The method and system involves the steps of reading into a memory training data, determining neural network weighting values until achieving target outputs close to the neural network output. If the target outputs are inadequate, wavelet parameters are determined to yield neural network outputs close to the desired set of target outputs and then providing signals characteristic of an industrial process and comparing the neural network output to the industrial process signals to evaluate the operating state of the industrial process. 33 figs.

  20. Neural tube defects in Costa Rica, 1987-2012: origins and development of birth defect surveillance and folic acid fortification.

    Science.gov (United States)

    Barboza-Argüello, María de la Paz; Umaña-Solís, Lila M; Azofeifa, Alejandro; Valencia, Diana; Flores, Alina L; Rodríguez-Aguilar, Sara; Alfaro-Calvo, Thelma; Mulinare, Joseph

    2015-03-01

    Our aim was to provide a descriptive overview of how the birth defects surveillance and folic acid fortification programs were implemented in Costa Rica-through the establishment of the Registry Center for Congenital Anomalies (Centro de Registro de Enfermedades Congénitas-CREC), and fortification legislation mandates. We estimated the overall prevalence of neural tube defects (i.e., spina bifida, anencephaly and encephalocele) before and after fortification captured by CREC. Prevalence was calculated by dividing the total number of infants born with neural tube defects by the total number of live births in the country (1987-2012).A total of 1,170 newborns with neural tube defects were identified from 1987 to 2012 (1992-1995 data excluded); 628 were identified during the baseline pre-fortification period (1987-1991; 1996-1998); 191 during the fortification period (1999-2002); and 351 during the post-fortification time period (2003-2012). The overall prevalence of neural tube defects decreased from 9.8 per 10,000 live-births (95 % CI 9.1-10.5) for the pre-fortification period to 4.8 per 10,000 live births (95 % CI 4.3-5.3) for the post-fortification period. Results indicate a statistically significant (P Costa Rica. Costa Rica's experience can serve as an example for other countries seeking to develop and strengthen both their birth defects surveillance and fortification programs.

  1. Neural Tube Defects in Costa Rica, 1987–2012: Origins and Development of Birth Defect Surveillance and Folic Acid Fortification

    Science.gov (United States)

    de la Paz Barboza-Argüello, María; Umaña-Solís, Lila M.; Azofeifa, Alejandro; Valencia, Diana; Flores, Alina L.; Rodríguez-Aguilar, Sara; Alfaro-Calvo, Thelma; Mulinare, Joseph

    2015-01-01

    Our aim was to provide a descriptive overview of how the birth defects surveillance and folic acid fortification programs were implemented in Costa Rica—through the establishment of the Registry Center for Congenital Anomalies (Centro de Registro de Enfermedades Congénitas—CREC), and fortification legislation mandates. We estimated the overall prevalence of neural tube defects (i.e., spina bifida, anencephaly and encephalocele) before and after fortification captured by CREC. Prevalence was calculated by dividing the total number of infants born with neural tube defects by the total number of live births in the country (1987–2012).A total of 1,170 newborns with neural tube defects were identified from 1987 to 2012 (1992–1995 data excluded); 628 were identified during the baseline pre-fortification period (1987–1991; 1996–1998); 191 during the fortification period (1999–2002); and 351 during the post-fortification time period (2003–2012). The overall prevalence of neural tube defects decreased from 9.8 per 10,000 live-births (95 % CI 9.1–10.5) for the pre-fortification period to 4.8 per 10,000 live births (95 % CI 4.3–5.3) for the post–fortification period. Results indicate a statistically significant (P Costa Rica. Costa Rica’s experience can serve as an example for other countries seeking to develop and strengthen both their birth defects surveillance and fortification programs. PMID:24952876

  2. Neural systems for tactual memories.

    Science.gov (United States)

    Bonda, E; Petrides, M; Evans, A

    1996-04-01

    1. The aim of this study was to investigate the neural systems involved in the memory processing of experiences through touch. 2. Regional cerebral blood flow was measured with positron emission tomography by means of the water bolus H2(15)O methodology in human subjects as they performed tasks involving different levels of tactual memory. In one of the experimental tasks, the subjects had to palpate nonsense shapes to match each one to a previously learned set, thus requiring constant reference to long-term memory. The other experimental task involved judgements of the recent recurrence of shapes during the scanning period. A set of three control tasks was used to control for the type of exploratory movements and sensory processing inherent in the two experimental tasks. 3. Comparisons of the distribution of activity between the experimental and the control tasks were carried out by means of the subtraction method. In relation to the control conditions, the two experimental tasks requiring memory resulted in significant changes within the posteroventral insula and the central opercular region. In addition, the task requiring recall from long-term memory yielded changes in the perirhinal cortex. 4. The above findings demonstrated that a ventrally directed parietoinsular pathway, leading to the posteroventral insula and the perirhinal cortex, constitutes a system by which long-lasting representations of tactual experiences are formed. It is proposed that the posteroventral insula is involved in tactual feature analysis, by analogy with the similar role of the inferotemporal cortex in vision, whereas the perirhinal cortex is further involved in the integration of these features into long-lasting representations of somatosensory experiences.

  3. Preterm birth results in alterations in neural connectivity at age 16 years.

    Science.gov (United States)

    Mullen, Katherine M; Vohr, Betty R; Katz, Karol H; Schneider, Karen C; Lacadie, Cheryl; Hampson, Michelle; Makuch, Robert W; Reiss, Allan L; Constable, R Todd; Ment, Laura R

    2011-02-14

    Very low birth weight preterm (PT) children are at high risk for brain injury. Employing diffusion tensor imaging (DTI), we tested the hypothesis that PT adolescents would demonstrate microstructural white matter disorganization relative to term controls at 16 years of age. Forty-four PT subjects (600-1250 g birth weight) without neonatal brain injury and 41 term controls were evaluated at age 16 years with DTI, the Wechsler Intelligence Scale for Children-III (WISC), the Peabody Picture Vocabulary Test-Revised (PPVT), and the Comprehensive Test of Phonological Processing (CTOPP). PT subjects scored lower than term subjects on WISC full scale (p=0.003), verbal (p=0.043), and performance IQ tests (p=0.001), as well as CTOPP phonological awareness (p=0.004), but scored comparably to term subjects on PPVT and CTOPP Rapid Naming tests. PT subjects had lower fractional anisotropy (FA) values in multiple regions including bilateral uncinate fasciculi (left: p=0.01; right: p=0.004), bilateral external capsules (left: planguage task) in the PT subjects (left: r=0.314, p=0.038; right: r=0.336, p=0.026). FA values in the left and right arcuate fasciculi correlated with CTOPP Rapid Naming scores (a phonologic task) in the PT subjects (left: r=0.424, p=0.004; right: r=0.301, p=0.047). These data support for the first time that dual pathways underlying language function are present in PT adolescents. The striking bilateral dorsal correlations for the PT group suggest that prematurely born subjects rely more heavily on the right hemisphere than typically developing adults for performance of phonological language tasks. These findings may represent either a delay in maturation or the engagement of alternative neural pathways for language in the developing PT brain. Copyright © 2010 Elsevier Inc. All rights reserved.

  4. Spiking neural P systems with multiple channels.

    Science.gov (United States)

    Peng, Hong; Yang, Jinyu; Wang, Jun; Wang, Tao; Sun, Zhang; Song, Xiaoxiao; Luo, Xiaohui; Huang, Xiangnian

    2017-11-01

    Spiking neural P systems (SNP systems, in short) are a class of distributed parallel computing systems inspired from the neurophysiological behavior of biological spiking neurons. In this paper, we investigate a new variant of SNP systems in which each neuron has one or more synaptic channels, called spiking neural P systems with multiple channels (SNP-MC systems, in short). The spiking rules with channel label are introduced to handle the firing mechanism of neurons, where the channel labels indicate synaptic channels of transmitting the generated spikes. The computation power of SNP-MC systems is investigated. Specifically, we prove that SNP-MC systems are Turing universal as both number generating and number accepting devices. Copyright © 2017 Elsevier Ltd. All rights reserved.

  5. Evaluating neural networks and artificial intelligence systems

    Science.gov (United States)

    Alberts, David S.

    1994-02-01

    Systems have no intrinsic value in and of themselves, but rather derive value from the contributions they make to the missions, decisions, and tasks they are intended to support. The estimation of the cost-effectiveness of systems is a prerequisite for rational planning, budgeting, and investment documents. Neural network and expert system applications, although similar in their incorporation of a significant amount of decision-making capability, differ from each other in ways that affect the manner in which they can be evaluated. Both these types of systems are, by definition, evolutionary systems, which also impacts their evaluation. This paper discusses key aspects of neural network and expert system applications and their impact on the evaluation process. A practical approach or methodology for evaluating a certain class of expert systems that are particularly difficult to measure using traditional evaluation approaches is presented.

  6. Neural Plasticity in the Gustatory System

    OpenAIRE

    Hill, David L.

    2004-01-01

    Sensory systems adapt to changing environmental influences by coordinated alterations in structure and function. These alterations are referred to as plastic changes. The gustatory system displays numerous plastic changes even in receptor cells. This review focuses on the plasticity of gustatory structures through the first synaptic relay in the brain. Unlike other sensory systems, there is a remarkable amount of environmentally induced changes in these peripheral-most neural structures. The ...

  7. Neural compensation in adulthood following very preterm birth demonstrated during a visual paired associates learning task

    Directory of Open Access Journals (Sweden)

    Philip J. Brittain

    2014-01-01

    Full Text Available Very preterm birth (VPT; < 33 weeks of gestation is associated with an increased risk of learning disability, which contributes to more VPT-born children repeating grades and underachieving in school. Learning problems associated with VPT birth may be caused by pathophysiological alterations in neurodevelopment resulting from perinatal brain insult; however, adaptive neuroplastic processes may subsequently occur in the developing preterm brain which ameliorate, to an extent, the potential sequelae of altered neurophysiology. Here, we used functional magnetic resonance imaging (fMRI to compare neuronal activation in 24 VPT individuals and 22 controls (CT in young adulthood during a learning task consisting of the encoding and subsequent recognition of repeated visual paired associates. Structural MRI data were also collected and analysed in order to explore possible structure-function associations. Whilst the two groups did not differ in their learning ability, as demonstrated by their capacity to recognize previously-seen and previously–unseen visual pairs, between-group differences in linear patterns of Blood Oxygenation Level Dependant (BOLD activity were observed across the four repeated blocks of the task for both the encoding and recognition conditions, suggesting that the way learning takes place differs between the two groups. During encoding, significant between-group differences in patterns of BOLD activity were seen in clusters centred on the cerebellum, the anterior cingulate gyrus, the midbrain/substantia nigra, medial temporal (including parahippocampal gyrus and inferior and superior frontal gyri. During the recognition condition, significant between-group differences in patterns of BOLD activity were seen in clusters centred on the claustrum and the posterior cerebellum. Structural analysis revealed smaller grey matter volume in right middle temporal gyrus in VPT individuals compared to controls, however volume in this region

  8. System and method for determining stability of a neural system

    Science.gov (United States)

    Curtis, Steven A. (Inventor)

    2011-01-01

    Disclosed are methods, systems, and computer-readable media for determining stability of a neural system. The method includes tracking a function world line of an N element neural system within at least one behavioral space, determining whether the tracking function world line is approaching a psychological stability surface, and implementing a quantitative solution that corrects instability if the tracked function world line is approaching the psychological stability surface.

  9. Unveiling neural coupling within the sensorimotor system : directionality and nonlinearity

    NARCIS (Netherlands)

    Yang, Y.; Dewald, J.P.A.; van der Helm, F.C.T.; Schouten, A.C.

    2017-01-01

    Neural coupling between the central nervous system and the periphery is essential for the neural control of movement. Corticomuscular coherence is a popular linear technique to assess synchronised oscillatory activity in the sensorimotor system. This oscillatory coupling originates from ascending

  10. Neural compensation in adulthood following very preterm birth demonstrated during a visual paired associates learning task.

    Science.gov (United States)

    Brittain, Philip J; Froudist Walsh, Sean; Nam, Kie-Woo; Giampietro, Vincent; Karolis, Vyacheslav; Murray, Robin M; Bhattacharyya, Sagnik; Kalpakidou, Anastasia; Nosarti, Chiara

    2014-01-01

    Very preterm birth (VPT; brain insult; however, adaptive neuroplastic processes may subsequently occur in the developing preterm brain which ameliorate, to an extent, the potential sequelae of altered neurophysiology. Here, we used functional magnetic resonance imaging (fMRI) to compare neuronal activation in 24 VPT individuals and 22 controls (CT) in young adulthood during a learning task consisting of the encoding and subsequent recognition of repeated visual paired associates. Structural MRI data were also collected and analysed in order to explore possible structure-function associations. Whilst the two groups did not differ in their learning ability, as demonstrated by their capacity to recognize previously-seen and previously-unseen visual pairs, between-group differences in linear patterns of Blood Oxygenation Level Dependant (BOLD) activity were observed across the four repeated blocks of the task for both the encoding and recognition conditions, suggesting that the way learning takes place differs between the two groups. During encoding, significant between-group differences in patterns of BOLD activity were seen in clusters centred on the cerebellum, the anterior cingulate gyrus, the midbrain/substantia nigra, medial temporal (including parahippocampal) gyrus and inferior and superior frontal gyri. During the recognition condition, significant between-group differences in patterns of BOLD activity were seen in clusters centred on the claustrum and the posterior cerebellum. Structural analysis revealed smaller grey matter volume in right middle temporal gyrus in VPT individuals compared to controls, however volume in this region was not significantly associated with functional activation. These results demonstrate that although cognitive task performance between VPT individuals and controls may be comparable on certain measures, differences in BOLD signal may also be evident, some of which could represent compensatory neural processes following VPT

  11. Simulating neural systems with Xyce.

    Energy Technology Data Exchange (ETDEWEB)

    Schiek, Richard Louis; Thornquist, Heidi K.; Mei, Ting; Warrender, Christina E.; Aimone, James Bradley; Teeter, Corinne; Duda, Alex M.

    2012-12-01

    Sandias parallel circuit simulator, Xyce, can address large scale neuron simulations in a new way extending the range within which one can perform high-fidelity, multi-compartment neuron simulations. This report documents the implementation of neuron devices in Xyce, their use in simulation and analysis of neuron systems.

  12. IMPLEMENTATION OF NEURAL - CRYPTOGRAPHIC SYSTEM USING FPGA

    Directory of Open Access Journals (Sweden)

    KARAM M. Z. OTHMAN

    2011-08-01

    Full Text Available Modern cryptography techniques are virtually unbreakable. As the Internet and other forms of electronic communication become more prevalent, electronic security is becoming increasingly important. Cryptography is used to protect e-mail messages, credit card information, and corporate data. The design of the cryptography system is a conventional cryptography that uses one key for encryption and decryption process. The chosen cryptography algorithm is stream cipher algorithm that encrypt one bit at a time. The central problem in the stream-cipher cryptography is the difficulty of generating a long unpredictable sequence of binary signals from short and random key. Pseudo random number generators (PRNG have been widely used to construct this key sequence. The pseudo random number generator was designed using the Artificial Neural Networks (ANN. The Artificial Neural Networks (ANN providing the required nonlinearity properties that increases the randomness statistical properties of the pseudo random generator. The learning algorithm of this neural network is backpropagation learning algorithm. The learning process was done by software program in Matlab (software implementation to get the efficient weights. Then, the learned neural network was implemented using field programmable gate array (FPGA.

  13. The Artifical Neural Network as means for modeling Nonlinear Systems

    OpenAIRE

    Drábek Oldøich; Taufer Ivan

    1998-01-01

    The paper deals with nonlinear system identification based on neural network. The topic of this publication is simulation of training and testing a neural network. A contribution is assigned to technologists which are good at the clasical identification problems but their knowledges about identification based on neural network are only on the stage of theoretical bases.

  14. The Artifical Neural Network as means for modeling Nonlinear Systems

    Directory of Open Access Journals (Sweden)

    Drábek Oldøich

    1998-12-01

    Full Text Available The paper deals with nonlinear system identification based on neural network. The topic of this publication is simulation of training and testing a neural network. A contribution is assigned to technologists which are good at the clasical identification problems but their knowledges about identification based on neural network are only on the stage of theoretical bases.

  15. Convergent evolution of neural systems in ctenophores.

    Science.gov (United States)

    Moroz, Leonid L

    2015-02-15

    Neurons are defined as polarized secretory cells specializing in directional propagation of electrical signals leading to the release of extracellular messengers - features that enable them to transmit information, primarily chemical in nature, beyond their immediate neighbors without affecting all intervening cells en route. Multiple origins of neurons and synapses from different classes of ancestral secretory cells might have occurred more than once during ~600 million years of animal evolution with independent events of nervous system centralization from a common bilaterian/cnidarian ancestor without the bona fide central nervous system. Ctenophores, or comb jellies, represent an example of extensive parallel evolution in neural systems. First, recent genome analyses place ctenophores as a sister group to other animals. Second, ctenophores have a smaller complement of pan-animal genes controlling canonical neurogenic, synaptic, muscle and immune systems, and developmental pathways than most other metazoans. However, comb jellies are carnivorous marine animals with a complex neuromuscular organization and sophisticated patterns of behavior. To sustain these functions, they have evolved a number of unique molecular innovations supporting the hypothesis of massive homoplasies in the organization of integrative and locomotory systems. Third, many bilaterian/cnidarian neuron-specific genes and 'classical' neurotransmitter pathways are either absent or, if present, not expressed in ctenophore neurons (e.g. the bilaterian/cnidarian neurotransmitter, γ-amino butyric acid or GABA, is localized in muscles and presumed bilaterian neuron-specific RNA-binding protein Elav is found in non-neuronal cells). Finally, metabolomic and pharmacological data failed to detect either the presence or any physiological action of serotonin, dopamine, noradrenaline, adrenaline, octopamine, acetylcholine or histamine - consistent with the hypothesis that ctenophore neural systems evolved

  16. Spacecraft Neural Network Control System Design using FPGA

    OpenAIRE

    Hanaa T. El-Madany; Faten H. Fahmy; Ninet M. A. El-Rahman; Hassen T. Dorrah

    2011-01-01

    Designing and implementing intelligent systems has become a crucial factor for the innovation and development of better products of space technologies. A neural network is a parallel system, capable of resolving paradigms that linear computing cannot. Field programmable gate array (FPGA) is a digital device that owns reprogrammable properties and robust flexibility. For the neural network based instrument prototype in real time application, conventional specific VLSI neural chip design suffer...

  17. Artificial Neural Network System for Thyroid Diagnosis

    Directory of Open Access Journals (Sweden)

    Mazin Abdulrasool Hameed

    2017-05-01

    Full Text Available Thyroid disease is one of major causes of severe medical problems for human beings. Therefore, proper diagnosis of thyroid disease is considered as an important issue to determine treatment for patients. This paper focuses on using Artificial Neural Network (ANN as a significant technique of artificial intelligence to diagnose thyroid diseases. The continuous values of three laboratory blood tests are used as input signals to the proposed system of ANN. All types of thyroid diseases that may occur in patients are taken into account in design of system, as well as the high accuracy of the detection and categorization of thyroid diseases are considered in the system. A multilayer feedforward architecture of ANN is adopted in the proposed design, and the back propagation is selected as learning algorithm to accomplish the training process. The result of this research shows that the proposed ANN system is able to precisely diagnose thyroid disease, and can be exploited in practical uses. The system is simulated via MATLAB software to evaluate its performance

  18. Neural network system for traffic flow management

    Science.gov (United States)

    Gilmore, John F.; Elibiary, Khalid J.; Petersson, L. E. Rickard

    1992-09-01

    Atlanta will be the home of several special events during the next five years ranging from the 1996 Olympics to the 1994 Super Bowl. When combined with the existing special events (Braves, Falcons, and Hawks games, concerts, festivals, etc.), the need to effectively manage traffic flow from surface streets to interstate highways is apparent. This paper describes a system for traffic event response and management for intelligent navigation utilizing signals (TERMINUS) developed at Georgia Tech for adaptively managing special event traffic flows in the Atlanta, Georgia area. TERMINUS (the original name given Atlanta, Georgia based upon its role as a rail line terminating center) is an intelligent surface street signal control system designed to manage traffic flow in Metro Atlanta. The system consists of three components. The first is a traffic simulation of the downtown Atlanta area around Fulton County Stadium that models the flow of traffic when a stadium event lets out. Parameters for the surrounding area include modeling for events during various times of day (such as rush hour). The second component is a computer graphics interface with the simulation that shows the traffic flows achieved based upon intelligent control system execution. The final component is the intelligent control system that manages surface street light signals based upon feedback from control sensors that dynamically adapt the intelligent controller's decision making process. The intelligent controller is a neural network model that allows TERMINUS to control the configuration of surface street signals to optimize the flow of traffic away from special events.

  19. Neural Network for Optimization of Existing Control Systems

    DEFF Research Database (Denmark)

    Madsen, Per Printz

    1995-01-01

    The purpose of this paper is to develop methods to use Neural Network based Controllers (NNC) as an optimization tool for existing control systems.......The purpose of this paper is to develop methods to use Neural Network based Controllers (NNC) as an optimization tool for existing control systems....

  20. Cell-specific deletion of glucosylceramide synthase in brain leads to severe neural defects after birth

    Science.gov (United States)

    Jennemann, Richard; Sandhoff, Roger; Wang, Shijun; Kiss, Eva; Gretz, Norbert; Zuliani, Cecilia; Martin-Villalba, Ana; Jäger, Richard; Schorle, Hubert; Kenzelmann, Marc; Bonrouhi, Mahnaz; Wiegandt, Herbert; Gröne, Hermann-Josef

    2005-01-01

    Sialic acid-containing glycosphingolipids, i.e., gangliosides, constitute a major component of neuronal cells and are thought to be essential for brain function. UDP-glucose:ceramide glucosyltransferase (Ugcg) catalyzes the initial step of glycosphingolipid (GSL) biosynthesis. To gain insight into the role of GSLs in brain development and function, a cell-specific disruption of Ugcg was performed as indicated by the absence of virtually all glucosylceramide-based GSLs. Shortly after birth, mice showed dysfunction of cerebellum and peripheral nerves, associated with structural defects. Axon branching of Purkinje cells was significantly reduced. In primary cultures of neurons, dendritic complexity was clearly diminished, and pruning occurred early. Myelin sheaths of peripheral nerves were broadened and focally severely disorganized. GSL deficiency also led to a down-regulation of gene expression sets involved in brain development and homeostasis. Mice died ≈3 weeks after birth. These results imply that GSLs are essential for brain maturation. PMID:16109770

  1. The Effects of GABAergic Polarity Changes on Episodic Neural Network Activity in Developing Neural Systems

    Directory of Open Access Journals (Sweden)

    Wilfredo Blanco

    2017-09-01

    Full Text Available Early in development, neural systems have primarily excitatory coupling, where even GABAergic synapses are excitatory. Many of these systems exhibit spontaneous episodes of activity that have been characterized through both experimental and computational studies. As development progress the neural system goes through many changes, including synaptic remodeling, intrinsic plasticity in the ion channel expression, and a transformation of GABAergic synapses from excitatory to inhibitory. What effect each of these, and other, changes have on the network behavior is hard to know from experimental studies since they all happen in parallel. One advantage of a computational approach is that one has the ability to study developmental changes in isolation. Here, we examine the effects of GABAergic synapse polarity change on the spontaneous activity of both a mean field and a neural network model that has both glutamatergic and GABAergic coupling, representative of a developing neural network. We find some intuitive behavioral changes as the GABAergic neurons go from excitatory to inhibitory, shared by both models, such as a decrease in the duration of episodes. We also find some paradoxical changes in the activity that are only present in the neural network model. In particular, we find that during early development the inter-episode durations become longer on average, while later in development they become shorter. In addressing this unexpected finding, we uncover a priming effect that is particularly important for a small subset of neurons, called the “intermediate neurons.” We characterize these neurons and demonstrate why they are crucial to episode initiation, and why the paradoxical behavioral change result from priming of these neurons. The study illustrates how even arguably the simplest of developmental changes that occurs in neural systems can present non-intuitive behaviors. It also makes predictions about neural network behavioral changes

  2. Neural Network Based Intelligent Sootblowing System

    Energy Technology Data Exchange (ETDEWEB)

    Mark Rhode

    2005-04-01

    . Due to the composition of coal, particulate matter is also a by-product of coal combustion. Modern day utility boilers are usually fitted with electrostatic precipitators to aid in the collection of particulate matter. Although extremely efficient, these devices are sensitive to rapid changes in inlet mass concentration as well as total mass loading. Traditionally, utility boilers are equipped with devices known as sootblowers, which use, steam, water or air to dislodge and clean the surfaces within the boiler and are operated based upon established rule or operator's judgment. Poor sootblowing regimes can influence particulate mass loading to the electrostatic precipitators. The project applied a neural network intelligent sootblowing system in conjunction with state-of-the-art controls and instruments to optimize the operation of a utility boiler and systematically control boiler slagging/fouling. This optimization process targeted reduction of NOx of 30%, improved efficiency of 2% and a reduction in opacity of 5%. The neural network system proved to be a non-invasive system which can readily be adapted to virtually any utility boiler. Specific conclusions from this neural network application are listed below. These conclusions should be used in conjunction with the specific details provided in the technical discussions of this report to develop a thorough understanding of the process.

  3. Analog neural network-based helicopter gearbox health monitoring system.

    Science.gov (United States)

    Monsen, P T; Dzwonczyk, M; Manolakos, E S

    1995-12-01

    The development of a reliable helicopter gearbox health monitoring system (HMS) has been the subject of considerable research over the past 15 years. The deployment of such a system could lead to a significant saving in lives and vehicles as well as dramatically reduce the cost of helicopter maintenance. Recent research results indicate that a neural network-based system could provide a viable solution to the problem. This paper presents two neural network-based realizations of an HMS system. A hybrid (digital/analog) neural system is proposed as an extremely accurate off-line monitoring tool used to reduce helicopter gearbox maintenance costs. In addition, an all analog neural network is proposed as a real-time helicopter gearbox fault monitor that can exploit the ability of an analog neural network to directly compute the discrete Fourier transform (DFT) as a sum of weighted samples. Hardware performance results are obtained using the Integrated Neural Computing Architecture (INCA/1) analog neural network platform that was designed and developed at The Charles Stark Draper Laboratory. The results indicate that it is possible to achieve a 100% fault detection rate with 0% false alarm rate by performing a DFT directly on the first layer of INCA/1 followed by a small-size two-layer feed-forward neural network and a simple post-processing majority voting stage.

  4. The ctenophore genome and the evolutionary origins of neural systems

    NARCIS (Netherlands)

    Moroz, Leonid L.; Kocot, Kevin M.; Citarella, Mathew R.; Dosung, Sohn; Norekian, Tigran P.; Povolotskaya, Inna S.; Grigorenko, Anastasia P.; Dailey, Christopher; Berezikov, Eugene; Buckley, Katherine M.; Ptitsyn, Andrey; Reshetov, Denis; Mukherjee, Krishanu; Moroz, Tatiana P.; Bobkova, Yelena; Yu, Fahong; Kapitonov, Vladimir V.; Jurka, Jerzy; Bobkov, Yuri V.; Swore, Joshua J.; Girardo, David O.; Fodor, Alexander; Gusev, Fedor; Sanford, Rachel; Bruders, Rebecca; Kittler, Ellen; Mills, Claudia E.; Rast, Jonathan P.; Derelle, Romain; Solovyev, Victor V.; Kondrashov, Fyodor A.; Swalla, Billie J.; Sweedler, Jonathan V.; Rogaev, Evgeny I.; Halanych, Kenneth M.; Kohn, Andrea B.

    2014-01-01

    The origins of neural systems remain unresolved. In contrast to other basal metazoans, ctenophores (comb jellies) have both complex nervous and mesoderm-derived muscular systems. These holoplanktonic predators also have sophisticated ciliated locomotion, behaviour and distinct development. Here we

  5. The Survey of Birth Defects Rate Based on Birth Registration System

    Directory of Open Access Journals (Sweden)

    Min Yu

    2015-01-01

    Conclusions: A downward trend of birth defects was observed in Xi′an City from 2003 to 2012. NTDs significantly decreased after large-scale supplemental folic acid intervention, while the incidence rate of CHD significantly increased.

  6. Spiking Neural P Systems with Communication on Request.

    Science.gov (United States)

    Pan, Linqiang; Păun, Gheorghe; Zhang, Gexiang; Neri, Ferrante

    2017-12-01

    Spiking Neural [Formula: see text] Systems are Neural System models characterized by the fact that each neuron mimics a biological cell and the communication between neurons is based on spikes. In the Spiking Neural [Formula: see text] systems investigated so far, the application of evolution rules depends on the contents of a neuron (checked by means of a regular expression). In these [Formula: see text] systems, a specified number of spikes are consumed and a specified number of spikes are produced, and then sent to each of the neurons linked by a synapse to the evolving neuron. [Formula: see text]In the present work, a novel communication strategy among neurons of Spiking Neural [Formula: see text] Systems is proposed. In the resulting models, called Spiking Neural [Formula: see text] Systems with Communication on Request, the spikes are requested from neighboring neurons, depending on the contents of the neuron (still checked by means of a regular expression). Unlike the traditional Spiking Neural [Formula: see text] systems, no spikes are consumed or created: the spikes are only moved along synapses and replicated (when two or more neurons request the contents of the same neuron). [Formula: see text]The Spiking Neural [Formula: see text] Systems with Communication on Request are proved to be computationally universal, that is, equivalent with Turing machines as long as two types of spikes are used. Following this work, further research questions are listed to be open problems.

  7. Neural Systems for Speech and Song in Autism

    Science.gov (United States)

    Lai, Grace; Pantazatos, Spiro P.; Schneider, Harry; Hirsch, Joy

    2012-01-01

    Despite language disabilities in autism, music abilities are frequently preserved. Paradoxically, brain regions associated with these functions typically overlap, enabling investigation of neural organization supporting speech and song in autism. Neural systems sensitive to speech and song were compared in low-functioning autistic and age-matched…

  8. Short-term synaptic plasticity and heterogeneity in neural systems

    Science.gov (United States)

    Mejias, J. F.; Kappen, H. J.; Longtin, A.; Torres, J. J.

    2013-01-01

    We review some recent results on neural dynamics and information processing which arise when considering several biophysical factors of interest, in particular, short-term synaptic plasticity and neural heterogeneity. The inclusion of short-term synaptic plasticity leads to enhanced long-term memory capacities, a higher robustness of memory to noise, and irregularity in the duration of the so-called up cortical states. On the other hand, considering some level of neural heterogeneity in neuron models allows neural systems to optimize information transmission in rate coding and temporal coding, two strategies commonly used by neurons to codify information in many brain areas. In all these studies, analytical approximations can be made to explain the underlying dynamics of these neural systems.

  9. Dopamine system: Manager of neural pathways

    Directory of Open Access Journals (Sweden)

    Simon eHong

    2013-12-01

    Full Text Available There are a growing number of roles that midbrain dopamine (DA neurons assume, such as, reward, aversion, alerting and vigor. Here I propose a theory that may be able to explain why the suggested functions of DA came about. It has been suggested that largely parallel cortico-basal ganglia-thalamo-cortico loops exist to control different aspects of behavior. I propose that (1 the midbrain DA system is organized in a similar manner, with different groups of DA neurons corresponding to these parallel neural pathways (NPs. The DA system can be viewed as the manager of these parallel NPs in that it recruits and activates only the task-relevant NPs when they are needed. It is likely that the functions of those NPs that have been consistently activated by the corresponding DA groups are facilitated. I also propose that (2 there are two levels of DA roles: the How and What roles. The How role is encoded in tonic and phasic DA neuron firing patterns and gives a directive to its target NP: how vigorously its function needs to be carried out. The tonic DA firing is to maintain a certain level of DA in the target NPs to support their expected behavioral and mental functions; it is only when a sudden unexpected boost or suppression of activity is required by the relevant target NP that DA neurons in the corresponding NP act in a phasic manner. The What role is the implementational aspect of the role of DA in the target NP, such as binding to D1 receptors to boost working memory. This What aspect of DA explains why DA seems to assume different functions depending on the region of the brain in which it is involved. In terms of the role of the lateral habenula (LHb, the LHb is expected to suppress maladaptive behaviors and mental processes by controlling the DA system. The demand-based smart management by the DA system may have given animals an edge in evolution with adaptive behaviors and a better survival rate in resource-scarce situations.

  10. Optical production systems using neural networks and symbolic substitution

    Science.gov (United States)

    Botha, Elizabeth; Casasent, David; Barnard, Etienne

    1988-01-01

    Two optical implementations of production systems are advanced. The production systems operate on a knowledge base where facts and rules are encoded as formulas in propositional calculus. The first implementation is a binary neural network. An analog neural network is used to include reasoning with uncertainties. The second implementation uses a new optical symbolic substitution correlator. This implementation is useful when a set of similar situations has to be handled in parallel on one processor.

  11. Genetic learning in rule-based and neural systems

    Science.gov (United States)

    Smith, Robert E.

    1993-01-01

    The design of neural networks and fuzzy systems can involve complex, nonlinear, and ill-conditioned optimization problems. Often, traditional optimization schemes are inadequate or inapplicable for such tasks. Genetic Algorithms (GA's) are a class of optimization procedures whose mechanics are based on those of natural genetics. Mathematical arguments show how GAs bring substantial computational leverage to search problems, without requiring the mathematical characteristics often necessary for traditional optimization schemes (e.g., modality, continuity, availability of derivative information, etc.). GA's have proven effective in a variety of search tasks that arise in neural networks and fuzzy systems. This presentation begins by introducing the mechanism and theoretical underpinnings of GA's. GA's are then related to a class of rule-based machine learning systems called learning classifier systems (LCS's). An LCS implements a low-level production-system that uses a GA as its primary rule discovery mechanism. This presentation illustrates how, despite its rule-based framework, an LCS can be thought of as a competitive neural network. Neural network simulator code for an LCS is presented. In this context, the GA is doing more than optimizing and objective function. It is searching for an ecology of hidden nodes with limited connectivity. The GA attempts to evolve this ecology such that effective neural network performance results. The GA is particularly well adapted to this task, given its naturally-inspired basis. The LCS/neural network analogy extends itself to other, more traditional neural networks. Conclusions to the presentation discuss the implications of using GA's in ecological search problems that arise in neural and fuzzy systems.

  12. Multiple neural network approaches to clinical expert systems

    Science.gov (United States)

    Stubbs, Derek F.

    1990-08-01

    We briefly review the concept of computer aided medical diagnosis and more extensively review the the existing literature on neural network applications in the field. Neural networks can function as simple expert systems for diagnosis or prognosis. Using a public database we develop a neural network for the diagnosis of a major presenting symptom while discussing the development process and possible approaches. MEDICAL EXPERTS SYSTEMS COMPUTER AIDED DIAGNOSIS Biomedicine is an incredibly diverse and multidisciplinary field and it is not surprising that neural networks with their many applications are finding more and more applications in the highly non-linear field of biomedicine. I want to concentrate on neural networks as medical expert systems for clinical diagnosis or prognosis. Expert Systems started out as a set of computerized " ifthen" rules. Everything was reduced to boolean logic and the promised land of computer experts was said to be in sight. It never came. Why? First the computer code explodes as the number of " ifs" increases. All the " ifs" have to interact. Second experts are not very good at reducing expertise to language. It turns out that experts recognize patterns and have non-verbal left-brain intuition decision processes. Third learning by example rather than learning by rule is the way natural brains works and making computers work by rule-learning is hideously labor intensive. Neural networks can learn from example. They learn the results

  13. System Identification, Prediction, Simulation and Control with Neural Networks

    DEFF Research Database (Denmark)

    Sørensen, O.

    1997-01-01

    a Gauss-Newton search direction is applied. 3) Amongst numerous model types, often met in control applications, only the Non-linear ARMAX (NARMAX) model, representing input/output description, is examined. A simulated example confirms that a neural network has the potential to perform excellent System...... Identification, Prediction, Simulation and Control of a dynamic, non-linear and noisy process. Further, the difficulties to control a practical non-linear laboratory process in a satisfactory way by using a traditional controller are overcomed by using a trained neural network to perform non-linear System......The intention of this paper is to make a systematic examination of the possibilities of applying neural networks in those technical areas, which are familiar to a control engineer. In other words, the potential of neural networks in control applications is given higher priority than a detailed...

  14. The Criticality Hypothesis in Neural Systems

    Science.gov (United States)

    Karimipanah, Yahya

    There is mounting evidence that neural networks of the cerebral cortex exhibit scale invariant dynamics. At the larger scale, fMRI recordings have shown evidence for spatiotemporal long range correlations. On the other hand, at the smaller scales this scale invariance is marked by the power law distribution of the size and duration of spontaneous bursts of activity, which are referred as neuronal avalanches. The existence of such avalanches has been confirmed by several studies in vitro and in vivo, among different species and across multiple scales, from spatial scale of MEG and EEG down to single cell resolution. This prevalent scale free nature of cortical activity suggests the hypothesis that the cortex resides at a critical state between two phases of order (short-lasting activity) and disorder (long-lasting activity). In addition, it has been shown, both theoretically and experimentally, that being at criticality brings about certain functional advantages for information processing. However, despite the plenty of evidence and plausibility of the neural criticality hypothesis, still very little is known on how the brain may leverage such criticality to facilitate neural coding. Moreover, the emergent functions that may arise from critical dynamics is poorly understood. In the first part of this thesis, we review several pieces of evidence for the neural criticality hypothesis at different scales, as well as some of the most popular theories of self-organized criticality (SOC). Thereafter, we will focus on the most prominent evidence from small scales, namely neuronal avalanches. We will explore the effect of adaptation and how it can maintain scale free dynamics even at the presence of external stimuli. Using calcium imaging we also experimentally demonstrate the existence of scale free activity at the cellular resolution in vivo. Moreover, by exploring the subsampling issue in neural data, we will find some fundamental constraints of the conventional methods

  15. HOSPITAL BASED BIRTH DEFECTS SURVEILLANCE WITH SPECIAL REFERENCE TO NEURAL TUBE DEFECTS: A STUDY OF ONE YEAR STATISTICS FROM A TERTIARY CARE GOVERNMENT TEACHING HOSPITAL OF NORTH COASTAL ANDHRA PRADESH

    Directory of Open Access Journals (Sweden)

    Manda Venkata

    2016-05-01

    Full Text Available INTRODUCTION Birth defects (also called congenital anomalies are defined as abnormalities of both structures or functions that are present at birth and are of prenatal origin. They contribute to a significant proportion of perinatal, neonatal & childhood mortality as well as morbidity. AIM To study at birth, prevalence of selected birth defects among babies delivered at King George Hospital, Visakhapatnam, from Jan 2015 to December 2015, appropriate management strategy of the most common neural tube defects. RESULTS Among the total deliveries of 6088 in the above period, the incidences of birth defects were 88 (1.44%, the most common among them were neural tube defects. CONCLUSION Birth defect surveillance and standardisation of the data is essential to understand the public health burden and to design appropriate prenatal prevention programs.

  16. Home birth integration into the health care systems of eleven international jurisdictions.

    Science.gov (United States)

    Comeau, Amanda; Hutton, Eileen K; Simioni, Julia; Anvari, Ella; Bowen, Megan; Kruegar, Samantha; Darling, Elizabeth K

    2018-02-13

    The purpose of this study was to develop assessment criteria that could be used to examine the level of integration of home birth within larger health care systems in developed countries across 11 international jurisdictions. An expert panel developed criteria and a definition to assess home birth integration within health care systems. We selected jurisdictions based on the publications that were eligible for inclusion in our systematic review and meta-analysis on planned place of birth. We sent the authors of the included publications a questionnaire about home birth practitioners and practices in their respective health care system at the time of their studies. We searched published peer-reviewed, non-peer-reviewed, and gray literature, and the websites of professional bodies to document information about home birth integration in each jurisdiction based on our criteria. Where information was lacking, we contacted experts in the field from the relevant jurisdiction. Home birth is well integrated into the health care system in British Columbia (Canada), England, Iceland, the Netherlands, New Zealand, Ontario (Canada), and Washington State (USA). Home birth is less well integrated into the health care system in Australia, Japan, Norway, and Sweden. This paper is the first to propose criteria for the evaluation of home birth integration within larger maternity care systems. Application of these criteria across 11 international jurisdictions indicates differences in the recognition and training of home birth practitioners, in access to hospital facilities, and in the supplies and equipment available at home births, which give rise to variation in the level of integration across different settings. Standardized criteria for the evaluation of systems integration are essential for interpreting planned home birth outcomes that emerge from contextual differences. © 2018 Wiley Periodicals, Inc.

  17. An artificial neural network controller for intelligent transportation systems applications

    Energy Technology Data Exchange (ETDEWEB)

    Vitela, J.E.; Hanebutte, U.R.; Reifman, J. [Argonne National Lab., IL (United States). Reactor Analysis Div.

    1996-04-01

    An Autonomous Intelligent Cruise Control (AICC) has been designed using a feedforward artificial neural network, as an example for utilizing artificial neural networks for nonlinear control problems arising in intelligent transportation systems applications. The AICC is based on a simple nonlinear model of the vehicle dynamics. A Neural Network Controller (NNC) code developed at Argonne National Laboratory to control discrete dynamical systems was used for this purpose. In order to test the NNC, an AICC-simulator containing graphical displays was developed for a system of two vehicles driving in a single lane. Two simulation cases are shown, one involving a lead vehicle with constant velocity and the other a lead vehicle with varying acceleration. More realistic vehicle dynamic models will be considered in future work.

  18. Neural signal sampling via the low power wireless pico system.

    Science.gov (United States)

    Cieslewski, Grzegorz; Cheney, David; Gugel, Karl; Sanchez, Justin C; Principe, Jose C

    2006-01-01

    This paper presents a powerful new low power wireless system for sampling multiple channels of neural activity based on Texas Instruments MSP430 microprocessors and Nordic Semiconductor's ultra low power high bandwidth RF transmitters and receivers. The system's development process, component selection, features and test methodology are presented.

  19. Role of neural network models for developing speech systems

    Indian Academy of Sciences (India)

    These prosody models are further examined for applications such as text to speech synthesis, speech recognition, speaker recognition and language identification. Neural network models in voice conversion system are explored for capturing the mapping functions between source and target speakers at source, system and ...

  20. NNSYSID - toolbox for system identification with neural networks

    DEFF Research Database (Denmark)

    Norgaard, M.; Ravn, Ole; Poulsen, Niels Kjølstad

    2002-01-01

    The NNSYSID toolset for System Identification has been developed as an add on to MATLAB(R). The NNSYSID toolbox has been designed to assist identification of nonlinear dynamic systems. It contains a number of nonlinear model structures based on neural networks, effective training algorithms...

  1. Neural expert decision support system for stroke diagnosis

    Science.gov (United States)

    Kupershtein, Leonid M.; Martyniuk, Tatiana B.; Krencin, Myhail D.; Kozhemiako, Andriy V.; Bezsmertnyi, Yurii; Bezsmertna, Halyna; Kolimoldayev, Maksat; Smolarz, Andrzej; Weryńska-Bieniasz, RóŻa; Uvaysova, Svetlana

    2017-08-01

    In the work the hybrid expert system for stroke diagnosis was presented. The base of expert system consists of neural network and production rules. This program can quickly and accurately set to the patient preliminary and final diagnoses, get examination and treatment plans, print data of patient, analyze statistics data and perform parameterized search for patients.

  2. Reject mechanisms for massively parallel neural network character recognition systems

    Science.gov (United States)

    Garris, Michael D.; Wilson, Charles L.

    1992-12-01

    Two reject mechanisms are compared using a massively parallel character recognition system implemented at NIST. The recognition system was designed to study the feasibility of automatically recognizing hand-printed text in a loosely constrained environment. The first method is a simple scalar threshold on the output activation of the winning neurode from the character classifier network. The second method uses an additional neural network trained on all outputs from the character classifier network to accept or reject assigned classifications. The neural network rejection method was expected to perform with greater accuracy than the scalar threshold method, but this was not supported by the test results presented. The scalar threshold method, even though arbitrary, is shown to be a viable reject mechanism for use with neural network character classifiers. Upon studying the performance of the neural network rejection method, analyses show that the two neural networks, the character classifier network and the rejection network, perform very similarly. This can be explained by the strong non-linear function of the character classifier network which effectively removes most of the correlation between character accuracy and all activations other than the winning activation. This suggests that any effective rejection network must receive information from the system which has not been filtered through the non-linear classifier.

  3. Neural-network-based fuzzy logic decision systems

    Science.gov (United States)

    Kulkarni, Arun D.; Giridhar, G. B.; Coca, Praveen

    1994-10-01

    During the last few years there has been a large and energetic upswing in research efforts aimed at synthesizing fuzzy logic with neural networks. This combination of neural networks and fuzzy logic seems natural because the two approaches generally attack the design of `intelligent' system from quite different angles. Neural networks provide algorithms for learning, classification, and optimization whereas fuzzy logic often deals with issues such as reasoning in a high (semantic or linguistic) level. Consequently the two technologies complement each other. In this paper, we combine neural networks with fuzzy logic techniques. We propose an artificial neural network (ANN) model for a fuzzy logic decision system. The model consists of six layers. The first three layers map the input variables to fuzzy set membership functions. The last three layers implement the decision rules. The model learns the decision rules using a supervised gradient descent procedure. As an illustration we considered two examples. The first example deals with pixel classification in multispectral satellite images. In our second example we used the fuzzy decision system to analyze data from magnetic resonance imaging (MRI) scans for tissue classification.

  4. Adaptive Synchronization of Memristor-based Chaotic Neural Systems

    Directory of Open Access Journals (Sweden)

    Xiaofang Hu

    2014-11-01

    Full Text Available Chaotic neural networks consisting of a great number of chaotic neurons are able to reproduce the rich dynamics observed in biological nervous systems. In recent years, the memristor has attracted much interest in the efficient implementation of artificial synapses and neurons. This work addresses adaptive synchronization of a class of memristor-based neural chaotic systems using a novel adaptive backstepping approach. A systematic design procedure is presented. Simulation results have demonstrated the effectiveness of the proposed adaptive synchronization method and its potential in practical application of memristive chaotic oscillators in secure communication.

  5. Epidemiology of Birth Defects Based on a Birth Defect Surveillance System from 2005 to 2014 in Hunan Province, China.

    Directory of Open Access Journals (Sweden)

    Donghua Xie

    Full Text Available To describe the epidemiology of birth defects (BDs in perinatal infants in Hunan Province, China, between 2005 and 2014.The BD surveillance data of perinatal infants (for stillbirth, dead fetus or live birth between 28 weeks of gestation and 7 days after birth were collected from 52 registered hospitals of Hunan between 2005 and 2014. The prevalence rates of BDs with 95% confidence interval (CI and crude odds ratio (ORs were calculated to examine the associations of infant gender, maternal age, and region (urban vs rural with BDs.From 2005 to 2014, there were a total of 925413 perinatal infants of which 17753 had BDs, with the average prevalence of 191.84 per 10000 PIs (perinatal infants, showing a significant uptrend. The risks of BDs are higher in urban areas versus rural areas (OR = 1.20, in male infants versus female infants (OR = 1.19, and in mothers above age 35 versus those below age 35 (OR = 1.24. The main five types of BDs are Congenital heart defects (CHD, Other malformation of external ear (OMEE, Polydactyly, Congenital malformation of kidney (CMK, and Congenital talipes equinovarus (CTE. From 2005 to 2014, the prevalence rates (per 10000 PIs of CHD and CMK increased significantly from 22.56 to 74 (OR = 3.29, 95%CI: 2.65-4.11 and from 7.61 to 14.62 (OR = 1.92, 95%CI:1.30-2.84, respectively; the prevalence rates of congenital hydrocephalus and neural tube defects (NTDs decreased significantly from 11.8 to 5.29 (OR = 0.45, 95%CI: 0.31-0.65 and from 7.87 to 1.74 (OR = 0.22, 95%CI: 0.13-0.38, respectively.The prevalence rates of specific BDs in perinatal infants in Hunan have changed in the last decade. Urban pregnant women, male perinatal infants, and mothers above age 35 present different prevalence rates of BDs. Wider use of new diagnosis technology, improving the ability of monitoring, strengthening the publicity and education are important to reduce the prevalence of BDs.

  6. Reliability Modeling of Microelectromechanical Systems Using Neural Networks

    Science.gov (United States)

    Perera. J. Sebastian

    2000-01-01

    Microelectromechanical systems (MEMS) are a broad and rapidly expanding field that is currently receiving a great deal of attention because of the potential to significantly improve the ability to sense, analyze, and control a variety of processes, such as heating and ventilation systems, automobiles, medicine, aeronautical flight, military surveillance, weather forecasting, and space exploration. MEMS are very small and are a blend of electrical and mechanical components, with electrical and mechanical systems on one chip. This research establishes reliability estimation and prediction for MEMS devices at the conceptual design phase using neural networks. At the conceptual design phase, before devices are built and tested, traditional methods of quantifying reliability are inadequate because the device is not in existence and cannot be tested to establish the reliability distributions. A novel approach using neural networks is created to predict the overall reliability of a MEMS device based on its components and each component's attributes. The methodology begins with collecting attribute data (fabrication process, physical specifications, operating environment, property characteristics, packaging, etc.) and reliability data for many types of microengines. The data are partitioned into training data (the majority) and validation data (the remainder). A neural network is applied to the training data (both attribute and reliability); the attributes become the system inputs and reliability data (cycles to failure), the system output. After the neural network is trained with sufficient data. the validation data are used to verify the neural networks provided accurate reliability estimates. Now, the reliability of a new proposed MEMS device can be estimated by using the appropriate trained neural networks developed in this work.

  7. Space-time system architecture for the neural optical computing

    Science.gov (United States)

    Lo, Yee-Man V.

    1991-02-01

    The brain can perform the tasks of associative recall detection recognition and optimization. In this paper space-time system field models of the brain are introduced. They are called the space-time maximum likelihood associative memory system (ST-ML-AMS) and the space-time adaptive learning system (ST-ALS). Performance of the system is analyzed using the probability of error in memory recall (PEMR) and the space-time neural capacity (ST-NC). 1.

  8. Seasonality of birth in children with central nervous system tumours in Denmark, 1970-2003

    DEFF Research Database (Denmark)

    Schmidt, L S; Grell, Kathrine; Frederiksen, K

    2008-01-01

    We investigated possible seasonal variation of births among children <20 years with a central nervous system tumour in Denmark (N=1640), comparing them with 2,582,714 children born between 1970 and 2003. No such variation was seen overall, but ependymoma showed seasonal variation.......We investigated possible seasonal variation of births among children

  9. A dynamical systems view of motor preparation: Implications for neural prosthetic system design

    Science.gov (United States)

    Shenoy, Krishna V.; Kaufman, Matthew T.; Sahani, Maneesh; Churchland, Mark M.

    2013-01-01

    Neural prosthetic systems aim to help disabled patients suffering from a range of neurological injuries and disease by using neural activity from the brain to directly control assistive devices. This approach in effect bypasses the dysfunctional neural circuitry, such as an injured spinal cord. To do so, neural prostheses depend critically on a scientific understanding of the neural activity that drives them. We review here several recent studies aimed at understanding the neural processes in premotor cortex that precede arm movements and lead to the initiation of movement. These studies were motivated by hypotheses and predictions conceived of within a dynamical systems perspective. This perspective concentrates on describing the neural state using as few degrees of freedom as possible and on inferring the rules that govern the motion of that neural state. Although quite general, this perspective has led to a number of specific predictions that have been addressed experimentally. It is hoped that the resulting picture of the dynamical role of preparatory and movement-related neural activity will be particularly helpful to the development of neural prostheses, which can themselves be viewed as dynamical systems under the control of the larger dynamical system to which they are attached. PMID:21763517

  10. A NEURAL NETWORK BASED IRIS RECOGNITION SYSTEM FOR PERSONAL IDENTIFICATION

    Directory of Open Access Journals (Sweden)

    Usham Dias

    2010-10-01

    Full Text Available This paper presents biometric personal identification based on iris recognition using artificial neural networks. Personal identification system consists of localization of the iris region, normalization, enhancement and then iris pattern recognition using neural network. In this paper, through results obtained, we have shown that a person’s left and right eye are unique. In this paper, we also show that the network is sensitive to the initial weights and that over-training gives bad results. We also propose a fast algorithm for the localization of the inner and outer boundaries of the iris region. Results of simulations illustrate the effectiveness of the neural system in personal identification. Finally a hardware iris recognition model is proposed and implementation aspects are discussed.

  11. Optical neural network system for pose determination of spinning satellites

    Science.gov (United States)

    Lee, Andrew; Casasent, David

    1990-01-01

    An optical neural network architecture and algorithm based on a Hopfield optimization network are presented for multitarget tracking. This tracker utilizes a neuron for every possible target track, and a quadratic energy function of neural activities which is minimized using gradient descent neural evolution. The neural net tracker is demonstrated as part of a system for determining position and orientation (pose) of spinning satellites with respect to a robotic spacecraft. The input to the system is time sequence video from a single camera. Novelty detection and filtering are utilized to locate and segment novel regions from the input images. The neural net multitarget tracker determines the correspondences (or tracks) of the novel regions as a function of time, and hence the paths of object (satellite) parts. The path traced out by a given part or region is approximately elliptical in image space, and the position, shape and orientation of the ellipse are functions of the satellite geometry and its pose. Having a geometric model of the satellite, and the elliptical path of a part in image space, the three-dimensional pose of the satellite is determined. Digital simulation results using this algorithm are presented for various satellite poses and lighting conditions.

  12. Using fuzzy logic to integrate neural networks and knowledge-based systems

    Science.gov (United States)

    Yen, John

    1991-01-01

    Outlined here is a novel hybrid architecture that uses fuzzy logic to integrate neural networks and knowledge-based systems. The author's approach offers important synergistic benefits to neural nets, approximate reasoning, and symbolic processing. Fuzzy inference rules extend symbolic systems with approximate reasoning capabilities, which are used for integrating and interpreting the outputs of neural networks. The symbolic system captures meta-level information about neural networks and defines its interaction with neural networks through a set of control tasks. Fuzzy action rules provide a robust mechanism for recognizing the situations in which neural networks require certain control actions. The neural nets, on the other hand, offer flexible classification and adaptive learning capabilities, which are crucial for dynamic and noisy environments. By combining neural nets and symbolic systems at their system levels through the use of fuzzy logic, the author's approach alleviates current difficulties in reconciling differences between low-level data processing mechanisms of neural nets and artificial intelligence systems.

  13. Epidemiologic study of neural tube defects in Los Angeles County. I. Prevalence at birth based on multiple sources of case ascertainment

    Energy Technology Data Exchange (ETDEWEB)

    Sever, L.E. (Pacific Northwest Lab., Richland, WA); Sanders, M.; Monsen, R.

    1982-01-01

    Epidemiologic studies of the neural tube defects (NTDs), anencephalus and spina bifida, have for the most part been based on single sources of case ascertainment in past studies. The present investigation attempts total ascertainment of NTD cases in the newborn population of Los Angeles County residents for the period 1966 to 1972. Design of the study, sources of data, and estimates of prevalence rates based on single and multiple sources of case ascertainment are here discussed. Anencephalus cases totaled 448, spina bifida 442, and encephalocele 72, giving prevalence rates of 0.52, 0.51, and 0.08 per 1000 total births, respectively, for these neural tube defects - rates considered to be low. The Los Angeles County prevalence rates are compared with those of other recent North American studies and support is provided for earlier suggestions of low rates on the West Coast.

  14. Classical Conditioning with Pulsed Integrated Neural Networks: Circuits and System

    DEFF Research Database (Denmark)

    Lehmann, Torsten

    1998-01-01

    In this paper we investigate on-chip learning for pulsed, integrated neural networks. We discuss the implementational problems the technology imposes on learning systems and we find that abiologically inspired approach using simple circuit structures is most likely to bring success. We develop a ...... chip to solve simple classical conditioning tasks, thus verifying the design methodologies put forward in the paper....

  15. Dynamic causal models of neural system dynamics: current state ...

    Indian Academy of Sciences (India)

    2006-09-28

    Sep 28, 2006 ... Keywords. Dynamic causal modelling; EEG; effective connectivity; event-related potentials; fMRI; neural system ... In this article, we review the conceptual and mathematical basis of DCM and its implementation for functional magnetic resonance imaging data and event-related potentials. After introducing ...

  16. Neural network based system for script identification in Indian ...

    Indian Academy of Sciences (India)

    R. Narasimhan (Krishtel eMaging) 1461 1996 Oct 15 13:05:22

    environments. The system developed includes a feature extractor and a modular neural network. The feature extractor consists of two stages. In the first stage ... environments is script/language identification (Muthusamy et al 1994; Hochberg et al 1997). ... In order to take advantage of the learning and generalization abilities ...

  17. A breathing circuit alarm system based on neural networks.

    Science.gov (United States)

    Orr, J A; Westenskow, D R

    1994-03-01

    The objectives of our study were (1) to implement intelligent respiratory alarms with a neural network; and (2) to increase alarm specificity and decrease false-alarm rates compared with current alarms. We trained a neural network to recognize 13 faults in an anesthesia breathing circuit. The system extracted 30 breath-to-breath features from the airway CO2, flow, and pressure signals. We created training data for the network by introducing 13 faults repeatedly in 5 dogs (616 total faults). We used the data to train the neural network using the backward error propagation algorithm. In animals, the trained network reported the alarms correctly for 95.0% of the faults when tested during controlled ventilation, and for 86.9% of the faults during spontaneous breathing. When tested in the operating room, the system found and correctly reported 54 of 57 faults that occurred during 43.6 hr of use. The alarm system produced a total of 74 false alarms during 43.6 hr of monitoring. Neural networks may be useful in creating intelligent anesthesia alarm systems.

  18. KCNQ potassium channels in sensory system and neural circuits.

    Science.gov (United States)

    Wang, Jing-jing; Li, Yang

    2016-01-01

    M channels, an important regulator of neural excitability, are composed of four subunits of the Kv7 (KCNQ) K(+) channel family. M channels were named as such because their activity was suppressed by stimulation of muscarinic acetylcholine receptors. These channels are of particular interest because they are activated at the subthreshold membrane potentials. Furthermore, neural KCNQ channels are drug targets for the treatments of epilepsy and a variety of neurological disorders, including chronic and neuropathic pain, deafness, and mental illness. This review will update readers on the roles of KCNQ channels in the sensory system and neural circuits as well as discuss their respective mechanisms and the implications for physiology and medicine. We will also consider future perspectives and the development of additional pharmacological models, such as seizure, stroke, pain and mental illness, which work in combination with drug-design targeting of KCNQ channels. These models will hopefully deepen our understanding of KCNQ channels and provide general therapeutic prospects of related channelopathies.

  19. Gene-environment interactions and the enteric nervous system: Neural plasticity and Hirschsprung disease prevention.

    Science.gov (United States)

    Heuckeroth, Robert O; Schäfer, Karl-Herbert

    2016-09-15

    Intestinal function is primarily controlled by an intrinsic nervous system of the bowel called the enteric nervous system (ENS). The cells of the ENS are neural crest derivatives that migrate into and through the bowel during early stages of organogenesis before differentiating into a wide variety of neurons and glia. Although genetic factors critically underlie ENS development, it is now clear that many non-genetic factors may influence the number of enteric neurons, types of enteric neurons, and ratio of neurons to glia. These non-genetic influences include dietary nutrients and medicines that may impact ENS structure and function before or after birth. This review summarizes current data about gene-environment interactions that affect ENS development and suggests that these factors may contribute to human intestinal motility disorders like Hirschsprung disease or irritable bowel syndrome. Copyright © 2016 Elsevier Inc. All rights reserved.

  20. Gene-environment interactions and the enteric nervous system: Neural plasticity and Hirschsprung disease prevention

    Science.gov (United States)

    Heuckeroth, Robert O.; Schäfer, Karl-Herbert

    2016-01-01

    Intestinal function is primarily controlled by an intrinsic nervous system of the bowel called the enteric nervous system (ENS). The cells of the ENS are neural crest derivatives that migrate into and through the bowel during early stages of organogenesis before differentiating into a wide variety of neurons and glia. Although genetic factors critically underlie ENS development, it is now clear that many non-genetic factors may influence the number of enteric neurons, types of enteric neurons, and ratio of neurons to glia. These non-genetic influences include dietary nutrients and medicines that may impact ENS structure and function before or after birth. This review summarizes current data about gene-environment interactions that affect ENS development and suggests that these factors may contribute to human intestinal motility disorders like Hirschsprung disease or irritable bowel syndrome. PMID:26997034

  1. Frequency-difference-dependent stochastic resonance in neural systems

    Science.gov (United States)

    Guo, Daqing; Perc, Matjaž; Zhang, Yangsong; Xu, Peng; Yao, Dezhong

    2017-08-01

    Biological neurons receive multiple noisy oscillatory signals, and their dynamical response to the superposition of these signals is of fundamental importance for information processing in the brain. Here we study the response of neural systems to the weak envelope modulation signal, which is superimposed by two periodic signals with different frequencies. We show that stochastic resonance occurs at the beat frequency in neural systems at the single-neuron as well as the population level. The performance of this frequency-difference-dependent stochastic resonance is influenced by both the beat frequency and the two forcing frequencies. Compared to a single neuron, a population of neurons is more efficient in detecting the information carried by the weak envelope modulation signal at the beat frequency. Furthermore, an appropriate fine-tuning of the excitation-inhibition balance can further optimize the response of a neural ensemble to the superimposed signal. Our results thus introduce and provide insights into the generation and modulation mechanism of the frequency-difference-dependent stochastic resonance in neural systems.

  2. Internal models and neural computation in the vestibular system.

    Science.gov (United States)

    Green, Andrea M; Angelaki, Dora E

    2010-01-01

    The vestibular system is vital for motor control and spatial self-motion perception. Afferents from the otolith organs and the semicircular canals converge with optokinetic, somatosensory and motor-related signals in the vestibular nuclei, which are reciprocally interconnected with the vestibulocerebellar cortex and deep cerebellar nuclei. Here, we review the properties of the many cell types in the vestibular nuclei, as well as some fundamental computations implemented within this brainstem-cerebellar circuitry. These include the sensorimotor transformations for reflex generation, the neural computations for inertial motion estimation, the distinction between active and passive head movements, as well as the integration of vestibular and proprioceptive information for body motion estimation. A common theme in the solution to such computational problems is the concept of internal models and their neural implementation. Recent studies have shed new insights into important organizational principles that closely resemble those proposed for other sensorimotor systems, where their neural basis has often been more difficult to identify. As such, the vestibular system provides an excellent model to explore common neural processing strategies relevant both for reflexive and for goal-directed, voluntary movement as well as perception.

  3. Synthesis of recurrent neural networks for dynamical system simulation.

    Science.gov (United States)

    Trischler, Adam P; D'Eleuterio, Gabriele M T

    2016-08-01

    We review several of the most widely used techniques for training recurrent neural networks to approximate dynamical systems, then describe a novel algorithm for this task. The algorithm is based on an earlier theoretical result that guarantees the quality of the network approximation. We show that a feedforward neural network can be trained on the vector-field representation of a given dynamical system using backpropagation, then recast it as a recurrent network that replicates the original system's dynamics. After detailing this algorithm and its relation to earlier approaches, we present numerical examples that demonstrate its capabilities. One of the distinguishing features of our approach is that both the original dynamical systems and the recurrent networks that simulate them operate in continuous time. Copyright © 2016 Elsevier Ltd. All rights reserved.

  4. How well do birth records serve maternal and child health programs? Birth registration system evaluation, New York City, 2008-2011.

    Science.gov (United States)

    Howland, Renata E; Madsen, Ann M; Toprani, Amita; Gambatese, Melissa; Mulready-Ward, Candace; Begier, Elizabeth

    2015-07-01

    National birth registration guidelines were revised in 2003 to improve data quality; however, few studies have evaluated the impact on local jurisdictions and their data users. In New York City (NYC), approximately 125,000 births are registered annually with the NYC Department of Health and Mental Hygiene, and data are used routinely by the department's maternal and child health (MCH) programs. In order to better meet MCH program needs, we used Centers for Disease Control and Prevention guidelines to assess birth data usefulness, simplicity, data quality, timeliness and representativeness. We interviewed birth registration and MCH program staff, reviewed a 2009 survey of birth registrars (n = 39), and analyzed 2008-2011 birth records for timeliness and completeness (n = 502,274). Thirteen MCH programs use birth registration data for eligibility determination, needs assessment, program evaluation, and surveillance. Demographic variables are used frequently, nearly 100 % complete, and considered the gold standard by programs; in contrast, medical variables' use and validity varies widely. Seventy-seven percent of surveyed birth registrars reported ≥1 problematic items in the system; 64.1 % requested further training. During 2008-2011, the median interval between birth and registration was 5 days (range 0-260 days); 11/13 programs were satisfied with timeliness. The NYC birth registration system provides local MCH programs useful, timely, and representative data. However, some medical items are difficult to collect, of low quality, and rarely used. We recommend enhancing training for birth registrars, continuing quality improvement efforts, increasing collaboration with program users, and removing consistently low-quality and low-use variables.

  5. Neural - fuzzy approach for system identification

    NARCIS (Netherlands)

    Tien, B.T.

    1997-01-01

    Most real-world processes have nonlinear and complex dynamics. Conventional methods of constructing nonlinear models from first principles are time consuming and require a level of knowledge about the internal functioning of the system that is often not available. Consequently, in such

  6. Neural Computations in a Dynamical System with Multiple Time Scales

    Science.gov (United States)

    Mi, Yuanyuan; Lin, Xiaohan; Wu, Si

    2016-01-01

    Neural systems display rich short-term dynamics at various levels, e.g., spike-frequency adaptation (SFA) at the single-neuron level, and short-term facilitation (STF) and depression (STD) at the synapse level. These dynamical features typically cover a broad range of time scales and exhibit large diversity in different brain regions. It remains unclear what is the computational benefit for the brain to have such variability in short-term dynamics. In this study, we propose that the brain can exploit such dynamical features to implement multiple seemingly contradictory computations in a single neural circuit. To demonstrate this idea, we use continuous attractor neural network (CANN) as a working model and include STF, SFA and STD with increasing time constants in its dynamics. Three computational tasks are considered, which are persistent activity, adaptation, and anticipative tracking. These tasks require conflicting neural mechanisms, and hence cannot be implemented by a single dynamical feature or any combination with similar time constants. However, with properly coordinated STF, SFA and STD, we show that the network is able to implement the three computational tasks concurrently. We hope this study will shed light on the understanding of how the brain orchestrates its rich dynamics at various levels to realize diverse cognitive functions. PMID:27679569

  7. Evolutionary Computation and Its Applications in Neural and Fuzzy Systems

    Directory of Open Access Journals (Sweden)

    Biaobiao Zhang

    2011-01-01

    Full Text Available Neural networks and fuzzy systems are two soft-computing paradigms for system modelling. Adapting a neural or fuzzy system requires to solve two optimization problems: structural optimization and parametric optimization. Structural optimization is a discrete optimization problem which is very hard to solve using conventional optimization techniques. Parametric optimization can be solved using conventional optimization techniques, but the solution may be easily trapped at a bad local optimum. Evolutionary computation is a general-purpose stochastic global optimization approach under the universally accepted neo-Darwinian paradigm, which is a combination of the classical Darwinian evolutionary theory, the selectionism of Weismann, and the genetics of Mendel. Evolutionary algorithms are a major approach to adaptation and optimization. In this paper, we first introduce evolutionary algorithms with emphasis on genetic algorithms and evolutionary strategies. Other evolutionary algorithms such as genetic programming, evolutionary programming, particle swarm optimization, immune algorithm, and ant colony optimization are also described. Some topics pertaining to evolutionary algorithms are also discussed, and a comparison between evolutionary algorithms and simulated annealing is made. Finally, the application of EAs to the learning of neural networks as well as to the structural and parametric adaptations of fuzzy systems is also detailed.

  8. Neural Mechanisms and Information Processing in Recognition Systems

    Directory of Open Access Journals (Sweden)

    Mamiko Ozaki

    2014-10-01

    Full Text Available Nestmate recognition is a hallmark of social insects. It is based on the match/mismatch of an identity signal carried by members of the society with that of the perceiving individual. While the behavioral response, amicable or aggressive, is very clear, the neural systems underlying recognition are not fully understood. Here we contrast two alternative hypotheses for the neural mechanisms that are responsible for the perception and information processing in recognition. We focus on recognition via chemical signals, as the common modality in social insects. The first, classical, hypothesis states that upon perception of recognition cues by the sensory system the information is passed as is to the antennal lobes and to higher brain centers where the information is deciphered and compared to a neural template. Match or mismatch information is then transferred to some behavior-generating centers where the appropriate response is elicited. An alternative hypothesis, that of “pre-filter mechanism”, posits that the decision as to whether to pass on the information to the central nervous system takes place in the peripheral sensory system. We suggest that, through sensory adaptation, only alien signals are passed on to the brain, specifically to an “aggressive-behavior-switching center”, where the response is generated if the signal is above a certain threshold.

  9. Engineering neural systems for high-level problem solving.

    Science.gov (United States)

    Sylvester, Jared; Reggia, James

    2016-07-01

    There is a long-standing, sometimes contentious debate in AI concerning the relative merits of a symbolic, top-down approach vs. a neural, bottom-up approach to engineering intelligent machine behaviors. While neurocomputational methods excel at lower-level cognitive tasks (incremental learning for pattern classification, low-level sensorimotor control, fault tolerance and processing of noisy data, etc.), they are largely non-competitive with top-down symbolic methods for tasks involving high-level cognitive problem solving (goal-directed reasoning, metacognition, planning, etc.). Here we take a step towards addressing this limitation by developing a purely neural framework named galis. Our goal in this work is to integrate top-down (non-symbolic) control of a neural network system with more traditional bottom-up neural computations. galis is based on attractor networks that can be "programmed" with temporal sequences of hand-crafted instructions that control problem solving by gating the activity retention of, communication between, and learning done by other neural networks. We demonstrate the effectiveness of this approach by showing that it can be applied successfully to solve sequential card matching problems, using both human performance and a top-down symbolic algorithm as experimental controls. Solving this kind of problem makes use of top-down attention control and the binding together of visual features in ways that are easy for symbolic AI systems but not for neural networks to achieve. Our model can not only be instructed on how to solve card matching problems successfully, but its performance also qualitatively (and sometimes quantitatively) matches the performance of both human subjects that we had perform the same task and the top-down symbolic algorithm that we used as an experimental control. We conclude that the core principles underlying the galis framework provide a promising approach to engineering purely neurocomputational systems for problem

  10. Neural feedback linearization adaptive control for affine nonlinear systems based on neural network estimator

    Directory of Open Access Journals (Sweden)

    Bahita Mohamed

    2011-01-01

    Full Text Available In this work, we introduce an adaptive neural network controller for a class of nonlinear systems. The approach uses two Radial Basis Functions, RBF networks. The first RBF network is used to approximate the ideal control law which cannot be implemented since the dynamics of the system are unknown. The second RBF network is used for on-line estimating the control gain which is a nonlinear and unknown function of the states. The updating laws for the combined estimator and controller are derived through Lyapunov analysis. Asymptotic stability is established with the tracking errors converging to a neighborhood of the origin. Finally, the proposed method is applied to control and stabilize the inverted pendulum system.

  11. Epidemiologic study of neural tube defects in Los Angeles County. II. Etiologic factors in an area with low prevalence at birth

    Energy Technology Data Exchange (ETDEWEB)

    Sever, L.E.

    1982-01-01

    Epidemiologic characteristics of neural tube defect (NTD) births occurring in Los Angeles County, California, residents during the period 1966-1972 are presented. The prevalence at birth was 0.52/1000 births for anencephalus, 0.51/1000 for spina bifida, and 0.08/1000 for encephalocele, rates considered to be low for a predominantly white population. We hypothesized that environmental (nongenetic) factors are of less etiologic importance in a low-prevalence population than in areas or time periods with high prevalence. We tested that hypothesis by examining epidemiologic characteristics of NTDs in Los Angeles County and comparing them with high-prevalence populations. The data did not support a major etiologic role for environmental factors: (1) no significant differences between rates by month of birth or conception; (2) no significant association with maternal age or parity for anencephalus; for spina bifida a significant maternal age effect (P < 0.01) and for encephalocele a parity effect (P < 0.02); and (3) no significant relationship with father's occupational class for either anencephalus or encephalocele but a marginally significant (P < 0.05) inverse association for spina bifida when a statistic based on ordinal relationships was used. Findings supporting the importance of genetic factors in etiology included: (1) a high percentage of males; (2) a higher twin concordance rate than in high-prevalence populations; and (3) an anencephalus rate among blacks comparable with rates for blacks in other United States populations. Our findings in conjunction with those from other areas and times of low prevalence suggest environmental factors play a relatively insignificant role in the etiology of NTDs in such populations.

  12. Predictive and Neural Predictive Control of Uncertain Systems

    Science.gov (United States)

    Kelkar, Atul G.

    2000-01-01

    Accomplishments and future work are:(1) Stability analysis: the work completed includes characterization of stability of receding horizon-based MPC in the setting of LQ paradigm. The current work-in-progress includes analyzing local as well as global stability of the closed-loop system under various nonlinearities; for example, actuator nonlinearities; sensor nonlinearities, and other plant nonlinearities. Actuator nonlinearities include three major types of nonlineaxities: saturation, dead-zone, and (0, 00) sector. (2) Robustness analysis: It is shown that receding horizon parameters such as input and output horizon lengths have direct effect on the robustness of the system. (3) Code development: A matlab code has been developed which can simulate various MPC formulations. The current effort is to generalize the code to include ability to handle all plant types and all MPC types. (4) Improved predictor: It is shown that MPC design using better predictors that can minimize prediction errors. It is shown analytically and numerically that Smith predictor can provide closed-loop stability under GPC operation for plants with dead times where standard optimal predictor fails. (5) Neural network predictors: When neural network is used as predictor it can be shown that neural network predicts the plant output within some finite error bound under certain conditions. Our preliminary study shows that with proper choice of update laws and network architectures such bound can be obtained. However, much work needs to be done to obtain a similar result in general case.

  13. Intelligent systems II complete approximation by neural network operators

    CERN Document Server

    Anastassiou, George A

    2016-01-01

    This monograph is the continuation and completion of the monograph, “Intelligent Systems: Approximation by Artificial Neural Networks” written by the same author and published 2011 by Springer. The book you hold in hand presents the complete recent and original work of the author in approximation by neural networks. Chapters are written in a self-contained style and can be read independently. Advanced courses and seminars can be taught out of this brief book. All necessary background and motivations are given per chapter. A related list of references is given also per chapter. The book’s results are expected to find applications in many areas of applied mathematics, computer science and engineering. As such this monograph is suitable for researchers, graduate students, and seminars of the above subjects, also for all science and engineering libraries.  .

  14. A population-based study of birth defects in Malaysia.

    Science.gov (United States)

    Thong, M K; Ho, J J; Khatijah, N N

    2005-01-01

    Birth defects are one of the leading causes of paediatric disability and mortality in developed and developing countries. Data on birth defects from population-based studies originating from developing countries are lacking. One of the objectives of this study was to determine the epidemiology of major birth defects in births during the perinatal period in Kinta district, Perak, Malaysia over a 14-month period, using a population-based birth defect register. There were 253 babies with major birth defects in 17,720 births, giving an incidence of 14.3/1000 births, a birth prevalence of 1 in 70. There were 80 babies with multiple birth defects and 173 with isolated birth defects. The exact syndromic diagnosis of the babies with multiple birth defects could not be identified in 18 (22.5%) babies. The main organ systems involved in the isolated birth defects were cardiovascular (13.8%), cleft lip and palate (11.9%), clubfeet (9.1%), central nervous system (CNS) (including neural tube defects) (7.9%), musculoskeletal (5.5%) and gastrointestinal systems (4.7%), and hydrops fetalis (4.3%). The babies with major birth defects were associated with lower birth weights, premature deliveries, higher Caesarean section rates, prolonged hospitalization and increased specialist care. Among the cohort of babies with major birth defects, the mortality rate was 25.2% during the perinatal period. Mothers with affected babies were associated with advanced maternal age, birth defects themselves or their relatives but not in their other offspring, and significantly higher rates of previous abortions. The consanguinity rate of 2.4% was twice that of the control population. It is concluded that a birth defects register is needed to monitor these developments and future interventional trials are needed to reduce birth defects in Malaysia.

  15. Neural systems for choice and valuation with counterfactual learning signals.

    Science.gov (United States)

    Tobia, M J; Guo, R; Schwarze, U; Boehmer, W; Gläscher, J; Finckh, B; Marschner, A; Büchel, C; Obermayer, K; Sommer, T

    2014-04-01

    The purpose of this experiment was to test a computational model of reinforcement learning with and without fictive prediction error (FPE) signals to investigate how counterfactual consequences contribute to acquired representations of action-specific expected value, and to determine the functional neuroanatomy and neuromodulator systems that are involved. 80 male participants underwent dietary depletion of either tryptophan or tyrosine/phenylalanine to manipulate serotonin (5HT) and dopamine (DA), respectively. They completed 80 rounds (240 trials) of a strategic sequential investment task that required accepting interim losses in order to access a lucrative state and maximize long-term gains, while being scanned. We extended the standard Q-learning model by incorporating both counterfactual gains and losses into separate error signals. The FPE model explained the participants' data significantly better than a model that did not include counterfactual learning signals. Expected value from the FPE model was significantly correlated with BOLD signal change in the ventromedial prefrontal cortex (vmPFC) and posterior orbitofrontal cortex (OFC), whereas expected value from the standard model did not predict changes in neural activity. The depletion procedure revealed significantly different neural responses to expected value in the vmPFC, caudate, and dopaminergic midbrain in the vicinity of the substantia nigra (SN). Differences in neural activity were not evident in the standard Q-learning computational model. These findings demonstrate that FPE signals are an important component of valuation for decision making, and that the neural representation of expected value incorporates cortical and subcortical structures via interactions among serotonergic and dopaminergic modulator systems. Copyright © 2013 Elsevier Inc. All rights reserved.

  16. [A telemetery system for neural signal acquiring and processing].

    Science.gov (United States)

    Wang, Min; Song, Yongji; Suen, Jiantao; Zhao, Yiliang; Jia, Aibin; Zhu, Jianping

    2011-02-01

    Recording and extracting characteristic brain signals in freely moving animals is the basic and significant requirement in the study of brain-computer interface (BCI). To record animal's behaving and extract characteristic brain signals simultaneously could help understand the complex behavior of neural ensembles. Here, a system was established to record and analyse extracellular discharge in freely moving rats for the study of BCI. It comprised microelectrode and micro-driver assembly, analog front end (AFE), programmer system on chip (PSoC), wireless communication and the LabVIEW used as the platform for the graphic user interface.

  17. A simple mechanical system for studying adaptive oscillatory neural networks

    DEFF Research Database (Denmark)

    Jouffroy, Guillaume; Jouffroy, Jerome

    model, etc.) might be too complex to study. In this paper, we use a comparatively simple mechanical system, the nonholonomic vehicle referred to as the Roller-Racer, as a means towards testing different learning strategies for an Recurrent Neural Network-based (RNN) controller/guidance system. After...... a brief description of the Roller-Racer, we present as a preliminary study an RNN-based feed-forward controller whose parameters are obtained through the well-known teacher forcing learning algorithm, extended to learn signals with a continuous component....

  18. Radial basis function (RBF) neural network control for mechanical systems design, analysis and Matlab simulation

    CERN Document Server

    Liu, Jinkun

    2013-01-01

    Radial Basis Function (RBF) Neural Network Control for Mechanical Systems is motivated by the need for systematic design approaches to stable adaptive control system design using neural network approximation-based techniques. The main objectives of the book are to introduce the concrete design methods and MATLAB simulation of stable adaptive RBF neural control strategies. In this book, a broad range of implementable neural network control design methods for mechanical systems are presented, such as robot manipulators, inverted pendulums, single link flexible joint robots, motors, etc. Advanced neural network controller design methods and their stability analysis are explored. The book provides readers with the fundamentals of neural network control system design.   This book is intended for the researchers in the fields of neural adaptive control, mechanical systems, Matlab simulation, engineering design, robotics and automation. Jinkun Liu is a professor at Beijing University of Aeronautics and Astronauti...

  19. Neural Network Based Intrusion Detection System for Critical Infrastructures

    Energy Technology Data Exchange (ETDEWEB)

    Todd Vollmer; Ondrej Linda; Milos Manic

    2009-07-01

    Resiliency and security in control systems such as SCADA and Nuclear plant’s in today’s world of hackers and malware are a relevant concern. Computer systems used within critical infrastructures to control physical functions are not immune to the threat of cyber attacks and may be potentially vulnerable. Tailoring an intrusion detection system to the specifics of critical infrastructures can significantly improve the security of such systems. The IDS-NNM – Intrusion Detection System using Neural Network based Modeling, is presented in this paper. The main contributions of this work are: 1) the use and analyses of real network data (data recorded from an existing critical infrastructure); 2) the development of a specific window based feature extraction technique; 3) the construction of training dataset using randomly generated intrusion vectors; 4) the use of a combination of two neural network learning algorithms – the Error-Back Propagation and Levenberg-Marquardt, for normal behavior modeling. The presented algorithm was evaluated on previously unseen network data. The IDS-NNM algorithm proved to be capable of capturing all intrusion attempts presented in the network communication while not generating any false alerts.

  20. Neural Network Target Identification System for False Alarm Reduction

    Science.gov (United States)

    Ye, David; Edens, Weston; Lu, Thomas T.; Chao, Tien-Hsin

    2009-01-01

    A multi-stage automated target recognition (ATR) system has been designed to perform computer vision tasks with adequate proficiency in mimicking human vision. The system is able to detect, identify, and track targets of interest. Potential regions of interest (ROIs) are first identified by the detection stage using an Optimum Trade-off Maximum Average Correlation Height (OT-MACH) filter combined with a wavelet transform. False positives are then eliminated by the verification stage using feature extraction methods in conjunction with neural networks. Feature extraction transforms the ROIs using filtering and binning algorithms to create feature vectors. A feed forward back propagation neural network (NN) is then trained to classify each feature vector and remove false positives. This paper discusses the test of the system performance and parameter optimizations process which adapts the system to various targets and datasets. The test results show that the system was successful in substantially reducing the false positive rate when tested on a sonar image dataset.

  1. Building Equity in the Birth-to-3 System: Who Is in the Room?

    Science.gov (United States)

    Harris, Wendy; Horn, Benita Rodriguez; Tripp, Susan; Yasui, Barbara

    2016-01-01

    The Birth-to-Three Equity Initiative launched in April 2015 as a multiyear systems change process to strengthen equitable access, services, and child and family outcomes in the "early intervention" system in Martin Luther King County, Washington. This article describes the training of agency leaders and "equity facilitators"…

  2. System identification of an unmanned quadcopter system using MRAN neural

    Science.gov (United States)

    Pairan, M. F.; Shamsudin, S. S.

    2017-12-01

    This project presents the performance analysis of the radial basis function neural network (RBF) trained with Minimal Resource Allocating Network (MRAN) algorithm for real-time identification of quadcopter. MRAN’s performance is compared with the RBF with Constant Trace algorithm for 2500 input-output pair data sampling. MRAN utilizes adding and pruning hidden neuron strategy to obtain optimum RBF structure, increase prediction accuracy and reduce training time. The results indicate that MRAN algorithm produces fast training time and more accurate prediction compared with standard RBF. The model proposed in this paper is capable of identifying and modelling a nonlinear representation of the quadcopter flight dynamics.

  3. Neural system modeling and simulation using Hybrid Functional Petri Net.

    Science.gov (United States)

    Tang, Yin; Wang, Fei

    2012-02-01

    The Petri net formalism has been proved to be powerful in biological modeling. It not only boasts of a most intuitive graphical presentation but also combines the methods of classical systems biology with the discrete modeling technique. Hybrid Functional Petri Net (HFPN) was proposed specially for biological system modeling. An array of well-constructed biological models using HFPN yielded very interesting results. In this paper, we propose a method to represent neural system behavior, where biochemistry and electrical chemistry are both included using the Petri net formalism. We built a model for the adrenergic system using HFPN and employed quantitative analysis. Our simulation results match the biological data well, showing that the model is very effective. Predictions made on our model further manifest the modeling power of HFPN and improve the understanding of the adrenergic system. The file of our model and more results with their analysis are available in our supplementary material.

  4. Development of a web-based integrated birth defects surveillance system in New York State.

    Science.gov (United States)

    Wang, Ying; Tao, Zhen; Cross, Philip K; Le, Linh H; Steen, Patricia M; Babcock, Gwen D; Druschel, Charlotte M; Hwang, Syni-An

    2008-01-01

    Over the past decade, the Internet has become a powerful and effective tool for public health surveillance. The objectives of this project were to develop secure Web-based applications for Birth Defects Surveillance and to integrate them into routine surveillance activities of the New York State (NYS) Congenital Malformations Registry (CMR). The Web-based applications were developed on infrastructure of New York State Health Provider Network using JAVA programming language. In addition, SAS/IntrNet software (SAS Institute Inc, Cary, NC) was also used to leverage the data analysis and processing capabilities of SAS for generating real-time reports and performing statistical and spatial analyses. Congenital Malformations Registry staff have developed and implemented a Web-based integrated birth defect surveillance system, which enables staff to routinely perform surveillance activities including monitoring the quality, timeliness, and completeness of case reporting by hospitals; matching the CMR cases to the vital records; conducting trends analysis on birth defect prevalence and mortality with data query and visualization capabilities; and performing temporal and spatial analysis. The CMR's Web-based integrated birth defects surveillance system empowers authorized users to perform routine surveillance activities using only a PC and a Web browser. This system will help NYS public health professionals and epidemiologists perform trend analyses and identify possible clusters of birth defects in space and time that may be related to environmental toxins.

  5. Intelligent reservoir operation system based on evolving artificial neural networks

    Science.gov (United States)

    Chaves, Paulo; Chang, Fi-John

    2008-06-01

    We propose a novel intelligent reservoir operation system based on an evolving artificial neural network (ANN). Evolving means the parameters of the ANN model are identified by the GA evolutionary optimization technique. Accordingly, the ANN model should represent the operational strategies of reservoir operation. The main advantages of the Evolving ANN Intelligent System (ENNIS) are as follows: (i) only a small number of parameters to be optimized even for long optimization horizons, (ii) easy to handle multiple decision variables, and (iii) the straightforward combination of the operation model with other prediction models. The developed intelligent system was applied to the operation of the Shihmen Reservoir in North Taiwan, to investigate its applicability and practicability. The proposed method is first built to a simple formulation for the operation of the Shihmen Reservoir, with single objective and single decision. Its results were compared to those obtained by dynamic programming. The constructed network proved to be a good operational strategy. The method was then built and applied to the reservoir with multiple (five) decision variables. The results demonstrated that the developed evolving neural networks improved the operation performance of the reservoir when compared to its current operational strategy. The system was capable of successfully simultaneously handling various decision variables and provided reasonable and suitable decisions.

  6. The ctenophore genome and the evolutionary origins of neural systems.

    Science.gov (United States)

    Moroz, Leonid L; Kocot, Kevin M; Citarella, Mathew R; Dosung, Sohn; Norekian, Tigran P; Povolotskaya, Inna S; Grigorenko, Anastasia P; Dailey, Christopher; Berezikov, Eugene; Buckley, Katherine M; Ptitsyn, Andrey; Reshetov, Denis; Mukherjee, Krishanu; Moroz, Tatiana P; Bobkova, Yelena; Yu, Fahong; Kapitonov, Vladimir V; Jurka, Jerzy; Bobkov, Yuri V; Swore, Joshua J; Girardo, David O; Fodor, Alexander; Gusev, Fedor; Sanford, Rachel; Bruders, Rebecca; Kittler, Ellen; Mills, Claudia E; Rast, Jonathan P; Derelle, Romain; Solovyev, Victor V; Kondrashov, Fyodor A; Swalla, Billie J; Sweedler, Jonathan V; Rogaev, Evgeny I; Halanych, Kenneth M; Kohn, Andrea B

    2014-06-05

    The origins of neural systems remain unresolved. In contrast to other basal metazoans, ctenophores (comb jellies) have both complex nervous and mesoderm-derived muscular systems. These holoplanktonic predators also have sophisticated ciliated locomotion, behaviour and distinct development. Here we present the draft genome of Pleurobrachia bachei, Pacific sea gooseberry, together with ten other ctenophore transcriptomes, and show that they are remarkably distinct from other animal genomes in their content of neurogenic, immune and developmental genes. Our integrative analyses place Ctenophora as the earliest lineage within Metazoa. This hypothesis is supported by comparative analysis of multiple gene families, including the apparent absence of HOX genes, canonical microRNA machinery, and reduced immune complement in ctenophores. Although two distinct nervous systems are well recognized in ctenophores, many bilaterian neuron-specific genes and genes of 'classical' neurotransmitter pathways either are absent or, if present, are not expressed in neurons. Our metabolomic and physiological data are consistent with the hypothesis that ctenophore neural systems, and possibly muscle specification, evolved independently from those in other animals.

  7. Fault Tolerant Neural Network for ECG Signal Classification Systems

    Directory of Open Access Journals (Sweden)

    MERAH, M.

    2011-08-01

    Full Text Available The aim of this paper is to apply a new robust hardware Artificial Neural Network (ANN for ECG classification systems. This ANN includes a penalization criterion which makes the performances in terms of robustness. Specifically, in this method, the ANN weights are normalized using the auto-prune method. Simulations performed on the MIT ? BIH ECG signals, have shown that significant robustness improvements are obtained regarding potential hardware artificial neuron failures. Moreover, we show that the proposed design achieves better generalization performances, compared to the standard back-propagation algorithm.

  8. Separate neural systems value immediate and delayed monetary rewards.

    Science.gov (United States)

    McClure, Samuel M; Laibson, David I; Loewenstein, George; Cohen, Jonathan D

    2004-10-15

    When humans are offered the choice between rewards available at different points in time, the relative values of the options are discounted according to their expected delays until delivery. Using functional magnetic resonance imaging, we examined the neural correlates of time discounting while subjects made a series of choices between monetary reward options that varied by delay to delivery. We demonstrate that two separate systems are involved in such decisions. Parts of the limbic system associated with the midbrain dopamine system, including paralimbic cortex, are preferentially activated by decisions involving immediately available rewards. In contrast, regions of the lateral prefrontal cortex and posterior parietal cortex are engaged uniformly by intertemporal choices irrespective of delay. Furthermore, the relative engagement of the two systems is directly associated with subjects' choices, with greater relative fronto-parietal activity when subjects choose longer term options.

  9. Examination of neural systems sub-serving facebook "addiction".

    Science.gov (United States)

    Turel, Ofir; He, Qinghua; Xue, Gui; Xiao, Lin; Bechara, Antoine

    2014-12-01

    Because addictive behaviors typically result from violated homeostasis of the impulsive (amygdala-striatal) and inhibitory (prefrontal cortex) brain systems, this study examined whether these systems sub-serve a specific case of technology-related addiction, namely Facebook "addiction." Using a go/no-go paradigm in functional MRI settings, the study examined how these brain systems in 20 Facebook users (M age = 20.3 yr., SD = 1.3, range = 18-23) who completed a Facebook addiction questionnaire, responded to Facebook and less potent (traffic sign) stimuli. The findings indicated that at least at the examined levels of addiction-like symptoms, technology-related "addictions" share some neural features with substance and gambling addictions, but more importantly they also differ from such addictions in their brain etiology and possibly pathogenesis, as related to abnormal functioning of the inhibitory-control brain system.

  10. Neural systems supporting and affecting economically relevant behavior

    Directory of Open Access Journals (Sweden)

    Braeutigam S

    2012-05-01

    Full Text Available Sven BraeutigamOxford Centre for Human Brain Activity, University of Oxford, Oxford, United KingdomAbstract: For about a hundred years, theorists and traders alike have tried to unravel and understand the mechanisms and hidden rules underlying and perhaps determining economically relevant behavior. This review focuses on recent developments in neuroeconomics, where the emphasis is placed on two directions of research: first, research exploiting common experiences of urban inhabitants in industrialized societies to provide experimental paradigms with a broader real-life content; second, research based on behavioral genetics, which provides an additional dimension for experimental control and manipulation. In addition, possible limitations of state-of-the-art neuroeconomics research are addressed. It is argued that observations of neuronal systems involved in economic behavior converge to some extent across the technologies and paradigms used. Conceptually, the data available as of today raise the possibility that neuroeconomic research might provide evidence at the neuronal level for the existence of multiple systems of thought and for the importance of conflict. Methodologically, Bayesian approaches in particular may play an important role in identifying mechanisms and establishing causality between patterns of neural activity and economic behavior.Keywords: neuroeconomics, behavioral genetics, decision-making, consumer behavior, neural system

  11. Using pulse width modulation for wireless transmission of neural signals in multichannel neural recording systems.

    Science.gov (United States)

    Yin, Ming; Ghovanloo, Maysam

    2009-08-01

    We have used a well-known technique in wireless communication, pulse width modulation (PWM) of time division multiplexed (TDM) signals, within the architecture of a novel wireless integrated neural recording (WINeR) system. We have evaluated the performance of the PWM-based architecture and indicated its accuracy and potential sources of error through detailed theoretical analysis, simulations, and measurements on a setup consisting of a 15-channel WINeR prototype as the transmitter and two types of receivers; an Agilent 89600 vector signal analyzer and a custom wideband receiver, with 36 and 75 MHz of maximum bandwidth, respectively. Furthermore, we present simulation results from a realistic MATLAB-Simulink model of the entire WINeR system to observe the system behavior in response to changes in various parameters. We have concluded that the 15-ch WINeR prototype, which is fabricated in a 0.5- mum standard CMOS process and consumes 4.5 mW from +/-1.5 V supplies, can acquire and wirelessly transmit up to 320 k-samples/s to a 75-MHz receiver with 8.4 bits of resolution, which is equivalent to a wireless data rate of approximately 2.56 Mb/s.

  12. A Chinese Named Entity Recognition System with Neural Networks

    Directory of Open Access Journals (Sweden)

    Yi Hui-Kang

    2017-01-01

    Full Text Available Named entity recognition (NER is a typical sequential labeling problem that plays an important role in natural language processing (NLP systems. In this paper, we discussed the details of applying a comprehensive model aggregating neural networks and conditional random field (CRF on Chinese NER tasks, and how to discovery character level features when implement a NER system in word level. We compared the difference between Chinese and English when modeling the character embeddings. We developed a NER system based on our analysis, it works well on the ACE 2004 and SIGHAN bakeoff 2006 MSRA dataset, and doesn’t rely on any gazetteers or handcraft features. We obtained F1 score of 82.3% on MSRA 2006.

  13. Assessing the prevalence of spina bifida and encephalocele in a Kenyan hospital from 2005–2010: implications for a neural tube defects surveillance system

    Science.gov (United States)

    Githuku, Jane N; Azofeifa, Alejandro; Valencia, Diana; Ao, Trong; Hamner, Heather; Amwayi, Samuel; Gura, Zeinab; Omolo, Jared; Albright, Leland; Guo, Jing; Arvelo, Wences

    2014-01-01

    Introduction Neural tube defects such as anencephaly, spina bifida, and encephalocele are congenital anomalies of the central nervous system. Data on the prevalence of neural tube defects in Kenya are limited. This study characterizes and estimates the prevalence of spina bifida and encephalocele reported in a referral hospital in Kenya from 2005-2010. Methods Cases were defined as a diagnosis of spina bifida or encephalocele. Prevalence was calculated as the number of cases by year and province of residence divided by the total number of live-births per province. Results From a total of 6,041 surgical records; 1,184 (93%) had reported diagnosis of spina bifida and 88 (7%) of encephalocele. Estimated prevalence of spina bifida and encephalocele from 2005-2010 was 3.3 [95% Confidence Interval (CI): 3.1-3.5] cases per 10,000 live-births. The highest prevalence of cases were reported in 2007 with 4.4 (95% CI: 3.9-5.0) cases per 10,000 live-births. Rift Valley province had the highest prevalence of spina bifida and encephalocele at 6.9 (95% CI: 6.3-7.5) cases per 10,000 live-births from 2005-2010. Conclusion Prevalence of spina bifida and encephalocele is likely underestimated, as only patients seeking care at the hospital were included. Variations in regional prevalence could be due to referral patterns and healthcare access. Implementation of a neural tube defects surveillance system would provide a more thorough assessment of the burden of neural tube defects in Kenya. PMID:26113894

  14. Assessing the prevalence of spina bifida and encephalocele in a Kenyan hospital from 2005-2010: implications for a neural tube defects surveillance system.

    Science.gov (United States)

    Githuku, Jane N; Azofeifa, Alejandro; Valencia, Diana; Ao, Trong; Hamner, Heather; Amwayi, Samuel; Gura, Zeinab; Omolo, Jared; Albright, Leland; Guo, Jing; Arvelo, Wences

    2014-01-01

    Neural tube defects such as anencephaly, spina bifida, and encephalocele are congenital anomalies of the central nervous system. Data on the prevalence of neural tube defects in Kenya are limited. This study characterizes and estimates the prevalence of spina bifida and encephalocele reported in a referral hospital in Kenya from 2005-2010. Cases were defined as a diagnosis of spina bifida or encephalocele. Prevalence was calculated as the number of cases by year and province of residence divided by the total number of live-births per province. From a total of 6,041 surgical records; 1,184 (93%) had reported diagnosis of spina bifida and 88 (7%) of encephalocele. Estimated prevalence of spina bifida and encephalocele from 2005-2010 was 3.3 [95% Confidence Interval (CI): 3.1-3.5] cases per 10,000 live-births. The highest prevalence of cases were reported in 2007 with 4.4 (95% CI: 3.9-5.0) cases per 10,000 live-births. Rift Valley province had the highest prevalence of spina bifida and encephalocele at 6.9 (95% CI: 6.3-7.5) cases per 10,000 live-births from 2005-2010. Prevalence of spina bifida and encephalocele is likely underestimated, as only patients seeking care at the hospital were included. Variations in regional prevalence could be due to referral patterns and healthcare access. Implementation of a neural tube defects surveillance system would provide a more thorough assessment of the burden of neural tube defects in Kenya.

  15. Quasi-Birth-Death Processes, Tree-Like QBDs, Probabilistic 1-Counter Automata, and Pushdown Systems

    NARCIS (Netherlands)

    K. Etessami (Kousha); D.K. Wojtczak (Dominik); M. Yannakakis

    2010-01-01

    textabstractWe begin by observing that (discrete-time) Quasi-Birth-Death Processes (QBDs) are equivalent, in a precise sense, to probabilistic 1-Counter Automata (p1CAs), and both Tree-Like QBDs (TL-QBDs) and Tree-Structured QBDs (TS-QBDs) are equivalent to both probabilistic Pushdown Systems

  16. Nutrition: Intervention Guidance for Service Providers and Families. Connecticut Birth to Three System, Service Guideline 6.

    Science.gov (United States)

    Connecticut Birth to Three System, Hartford.

    This guide was developed to assist families and service providers in Connecticut with nutrition services for infants and toddlers with disabilities. Individual sections provide information about the following topics: laws and regulations related to nutrition services; eligibility for the Connecticut Birth to Three System and nutrition; nutrition…

  17. Evolution of the Galaxy and the Birth of the Solar System: The Short ...

    Indian Academy of Sciences (India)

    An attempt is made, probably for the first time, to understand the origin of the solar system in context with the evolution of the galaxy as a natural consequence of the birth of several generations of stellar clusters. The galaxy is numerically simulated to deduce the inventories of the short-lived nuclides, 26Al, 36Cl, 41Ca, 53Mn ...

  18. Neural Network Enhanced Structure Determination of Osteoporosis, Immune System, and Radiation Repair Proteins Project

    Data.gov (United States)

    National Aeronautics and Space Administration — The proposed innovation will utilize self learning neural network technology to determine the structure of osteoporosis, immune system disease, and excess radiation...

  19. Hybrid fault diagnosis of nonlinear systems using neural parameter estimators.

    Science.gov (United States)

    Sobhani-Tehrani, E; Talebi, H A; Khorasani, K

    2014-02-01

    This paper presents a novel integrated hybrid approach for fault diagnosis (FD) of nonlinear systems taking advantage of both the system's mathematical model and the adaptive nonlinear approximation capability of computational intelligence techniques. Unlike most FD techniques, the proposed solution simultaneously accomplishes fault detection, isolation, and identification (FDII) within a unified diagnostic module. At the core of this solution is a bank of adaptive neural parameter estimators (NPEs) associated with a set of single-parameter fault models. The NPEs continuously estimate unknown fault parameters (FPs) that are indicators of faults in the system. Two NPE structures, series-parallel and parallel, are developed with their exclusive set of desirable attributes. The parallel scheme is extremely robust to measurement noise and possesses a simpler, yet more solid, fault isolation logic. In contrast, the series-parallel scheme displays short FD delays and is robust to closed-loop system transients due to changes in control commands. Finally, a fault tolerant observer (FTO) is designed to extend the capability of the two NPEs that originally assumes full state measurements for systems that have only partial state measurements. The proposed FTO is a neural state estimator that can estimate unmeasured states even in the presence of faults. The estimated and the measured states then comprise the inputs to the two proposed FDII schemes. Simulation results for FDII of reaction wheels of a three-axis stabilized satellite in the presence of disturbances and noise demonstrate the effectiveness of the proposed FDII solutions under partial state measurements. Copyright © 2013 Elsevier Ltd. All rights reserved.

  20. Low-cost wireless neural recording system and software.

    Science.gov (United States)

    Gregory, Jeffrey A; Borna, Amir; Roy, Sabyasachi; Wang, Xiaoqin; Lewandowski, Brian; Schmidt, Marc; Najafi, Khalil

    2009-01-01

    We describe a flexible wireless neural recording system, which is comprised of a 15-channel analog FM transmitter, digital receiver and custom user interface software for data acquisition. The analog front-end is constructed from commercial off the shelf (COTS) components and weighs 6.3g (including batteries) and is capable of transmitting over 24 hours up to a range over 3m with a 25microV(rms) in-vivo noise floor. The Software Defined Radio (SDR) and the acquisition software provide a data acquisition platform with real time data display and can be customized based on the specifications of various experiments. The described system was characterized with in-vitro and in-vivo experiments and the results are presented.

  1. Stability Analysis of Neural Networks-Based System Identification

    Directory of Open Access Journals (Sweden)

    Talel Korkobi

    2008-01-01

    Full Text Available This paper treats some problems related to nonlinear systems identification. A stability analysis neural network model for identifying nonlinear dynamic systems is presented. A constrained adaptive stable backpropagation updating law is presented and used in the proposed identification approach. The proposed backpropagation training algorithm is modified to obtain an adaptive learning rate guarantying convergence stability. The proposed learning rule is the backpropagation algorithm under the condition that the learning rate belongs to a specified range defining the stability domain. Satisfying such condition, unstable phenomena during the learning process are avoided. A Lyapunov analysis leads to the computation of the expression of a convenient adaptive learning rate verifying the convergence stability criteria. Finally, the elaborated training algorithm is applied in several simulations. The results confirm the effectiveness of the CSBP algorithm.

  2. A direct-to-drive neural data acquisition system

    Directory of Open Access Journals (Sweden)

    Justin P Kinney

    2015-09-01

    Full Text Available Driven by the increasing channel count of neural probes, there is much effort being directed to creating increasingly scalable electrophysiology data acquisition systems. However, all such systems still rely on personal computers for data storage, and thus are limited by the bandwidth and cost of the computers, especially as the scale of recording increases. Here we present a novel architecture in which a digital processor receives data from an analog-to-digital converter, and writes that data directly to hard drives, without the need for a personal computer to serve as an intermediary in the data acquisition process. This minimalist architecture may support exceptionally high data throughput, without incurring costs to support unnecessary hardware and overhead associated with personal computers, thus facilitating scaling of electrophysiological recording in the future.

  3. Analyses of antigen dependency networks unveil immune system reorganization between birth and adulthood

    Science.gov (United States)

    Madi, Asaf; Kenett, Dror Y.; Bransburg-Zabary, Sharron; Merbl, Yifat; Quintana, Francisco J.; Boccaletti, Stefano; Tauber, Alfred I.; Cohen, Irun R.; Ben-Jacob, Eshel

    2011-03-01

    Much effort has been devoted to assess the importance of nodes in complex biological networks (such as gene transcriptional regulatory networks, protein interaction networks, and neural networks). Examples of commonly used measures of node importance include node degree, node centrality, and node vulnerability score (the effect of the node deletion on the network efficiency). Here, we present a new approach to compute and investigate the mutual dependencies between network nodes from the matrices of node-node correlations. To this end, we first define the dependency of node i on node j (or the influence of node j on node i), D(i, j) as the average over all nodes k of the difference between the i - k correlation and the partial correlations between these nodes with respect to node j. Note that the dependencies, D(i, j) define a directed weighted matrix, since, in general, D(i, j) differs from D( j, i). For this reason, many of the commonly used measures of node importance, such as node centrality, cannot be used. Hence, to assess the node importance of the dependency networks, we define the system level influence (SLI) of antigen j, SLI( j) as the sum of the influence of j on all other antigens i. Next, we define the system level influence or the influence score of antigen j, SLI( j) as the sum of D(i, j) over all nodes i. We introduce the new approach and demonstrate that it can unveil important biological information in the context of the immune system. More specifically, we investigated antigen dependency networks computed from antigen microarray data of autoantibody reactivity of IgM and IgG isotypes present in the sera of ten mothers and their newborns. We found that the analysis was able to unveil that there is only a subset of antigens that have high influence scores (SLI) common both to the mothers and newborns. Networks comparison in terms of modularity (using the Newman's algorithm) and of topology (measured by the divergence rate) revealed that, at birth

  4. Fuzzy stochastic neural network model for structural system identification

    Science.gov (United States)

    Jiang, Xiaomo; Mahadevan, Sankaran; Yuan, Yong

    2017-01-01

    This paper presents a dynamic fuzzy stochastic neural network model for nonparametric system identification using ambient vibration data. The model is developed to handle two types of imprecision in the sensed data: fuzzy information and measurement uncertainties. The dimension of the input vector is determined by using the false nearest neighbor approach. A Bayesian information criterion is applied to obtain the optimum number of stochastic neurons in the model. A fuzzy C-means clustering algorithm is employed as a data mining tool to divide the sensed data into clusters with common features. The fuzzy stochastic model is created by combining the fuzzy clusters of input vectors with the radial basis activation functions in the stochastic neural network. A natural gradient method is developed based on the Kullback-Leibler distance criterion for quick convergence of the model training. The model is validated using a power density pseudospectrum approach and a Bayesian hypothesis testing-based metric. The proposed methodology is investigated with numerically simulated data from a Markov Chain model and a two-story planar frame, and experimentally sensed data from ambient vibration data of a benchmark structure.

  5. Maternal systemic or cord blood inflammation is associated with birth anthropometry in a Tanzanian prospective cohort.

    Science.gov (United States)

    Wilkinson, A L; Pedersen, S H; Urassa, M; Michael, D; Andreasen, A; Todd, J; Kinung'hi, S M; Changalucha, J; McDermid, J M

    2017-01-01

    HIV infection is associated with chronic systemic inflammation, with or without antiretroviral therapy. Consequences for foetal growth are not understood, particularly in settings where multiple maternal infections and malnutrition are common. The study was designed to examine maternal systemic circulating and umbilical cord blood cytokine concentrations in relation to birth anthropometry in a Tanzanian prospective cohort. A 9-plex panel of maternal plasma cytokines in HIV-positive (n = 44) and HIV-negative (n = 70) mothers and the same cytokines in umbilical cord blood collected at delivery was assayed. Linear regression modelled associations between maternal or cord blood cytokines and birth anthropometry. Health indicators (haemoglobin, mid-upper-arm circumference, body mass index) in HIV-positive mothers without considerable immunosuppression did not differ from HIV-negative women. Despite this, HIV-exposed infants had lower birthweight and length. Subgroup analyses indicated that HIV management using HAART was associated with lower plasma TNF-α, as were longer durations of any antiretroviral therapy (≥2 months). Greater maternal plasma TNF-α was associated with earlier delivery (-1.7 weeks, P = 0.039) and lower birthweights (-287 g; P = 0.020), while greater umbilical cord TNF-α (-1.43 cm; P = 0.036) and IL-12p70 (-2.4 cm; P = 0.008) were associated with shorter birth length. Birthweight was inversely associated with cord IL-12p70 (-723 g; P = 0.001) and IFN-γ (-482 g, P = 0.007). Maternal cytokines during pregnancy did not correlate with umbilical cord cytokines at delivery. Systemic inflammation identified in maternal plasma or umbilical cord blood was associated with poorer birth anthropometrics in HIV-exposed and HIV-unexposed infants. Controlling maternal and/or foetal systemic inflammation may improve birth anthropometry. © 2016 The Authors. Tropical Medicine & International Health Published by John Wiley & Sons Ltd.

  6. Multiagent Intrusion Detection Based on Neural Network Detectors and Artificial Immune System

    OpenAIRE

    Vaitsekhovich, L.; Golovko, V; Rubanau, V.

    2009-01-01

    In this article the artificial immune system and neural network techniques for intrusion detection have been addressed. The AIS allows detecting unknown samples of computer attacks. The integration of AIS and neural networks as detectors permits to increase performance of the system security. The detector structure is based on the integration of the different neural networks namely RNN and MLP. The KDD-99 dataset was used for experiments performing. The experimental results show that such int...

  7. Identification of the non-linear systems using internal recurrent neural networks

    Directory of Open Access Journals (Sweden)

    Bogdan CODRES

    2006-12-01

    Full Text Available In the past years utilization of neural networks took a distinct ampleness because of the following properties: distributed representation of information, capacity of generalization in case of uncontained situation in training data set, tolerance to noise, resistance to partial destruction, parallel processing. Another major advantage of neural networks is that they allow us to obtain the model of the investigated system, systems that is not necessarily to be linear. In fact, the true value of neural networks is seen in the case of identification and control of nonlinear systems. In this paper there are presented some identification techniques using neural networks.

  8. BOOK REVIEW: Theory of Neural Information Processing Systems

    Science.gov (United States)

    Galla, Tobias

    2006-04-01

    It is difficult not to be amazed by the ability of the human brain to process, to structure and to memorize information. Even by the toughest standards the behaviour of this network of about 1011 neurons qualifies as complex, and both the scientific community and the public take great interest in the growing field of neuroscience. The scientific endeavour to learn more about the function of the brain as an information processing system is here a truly interdisciplinary one, with important contributions from biology, computer science, physics, engineering and mathematics as the authors quite rightly point out in the introduction of their book. The role of the theoretical disciplines here is to provide mathematical models of information processing systems and the tools to study them. These models and tools are at the centre of the material covered in the book by Coolen, Kühn and Sollich. The book is divided into five parts, providing basic introductory material on neural network models as well as the details of advanced techniques to study them. A mathematical appendix complements the main text. The range of topics is extremely broad, still the presentation is concise and the book well arranged. To stress the breadth of the book let me just mention a few keywords here: the material ranges from the basics of perceptrons and recurrent network architectures to more advanced aspects such as Bayesian learning and support vector machines; Shannon's theory of information and the definition of entropy are discussed, and a chapter on Amari's information geometry is not missing either. Finally the statistical mechanics chapters cover Gardner theory and the replica analysis of the Hopfield model, not without being preceded by a brief introduction of the basic concepts of equilibrium statistical physics. The book also contains a part on effective theories of the macroscopic dynamics of neural networks. Many dynamical aspects of neural networks are usually hard to find in the

  9. Neural mechanism of facilitation system during physical fatigue.

    Directory of Open Access Journals (Sweden)

    Masaaki Tanaka

    Full Text Available An enhanced facilitation system caused by motivational input plays an important role in supporting performance during physical fatigue. We tried to clarify the neural mechanisms of the facilitation system during physical fatigue using magnetoencephalography (MEG and a classical conditioning technique. Twelve right-handed volunteers participated in this study. Participants underwent MEG recording during the imagery of maximum grips of the right hand guided by metronome sounds for 10 min. Thereafter, fatigue-inducing maximum handgrip trials were performed for 10 min; the metronome sounds were started 5 min after the beginning of the handgrip trials. The metronome sounds were used as conditioned stimuli and maximum handgrip trials as unconditioned stimuli. The next day, they were randomly assigned to two groups in a single-blinded, two-crossover fashion to undergo two types of MEG recordings, that is, for the control and motivation sessions, during the imagery of maximum grips of the right hand guided by metronome sounds for 10 min. The alpha-band event-related desynchronizations (ERDs of the motivation session relative to the control session within the time windows of 500 to 700 and 800 to 900 ms after the onset of handgrip cue sounds were identified in the sensorimotor areas. In addition, the alpha-band ERD within the time window of 400 to 500 ms was identified in the right dorsolateral prefrontal cortex (Brodmann's area 46. The ERD level in the right dorsolateral prefrontal cortex was positively associated with that in the sensorimotor areas within the time window of 500 to 700 ms. These results suggest that the right dorsolateral prefrontal cortex is involved in the neural substrates of the facilitation system and activates the sensorimotor areas during physical fatigue.

  10. Optimal Workflow Scheduling in Critical Infrastructure Systems with Neural Networks

    Directory of Open Access Journals (Sweden)

    S. Vukmirović

    2012-04-01

    Full Text Available Critical infrastructure systems (CISs, such as power grids, transportation systems, communication networks and water systems are the backbone of a country’s national security and industrial prosperity. These CISs execute large numbers of workflows with very high resource requirements that can span through different systems and last for a long time. The proper functioning and synchronization of these workflows is essential since humanity’s well-being is connected to it. Because of this, the challenge of ensuring availability and reliability of these services in the face of a broad range of operating conditions is very complicated. This paper proposes an architecture which dynamically executes a scheduling algorithm using feedback about the current status of CIS nodes. Different artificial neural networks (ANNs were created in order to solve the scheduling problem. Their performances were compared and as the main result of this paper, an optimal ANN architecture for workflow scheduling in CISs is proposed. A case study is shown for a meter data management system with measurements from a power distribution management system in Serbia. Performance tests show that significant improvement of the overall execution time can be achieved by ANNs.

  11. Stochastic Neural Field Theory and the System-Size Expansion

    KAUST Repository

    Bressloff, Paul C.

    2010-01-01

    We analyze a master equation formulation of stochastic neurodynamics for a network of synaptically coupled homogeneous neuronal populations each consisting of N identical neurons. The state of the network is specified by the fraction of active or spiking neurons in each population, and transition rates are chosen so that in the thermodynamic or deterministic limit (N → ∞) we recover standard activity-based or voltage-based rate models. We derive the lowest order corrections to these rate equations for large but finite N using two different approximation schemes, one based on the Van Kampen system-size expansion and the other based on path integral methods. Both methods yield the same series expansion of the moment equations, which at O(1/N) can be truncated to form a closed system of equations for the first-and second-order moments. Taking a continuum limit of the moment equations while keeping the system size N fixed generates a system of integrodifferential equations for the mean and covariance of the corresponding stochastic neural field model. We also show how the path integral approach can be used to study large deviation or rare event statistics underlying escape from the basin of attraction of a stable fixed point of the mean-field dynamics; such an analysis is not possible using the system-size expansion since the latter cannot accurately determine exponentially small transitions. © by SIAM.

  12. Neural systems language: a formal modeling language for the systematic description, unambiguous communication, and automated digital curation of neural connectivity.

    Science.gov (United States)

    Brown, Ramsay A; Swanson, Larry W

    2013-09-01

    Systematic description and the unambiguous communication of findings and models remain among the unresolved fundamental challenges in systems neuroscience. No common descriptive frameworks exist to describe systematically the connective architecture of the nervous system, even at the grossest level of observation. Furthermore, the accelerating volume of novel data generated on neural connectivity outpaces the rate at which this data is curated into neuroinformatics databases to synthesize digitally systems-level insights from disjointed reports and observations. To help address these challenges, we propose the Neural Systems Language (NSyL). NSyL is a modeling language to be used by investigators to encode and communicate systematically reports of neural connectivity from neuroanatomy and brain imaging. NSyL engenders systematic description and communication of connectivity irrespective of the animal taxon described, experimental or observational technique implemented, or nomenclature referenced. As a language, NSyL is internally consistent, concise, and comprehensible to both humans and computers. NSyL is a promising development for systematizing the representation of neural architecture, effectively managing the increasing volume of data on neural connectivity and streamlining systems neuroscience research. Here we present similar precedent systems, how NSyL extends existing frameworks, and the reasoning behind NSyL's development. We explore NSyL's potential for balancing robustness and consistency in representation by encoding previously reported assertions of connectivity from the literature as examples. Finally, we propose and discuss the implications of a framework for how NSyL will be digitally implemented in the future to streamline curation of experimental results and bridge the gaps among anatomists, imagers, and neuroinformatics databases. Copyright © 2013 Wiley Periodicals, Inc.

  13. A neural network architecture for implementation of expert systems for real time monitoring

    Science.gov (United States)

    Ramamoorthy, P. A.

    1991-01-01

    Since neural networks have the advantages of massive parallelism and simple architecture, they are good tools for implementing real time expert systems. In a rule based expert system, the antecedents of rules are in the conjunctive or disjunctive form. We constructed a multilayer feedforward type network in which neurons represent AND or OR operations of rules. Further, we developed a translator which can automatically map a given rule base into the network. Also, we proposed a new and powerful yet flexible architecture that combines the advantages of both fuzzy expert systems and neural networks. This architecture uses the fuzzy logic concepts to separate input data domains into several smaller and overlapped regions. Rule-based expert systems for time critical applications using neural networks, the automated implementation of rule-based expert systems with neural nets, and fuzzy expert systems vs. neural nets are covered.

  14. Platforms for artificial neural networks : neurosimulators and performance prediction of MIMD-parallel systems

    NARCIS (Netherlands)

    Vuurpijl, L.G.

    1998-01-01

    In this thesis, two platforms for simulating artificial neural networks are discussed: MIMD-parallel processor systems as an execution platform and neurosimulators as a research and development platform. Because of the parallelism encountered in neural networks, distributed processor systems seem to

  15. Analysis of the developing neural system using an in vitro model by Raman spectroscopy.

    Science.gov (United States)

    Hashimoto, Kosuke; Kudoh, Suguru N; Sato, Hidetoshi

    2015-04-07

    We developed an in vitro model of early neural cell development. The maturation of a normal neural cell was studied in vitro using Raman spectroscopy for 120 days. The Raman spectra datasets were analyzed by principal component analysis (PCA) to investigate the relationship between maturation stages and molecular composition changes in neural cells. According to the PCA, the Raman spectra datasets can be classified into four larger groups. Previous electrophysiological studies have suggested that a normal neural cell goes through three maturation states. The groups we observed by Raman analysis showed good agreement with the electrophysiological results, except with the addition of a fourth state. The results demonstrated that Raman analysis was powerful to investigate the daily changes in molecular composition of the growing neural cell. This in vitro model system may be useful for future studies of the effects of endocrine disrupters in the developing early neural system.

  16. NNETS - NEURAL NETWORK ENVIRONMENT ON A TRANSPUTER SYSTEM

    Science.gov (United States)

    Villarreal, J.

    1994-01-01

    The primary purpose of NNETS (Neural Network Environment on a Transputer System) is to provide users a high degree of flexibility in creating and manipulating a wide variety of neural network topologies at processing speeds not found in conventional computing environments. To accomplish this purpose, NNETS supports back propagation and back propagation related algorithms. The back propagation algorithm used is an implementation of Rumelhart's Generalized Delta Rule. NNETS was developed on the INMOS Transputer. NNETS predefines a Back Propagation Network, a Jordan Network, and a Reinforcement Network to assist users in learning and defining their own networks. The program also allows users to configure other neural network paradigms from the NNETS basic architecture. The Jordan network is basically a feed forward network that has the outputs connected to a pseudo input layer. The state of the network is dependent on the inputs from the environment plus the state of the network. The Reinforcement network learns via a scalar feedback signal called reinforcement. The network propagates forward randomly. The environment looks at the outputs of the network to produce a reinforcement signal that is fed back to the network. NNETS was written for the INMOS C compiler D711B version 1.3 or later (MS-DOS version). A small portion of the software was written in the OCCAM language to perform the communications routing between processors. NNETS is configured to operate on a 4 X 10 array of Transputers in sequence with a Transputer based graphics processor controlled by a master IBM PC 286 (or better) Transputer. A RGB monitor is required which must be capable of 512 X 512 resolution. It must be able to receive red, green, and blue signals via BNC connectors. NNETS is meant for experienced Transputer users only. The program is distributed on 5.25 inch 1.2Mb MS-DOS format diskettes. NNETS was developed in 1991. Transputer and OCCAM are registered trademarks of Inmos Corporation. MS

  17. Adaptive Neural Control for a Class of Outputs Time-Delay Nonlinear Systems

    Directory of Open Access Journals (Sweden)

    Ruliang Wang

    2012-01-01

    Full Text Available This paper considers an adaptive neural control for a class of outputs time-delay nonlinear systems with perturbed or no. Based on RBF neural networks, the radius basis function (RBF neural networks is employed to estimate the unknown continuous functions. The proposed control guarantees that all closed-loop signals remain bounded. The simulation results demonstrate the effectiveness of the proposed control scheme.

  18. NNSYSID and NNCTRL Tools for system identification and control with neural networks

    DEFF Research Database (Denmark)

    Nørgaard, Magnus; Ravn, Ole; Poulsen, Niels Kjølstad

    2001-01-01

    a number of nonlinear model structures based on neural networks, effective training algorithms and tools for model validation and model structure selection. The NNCTRL toolkit is an add-on to NNSYSID and provides tools for design and simulation of control systems based on neural networks. The user can......Two toolsets for use with MATLAB have been developed: the neural network based system identification toolbox (NNSYSID) and the neural network based control system design toolkit (NNCTRL). The NNSYSID toolbox has been designed to assist identification of nonlinear dynamic systems. It contains...... choose among several designs such as direct inverse control, internal model control, nonlinear feedforward, feedback linearisation, optimal control, gain scheduling based on instantaneous linearisation of neural network models and nonlinear model predictive control. This article gives an overview...

  19. NNSYSID and NNCTRL Tools for system identification and control with neural networks

    DEFF Research Database (Denmark)

    Nørgaard, Magnus; Ravn, Ole; Poulsen, Niels Kjølstad

    2001-01-01

    choose among several designs such as direct inverse control, internal model control, nonlinear feedforward, feedback linearisation, optimal control, gain scheduling based on instantaneous linearisation of neural network models and nonlinear model predictive control. This article gives an overview......Two toolsets for use with MATLAB have been developed: the neural network based system identification toolbox (NNSYSID) and the neural network based control system design toolkit (NNCTRL). The NNSYSID toolbox has been designed to assist identification of nonlinear dynamic systems. It contains...... a number of nonlinear model structures based on neural networks, effective training algorithms and tools for model validation and model structure selection. The NNCTRL toolkit is an add-on to NNSYSID and provides tools for design and simulation of control systems based on neural networks. The user can...

  20. Hybrid energy system evaluation in water supply system energy production: neural network approach

    Energy Technology Data Exchange (ETDEWEB)

    Goncalves, Fabio V.; Ramos, Helena M. [Civil Engineering Department, Instituto Superior Tecnico, Technical University of Lisbon, Av. Rovisco Pais, 1049-001, Lisbon (Portugal); Reis, Luisa Fernanda R. [Universidade de Sao Paulo, EESC/USP, Departamento de Hidraulica e Saneamento., Avenida do Trabalhador Saocarlense, 400, Sao Carlos-SP (Brazil)

    2010-07-01

    Water supply systems are large consumers of energy and the use of hybrid systems for green energy production is this new proposal. This work presents a computational model based on neural networks to determine the best configuration of a hybrid system to generate energy in water supply systems. In this study the energy sources to make this hybrid system can be the national power grid, micro-hydro and wind turbines. The artificial neural network is composed of six layers, trained to use data generated by a model of hybrid configuration and an economic simulator - CES. The reason for the development of an advanced model of forecasting based on neural networks is to allow rapid simulation and proper interaction with hydraulic and power model simulator - HPS. The results show that this computational model is useful as advanced decision support system in the design of configurations of hybrid power systems applied to water supply systems, improving the solutions in the development of its global energy efficiency.

  1. On the Computational Power of Spiking Neural P Systems with Self-Organization

    Science.gov (United States)

    Wang, Xun; Song, Tao; Gong, Faming; Zheng, Pan

    2016-06-01

    Neural-like computing models are versatile computing mechanisms in the field of artificial intelligence. Spiking neural P systems (SN P systems for short) are one of the recently developed spiking neural network models inspired by the way neurons communicate. The communications among neurons are essentially achieved by spikes, i. e. short electrical pulses. In terms of motivation, SN P systems fall into the third generation of neural network models. In this study, a novel variant of SN P systems, namely SN P systems with self-organization, is introduced, and the computational power of the system is investigated and evaluated. It is proved that SN P systems with self-organization are capable of computing and accept the family of sets of Turing computable natural numbers. Moreover, with 87 neurons the system can compute any Turing computable recursive function, thus achieves Turing universality. These results demonstrate promising initiatives to solve an open problem arisen by Gh Păun.

  2. ANOMALY NETWORK INTRUSION DETECTION SYSTEM BASED ON DISTRIBUTED TIME-DELAY NEURAL NETWORK (DTDNN

    Directory of Open Access Journals (Sweden)

    LAHEEB MOHAMMAD IBRAHIM

    2010-12-01

    Full Text Available In this research, a hierarchical off-line anomaly network intrusion detection system based on Distributed Time-Delay Artificial Neural Network is introduced. This research aims to solve a hierarchical multi class problem in which the type of attack (DoS, U2R, R2L and Probe attack detected by dynamic neural network. The results indicate that dynamic neural nets (Distributed Time-Delay Artificial Neural Network can achieve a high detection rate, where the overall accuracy classification rate average is equal to 97.24%.

  3. An alternative respiratory sounds classification system utilizing artificial neural networks

    Directory of Open Access Journals (Sweden)

    Rami J Oweis

    2015-04-01

    Full Text Available Background: Computerized lung sound analysis involves recording lung sound via an electronic device, followed by computer analysis and classification based on specific signal characteristics as non-linearity and nonstationarity caused by air turbulence. An automatic analysis is necessary to avoid dependence on expert skills. Methods: This work revolves around exploiting autocorrelation in the feature extraction stage. All process stages were implemented in MATLAB. The classification process was performed comparatively using both artificial neural networks (ANNs and adaptive neuro-fuzzy inference systems (ANFIS toolboxes. The methods have been applied to 10 different respiratory sounds for classification. Results: The ANN was superior to the ANFIS system and returned superior performance parameters. Its accuracy, specificity, and sensitivity were 98.6%, 100%, and 97.8%, respectively. The obtained parameters showed superiority to many recent approaches. Conclusions: The promising proposed method is an efficient fast tool for the intended purpose as manifested in the performance parameters, specifically, accuracy, specificity, and sensitivity. Furthermore, it may be added that utilizing the autocorrelation function in the feature extraction in such applications results in enhanced performance and avoids undesired computation complexities compared to other techniques.

  4. An alternative respiratory sounds classification system utilizing artificial neural networks.

    Science.gov (United States)

    Oweis, Rami J; Abdulhay, Enas W; Khayal, Amer; Awad, Areen

    2015-01-01

    Computerized lung sound analysis involves recording lung sound via an electronic device, followed by computer analysis and classification based on specific signal characteristics as non-linearity and nonstationarity caused by air turbulence. An automatic analysis is necessary to avoid dependence on expert skills. This work revolves around exploiting autocorrelation in the feature extraction stage. All process stages were implemented in MATLAB. The classification process was performed comparatively using both artificial neural networks (ANNs) and adaptive neuro-fuzzy inference systems (ANFIS) toolboxes. The methods have been applied to 10 different respiratory sounds for classification. The ANN was superior to the ANFIS system and returned superior performance parameters. Its accuracy, specificity, and sensitivity were 98.6%, 100%, and 97.8%, respectively. The obtained parameters showed superiority to many recent approaches. The promising proposed method is an efficient fast tool for the intended purpose as manifested in the performance parameters, specifically, accuracy, specificity, and sensitivity. Furthermore, it may be added that utilizing the autocorrelation function in the feature extraction in such applications results in enhanced performance and avoids undesired computation complexities compared to other techniques.

  5. Neural network based optimal control of HVAC&R systems

    Science.gov (United States)

    Ning, Min

    Heating, Ventilation, Air-Conditioning and Refrigeration (HVAC&R) systems have wide applications in providing a desired indoor environment for different types of buildings. It is well acknowledged that 30%-40% of the total energy generated is consumed by buildings and HVAC&R systems alone account for more than 50% of the building energy consumption. Low operational efficiency especially under partial load conditions and poor control are part of reasons for such high energy consumption. To improve energy efficiency, HVAC&R systems should be properly operated to maintain a comfortable and healthy indoor environment under dynamic ambient and indoor conditions with the least energy consumption. This research focuses on the optimal operation of HVAC&R systems. The optimization problem is formulated and solved to find the optimal set points for the chilled water supply temperature, discharge air temperature and AHU (air handling unit) fan static pressure such that the indoor environment is maintained with the least chiller and fan energy consumption. To achieve this objective, a dynamic system model is developed first to simulate the system behavior under different control schemes and operating conditions. The system model is modular in structure, which includes a water-cooled vapor compression chiller model and a two-zone VAV system model. A fuzzy-set based extended transformation approach is then applied to investigate the uncertainties of this model caused by uncertain parameters and the sensitivities of the control inputs with respect to the interested model outputs. A multi-layer feed forward neural network is constructed and trained in unsupervised mode to minimize the cost function which is comprised of overall energy cost and penalty cost when one or more constraints are violated. After training, the network is implemented as a supervisory controller to compute the optimal settings for the system. In order to implement the optimal set points predicted by the

  6. Birth Outcomes of Children Fathered by Men Treated with Systemic Corticosteroids during the Conception Period

    DEFF Research Database (Denmark)

    Larsen, M D; Friedman, S; Magnussen, B

    2018-01-01

    Information on the safety of paternal use of medications prior to the time of conception is limited, and there is little available evidence regarding possible adverse effects of paternal use of systemic corticosteroids (SCS). In this cohort study, based on nationwide data, we examined the associa......Information on the safety of paternal use of medications prior to the time of conception is limited, and there is little available evidence regarding possible adverse effects of paternal use of systemic corticosteroids (SCS). In this cohort study, based on nationwide data, we examined...... the association between paternal use of SCS prior to conception and adverse birth outcomes. The study includes data from all singletons born in Denmark from 1January 1997 through 2013 (N=1,013,994). Children fathered by men who redeemed a prescription of SCS within 3 months before conception (N=2,380) constituted...... the exposed groups. The outcomes were congenital abnormalities (CAs), pre-term birth and small for gestational age (SGA). We adjusted for co-variates in multilevel logistic regression analyses. The adjusted odds ratios for pre-term birth and SGA were 0.81 (95% CI: 0.55-1.21) and 1.06 (95% CI: 0...

  7. PERFORMANCE COMPARISON FOR INTRUSION DETECTION SYSTEM USING NEURAL NETWORK WITH KDD DATASET

    Directory of Open Access Journals (Sweden)

    S. Devaraju

    2014-04-01

    Full Text Available Intrusion Detection Systems are challenging task for finding the user as normal user or attack user in any organizational information systems or IT Industry. The Intrusion Detection System is an effective method to deal with the kinds of problem in networks. Different classifiers are used to detect the different kinds of attacks in networks. In this paper, the performance of intrusion detection is compared with various neural network classifiers. In the proposed research the four types of classifiers used are Feed Forward Neural Network (FFNN, Generalized Regression Neural Network (GRNN, Probabilistic Neural Network (PNN and Radial Basis Neural Network (RBNN. The performance of the full featured KDD Cup 1999 dataset is compared with that of the reduced featured KDD Cup 1999 dataset. The MATLAB software is used to train and test the dataset and the efficiency and False Alarm Rate is measured. It is proved that the reduced dataset is performing better than the full featured dataset.

  8. Review: the role of neural crest cells in the endocrine system.

    Science.gov (United States)

    Adams, Meghan Sara; Bronner-Fraser, Marianne

    2009-01-01

    The neural crest is a pluripotent population of cells that arises at the junction of the neural tube and the dorsal ectoderm. These highly migratory cells form diverse derivatives including neurons and glia of the sensory, sympathetic, and enteric nervous systems, melanocytes, and the bones, cartilage, and connective tissues of the face. The neural crest has long been associated with the endocrine system, although not always correctly. According to current understanding, neural crest cells give rise to the chromaffin cells of the adrenal medulla, chief cells of the extra-adrenal paraganglia, and thyroid C cells. The endocrine tumors that correspond to these cell types are pheochromocytomas, extra-adrenal paragangliomas, and medullary thyroid carcinomas. Although controversies concerning embryological origin appear to have mostly been resolved, questions persist concerning the pathobiology of each tumor type and its basis in neural crest embryology. Here we present a brief history of the work on neural crest development, both in general and in application to the endocrine system. In particular, we present findings related to the plasticity and pluripotency of neural crest cells as well as a discussion of several different neural crest tumors in the endocrine system.

  9. Neural Control System in Obstacle Avoidance in Mobile Robots Using Ultrasonic Sensors

    Directory of Open Access Journals (Sweden)

    A. Medina-Santiago

    2014-02-01

    Full Text Available This paper presents the development and implementation of neural control systems in mobile robots in obstacle avoidance in real time using ultrasonic sensors with complex strategies of decision-making in development (Matlab and Processing. An Arduino embedded platform is used to implement the neural control for field results.

  10. Modeling of the height control system using artificial neural networks

    Directory of Open Access Journals (Sweden)

    A. R Tahavvor

    2016-09-01

    Full Text Available Introduction Automation of agricultural and machinery construction has generally been enhanced by intelligent control systems due to utility and efficiency rising, ease of use, profitability and upgrading according to market demand. A broad variety of industrial merchandise are now supplied with computerized control systems of earth moving processes to be performed by construction and agriculture field vehicle such as grader, backhoe, tractor and scraper machines. A height control machine which is used in measuring base thickness is consisted of two mechanical and electronic parts. The mechanical part is consisted of conveyor belt, main body, electrical engine and invertors while the electronic part is consisted of ultrasonic, wave transmitter and receiver sensor, electronic board, control set, and microcontroller. The main job of these controlling devices consists of the topographic surveying, cutting and filling of elevated and spotted low area, and these actions fundamentally dependent onthe machine's ability in elevation and thickness measurement and control. In this study, machine was first tested and then some experiments were conducted for data collection. Study of system modeling in artificial neural networks (ANN was done for measuring, controlling the height for bases by input variable input vectors such as sampling time, probe speed, conveyer speed, sound wave speed and speed sensor are finally the maximum and minimum probe output vector on various conditions. The result reveals the capability of this procedure for experimental recognition of sensors' behavior and improvement of field machine control systems. Inspection, calibration and response, diagnosis of the elevation control system in combination with machine function can also be evaluated by some extra development of this system. Materials and Methods Designing and manufacture of the planned apparatus classified in three dissimilar, mechanical and electronic module, courses of

  11. Fault diagnosis in satellite attitude control systems using artificial neural networkk

    Science.gov (United States)

    Ayodele I., Olanipekun

    The nonlinear behavior exhibited by altitude control system processes and also the presence of external constraints on the operating conditions causes hitch in the dynamics of system processes. This research work proposes a fault detection/tolerant prediction in an altitude control system. This is done through the artificial neural network fault detection by deploying the neural network approach. A fault detection and isolation module is developed in the actuator system of the Altitude Control System, thereby achieving the goal of this thesis. This can be done by two basic classification stages: Neural Residual Generator (Neural Observer)- This stage is responsible for generating residual errors that can reflect the real behavior of the entire process as against its normal conditions. Adaptive Neural Classifier - This stage is responsible for managing the isolation task of the fault detected by evaluating the generated residual errors from the neural estimator which gives detailed information about faults detected e.g., fault location and time. These two stages can be implemented by executing the tasks listed below: 1. Study and develop a generic three axis stabilized altitude control model based on the reaction wheels. This is established with three separate PD controllers designed for each reaction wheel of the satellite axis using the Matlab - SIMULINK. 2. Develop a dynamic neural network residual generator based on Dynamic Multilayer Perceptron Network (DMLP) which is then applied to the reaction wheel model designed commonly called the actuator in the altitude control system of a satellite 3. Develop a neural network adaptive classifier based on the Learning Vector Quantization (LVQ) model which is used for the isolation concept. The advantages of the proposed dynamic neural network and neural adaptive classifier approach are showcased.

  12. Artificial Neural Network-Based System for PET Volume Segmentation

    Directory of Open Access Journals (Sweden)

    Mhd Saeed Sharif

    2010-01-01

    Full Text Available Tumour detection, classification, and quantification in positron emission tomography (PET imaging at early stage of disease are important issues for clinical diagnosis, assessment of response to treatment, and radiotherapy planning. Many techniques have been proposed for segmenting medical imaging data; however, some of the approaches have poor performance, large inaccuracy, and require substantial computation time for analysing large medical volumes. Artificial intelligence (AI approaches can provide improved accuracy and save decent amount of time. Artificial neural networks (ANNs, as one of the best AI techniques, have the capability to classify and quantify precisely lesions and model the clinical evaluation for a specific problem. This paper presents a novel application of ANNs in the wavelet domain for PET volume segmentation. ANN performance evaluation using different training algorithms in both spatial and wavelet domains with a different number of neurons in the hidden layer is also presented. The best number of neurons in the hidden layer is determined according to the experimental results, which is also stated Levenberg-Marquardt backpropagation training algorithm as the best training approach for the proposed application. The proposed intelligent system results are compared with those obtained using conventional techniques including thresholding and clustering based approaches. Experimental and Monte Carlo simulated PET phantom data sets and clinical PET volumes of nonsmall cell lung cancer patients were utilised to validate the proposed algorithm which has demonstrated promising results.

  13. Honey characterization using computer vision system and artificial neural networks.

    Science.gov (United States)

    Shafiee, Sahameh; Minaei, Saeid; Moghaddam-Charkari, Nasrollah; Barzegar, Mohsen

    2014-09-15

    This paper reports the development of a computer vision system (CVS) for non-destructive characterization of honey based on colour and its correlated chemical attributes including ash content (AC), antioxidant activity (AA), and total phenolic content (TPC). Artificial neural network (ANN) models were applied to transform RGB values of images to CIE L*a*b* colourimetric measurements and to predict AC, TPC and AA from colour features of images. The developed ANN models were able to convert RGB values to CIE L*a*b* colourimetric parameters with low generalization error of 1.01±0.99. In addition, the developed models for prediction of AC, TPC and AA showed high performance based on colour parameters of honey images, as the R(2) values for prediction were 0.99, 0.98, and 0.87, for AC, AA and TPC, respectively. The experimental results show the effectiveness and possibility of applying CVS for non-destructive honey characterization by the industry. Copyright © 2014 Elsevier Ltd. All rights reserved.

  14. Progress Toward Adaptive Integration and Optimization of Automated and Neural Processing Systems: Establishing Neural and Behavioral Benchmarks of Optimized Performance

    Science.gov (United States)

    2014-11-01

    grid, using an Advanced Brain Monitoring (ABM) ×24 system configured with the single-trial event - related potential (ERP) sensor strip and operating...ROC curve BCI brain-computer interface EEG electroencephalogram ERP event - related potential EVUS estimated volume under the surface FOV field of...stations. 15. SUBJECT TERMS rapid serial visual presentation, RSVP, EEG, neural classification, P300 , brain-computer interface 16. SECURITY

  15. Predictive Control of Hydronic Floor Heating Systems using Neural Networks and Genetic Algorithms

    DEFF Research Database (Denmark)

    Vinther, Kasper; Green, Torben; Østergaard, Søren

    2017-01-01

    This paper presents the use a neural network and a micro genetic algorithm to optimize future set-points in existing hydronic floor heating systems for improved energy efficiency. The neural network can be trained to predict the impact of changes in set-points on future room temperatures. Additio...... space is not guaranteed. Evaluation of the performance of multiple neural networks is performed, using different levels of information, and optimization results are presented on a detailed house simulation model.......This paper presents the use a neural network and a micro genetic algorithm to optimize future set-points in existing hydronic floor heating systems for improved energy efficiency. The neural network can be trained to predict the impact of changes in set-points on future room temperatures...

  16. Compensating for Channel Fading in DS-CDMA Communication Systems Employing ICA Neural Network Detectors

    Directory of Open Access Journals (Sweden)

    David Overbye

    2005-06-01

    Full Text Available In this paper we examine the impact of channel fading on the bit error rate of a DS-CDMA communication system. The system employs detectors that incorporate neural networks effecting methods of independent component analysis (ICA, subspace estimation of channel noise, and Hopfield type neural networks. The Rayleigh fading channel model is used. When employed in a Rayleigh fading environment, the ICA neural network detectors that give superior performance in a flat fading channel did not retain this superior performance. We then present a new method of compensating for channel fading based on the incorporation of priors in the ICA neural network learning algorithms. When the ICA neural network detectors were compensated using the incorporation of priors, they give significantly better performance than the traditional detectors and the uncompensated ICA detectors. Keywords: CDMA, Multi-user Detection, Rayleigh Fading, Multipath Detection, Independent Component Analysis, Prior Probability Hebbian Learning, Natural Gradient

  17. Artificial Neural Systems Application to the Simulation of Air Combat Decision Making

    Science.gov (United States)

    1992-04-01

    unit, the CPU , whereas neural networks utilize the effects of many, simple processing elements. Traditional computing is done in a step-by-step, serial...Nielsen Neurocomputers (HNC). The ANZA-Plus coprocessor is part of an 80386 -based computer system which is optimized for training and executing neural...host computer for this program is a Zenith 386/16 system running under the DOS 3.31 operating system. The 80386 microprocessor in this machine operates

  18. Neural Network Expert System in the Application of Tower Fault Diagnosis

    Science.gov (United States)

    Liu, Xiaoyang; Xia, Zhongwu; Tao, Zhiyong; Zhao, Zhenlian

    For the corresponding fuzzy relationship between the fault symptoms and the fault causes in the process of tower crane operation, this paper puts forward a kind of rapid new method of fast detection and diagnosis for common fault based on neural network expert system. This paper makes full use of expert system and neural network advantages, and briefly introduces the structure, function, algorithm and realization of the adopted system. Results show that the new algorithm is feasible and can achieve rapid faults diagnosis.

  19. Research on architecture of intelligent design platform for artificial neural network expert system

    Science.gov (United States)

    Gu, Honghong

    2017-09-01

    Based on the review of the development and current situation of CAD technology, the necessity of combination of artificial neural network and expert system, and then present an intelligent design system based on artificial neural network. Moreover, it discussed the feasibility of realization of a design-oriented expert system development tools on the basis of above combination. In addition, knowledge representation strategy and method and the solving process are given in this paper.

  20. The system of physical training for women during pregnancy to a natural birth of healthy children.

    Directory of Open Access Journals (Sweden)

    Prusik Krzysztof

    2010-12-01

    Full Text Available The substantive provisions of the system of physical preparation of pregnant are considered in «Opening of flower». The system is built in obedience to the substantive provisions of theory and method of physical culture, a central place in the system occupies gymnastics for future mothers in verses about nature. A gymnastics contains undulations, directed on strengthening of muscles of small pelvis, development of flexibility and to elasticity. In experimental research took part 59 pregnant which parted on 4 groups in obedience to the level of their physical preparedness. It is rotined that application of the system of physical preparation of future mothers «Opening of flower» is rendered by positive influence on motion of births, instrumental in normalization of indexes of blood and urine tests, prevention of breaks of crotch and complications, substantial diminishing of the pain feelings during births. Application of the system of physical «Opening of flower» renders future mothers also positive influence on development of children.

  1. A case for spiking neural network simulation based on configurable multiple-FPGA systems.

    Science.gov (United States)

    Yang, Shufan; Wu, Qiang; Li, Renfa

    2011-09-01

    Recent neuropsychological research has begun to reveal that neurons encode information in the timing of spikes. Spiking neural network simulations are a flexible and powerful method for investigating the behaviour of neuronal systems. Simulation of the spiking neural networks in software is unable to rapidly generate output spikes in large-scale of neural network. An alternative approach, hardware implementation of such system, provides the possibility to generate independent spikes precisely and simultaneously output spike waves in real time, under the premise that spiking neural network can take full advantage of hardware inherent parallelism. We introduce a configurable FPGA-oriented hardware platform for spiking neural network simulation in this work. We aim to use this platform to combine the speed of dedicated hardware with the programmability of software so that it might allow neuroscientists to put together sophisticated computation experiments of their own model. A feed-forward hierarchy network is developed as a case study to describe the operation of biological neural systems (such as orientation selectivity of visual cortex) and computational models of such systems. This model demonstrates how a feed-forward neural network constructs the circuitry required for orientation selectivity and provides platform for reaching a deeper understanding of the primate visual system. In the future, larger scale models based on this framework can be used to replicate the actual architecture in visual cortex, leading to more detailed predictions and insights into visual perception phenomenon.

  2. Cancer and birth defects surveillance system for communities around the Savannah River Site: Phase 2 -- Birth defects. Technical progress report, year 01

    Energy Technology Data Exchange (ETDEWEB)

    Dunbar, J.B.

    1995-10-01

    The Savannah River Region Health Information System Birth Defects Registry (SRRHIS-BDR) began on September 30, 1994. As with the SRRHIS Cancer Registry, surveillance of the 12 Georgia counties was subcontracted to Emory University School of Public Health. Collaborative efforts between the Medical University of South Carolina (MUSC) and Emory University staffs have been characterized by warm relationships and commitment to developing a state of the art registry. As a result of early planning efforts, the authors were able to actually activate the data collection. As of the end of September 1995, partial data from the 1994 birth cohort and up-to-date data for the 1995 birth cohort had been collected on the South Carolina side. The Georgia Staff started later and have not yet caught up to the 1994 level. South Carolina was able to start earlier because they were fortunate to quickly recruit an abstractor. Also, by the end of the first year, an innovative automated data entry system for laptop computers was developed by the computer staff to facilitate and improve data collection.

  3. Neutron spectrometry and dosimetry by means of Bonner spheres system and artificial neural networks applying robust design of artificial neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Martinez B, M.R.; Ortiz R, J.M.; Vega C, H.R. [UAZ, Av. Ramon Lopez Velarde No. 801, 98000 Zacatecas (Mexico)

    2006-07-01

    An Artificial Neural Network has been designed, trained and tested to unfold neutron spectra and simultaneously to calculate equivalent doses. A set of 187 neutron spectra compiled by the International Atomic Energy Agency and 13 equivalent doses were used in the artificial neural network designed, trained and tested. In order to design the neural network was used the robust design of artificial neural networks methodology, which assures that the quality of the neural networks takes into account from the design stage. Unless previous works, here, for first time a group of neural networks were designed and trained to unfold 187 neutron spectra and at the same time to calculate 13 equivalent doses, starting from the count rates coming from the Bonner spheres system by using a systematic and experimental strategy. (Author)

  4. Simulation of Missile Autopilot with Two-Rate Hybrid Neural Network System

    Directory of Open Access Journals (Sweden)

    ASTROV, I.

    2007-04-01

    Full Text Available This paper proposes a two-rate hybrid neural network system, which consists of two artificial neural network subsystems. These neural network subsystems are used as the dynamic subsystems controllers.1 This is because such neuromorphic controllers are especially suitable to control complex systems. An illustrative example - two-rate neural network hybrid control of decomposed stochastic model of a rigid guided missile over different operating conditions - was carried out using the proposed two-rate state-space decomposition technique. This example demonstrates that this research technique results in simplified low-order autonomous control subsystems with various speeds of actuation, and shows the quality of the proposed technique. The obtained results show that the control tasks for the autonomous subsystems can be solved more qualitatively than for the original system. The simulation and animation results with use of software package Simulink demonstrate that this research technique would work for real-time stochastic systems.

  5. Identification of Complex Dynamical Systems with Neural Networks (2/2)

    CERN Multimedia

    CERN. Geneva

    2016-01-01

    The identification and analysis of high dimensional nonlinear systems is obviously a challenging task. Neural networks have been proven to be universal approximators but this still leaves the identification task a hard one. To do it efficiently, we have to violate some of the rules of classical regression theory. Furthermore we should focus on the interpretation of the resulting model to overcome its black box character. First, we will discuss function approximation with 3 layer feedforward neural networks up to new developments in deep neural networks and deep learning. These nets are not only of interest in connection with image analysis but are a center point of the current artificial intelligence developments. Second, we will focus on the analysis of complex dynamical system in the form of state space models realized as recurrent neural networks. After the introduction of small open dynamical systems we will study dynamical systems on manifolds. Here manifold and dynamics have to be identified in parall...

  6. Identification of Complex Dynamical Systems with Neural Networks (1/2)

    CERN Multimedia

    CERN. Geneva

    2016-01-01

    The identification and analysis of high dimensional nonlinear systems is obviously a challenging task. Neural networks have been proven to be universal approximators but this still leaves the identification task a hard one. To do it efficiently, we have to violate some of the rules of classical regression theory. Furthermore we should focus on the interpretation of the resulting model to overcome its black box character. First, we will discuss function approximation with 3 layer feedforward neural networks up to new developments in deep neural networks and deep learning. These nets are not only of interest in connection with image analysis but are a center point of the current artificial intelligence developments. Second, we will focus on the analysis of complex dynamical system in the form of state space models realized as recurrent neural networks. After the introduction of small open dynamical systems we will study dynamical systems on manifolds. Here manifold and dynamics have to be identified in parall...

  7. Adaptive Wavelet Neural Network Backstepping Sliding Mode Tracking Control for PMSM Drive System

    OpenAIRE

    Liu, Da; Li, Muguo

    2015-01-01

    This paper presents a wavelet neural network backstepping sliding mode controller (WNNBSSM) for permanent-magnet synchronous motor (PMSM) position servo control system. Backstepping sliding mode (BSSM) is utilized to guarantee favorable tracking performance and stability of the whole system, meanwhile, wavelet neural network (WNN) is used for approximating nonlinear uncertainties. The designed controller combined the merits of the backstepping sliding mode control with robust characteristics ...

  8. Real-time cerebellar neuroprosthetic system based on a spiking neural network model of motor learning.

    Science.gov (United States)

    Xu, Tao; Xiao, Na; Zhai, Xiaolong; Kwan Chan, Pak; Tin, Chung

    2018-02-01

    Damage to the brain, as a result of various medical conditions, impacts the everyday life of patients and there is still no complete cure to neurological disorders. Neuroprostheses that can functionally replace the damaged neural circuit have recently emerged as a possible solution to these problems. Here we describe the development of a real-time cerebellar neuroprosthetic system to substitute neural function in cerebellar circuitry for learning delay eyeblink conditioning (DEC). The system was empowered by a biologically realistic spiking neural network (SNN) model of the cerebellar neural circuit, which considers the neuronal population and anatomical connectivity of the network. The model simulated synaptic plasticity critical for learning DEC. This SNN model was carefully implemented on a field programmable gate array (FPGA) platform for real-time simulation. This hardware system was interfaced in in vivo experiments with anesthetized rats and it used neural spikes recorded online from the animal to learn and trigger conditioned eyeblink in the animal during training. This rat-FPGA hybrid system was able to process neuronal spikes in real-time with an embedded cerebellum model of ~10 000 neurons and reproduce learning of DEC with different inter-stimulus intervals. Our results validated that the system performance is physiologically relevant at both the neural (firing pattern) and behavioral (eyeblink pattern) levels. This integrated system provides the sufficient computation power for mimicking the cerebellar circuit in real-time. The system interacts with the biological system naturally at the spike level and can be generalized for including other neural components (neuron types and plasticity) and neural functions for potential neuroprosthetic applications.

  9. Real-time cerebellar neuroprosthetic system based on a spiking neural network model of motor learning

    Science.gov (United States)

    Xu, Tao; Xiao, Na; Zhai, Xiaolong; Chan, Pak Kwan; Tin, Chung

    2018-02-01

    Objective. Damage to the brain, as a result of various medical conditions, impacts the everyday life of patients and there is still no complete cure to neurological disorders. Neuroprostheses that can functionally replace the damaged neural circuit have recently emerged as a possible solution to these problems. Here we describe the development of a real-time cerebellar neuroprosthetic system to substitute neural function in cerebellar circuitry for learning delay eyeblink conditioning (DEC). Approach. The system was empowered by a biologically realistic spiking neural network (SNN) model of the cerebellar neural circuit, which considers the neuronal population and anatomical connectivity of the network. The model simulated synaptic plasticity critical for learning DEC. This SNN model was carefully implemented on a field programmable gate array (FPGA) platform for real-time simulation. This hardware system was interfaced in in vivo experiments with anesthetized rats and it used neural spikes recorded online from the animal to learn and trigger conditioned eyeblink in the animal during training. Main results. This rat-FPGA hybrid system was able to process neuronal spikes in real-time with an embedded cerebellum model of ~10 000 neurons and reproduce learning of DEC with different inter-stimulus intervals. Our results validated that the system performance is physiologically relevant at both the neural (firing pattern) and behavioral (eyeblink pattern) levels. Significance. This integrated system provides the sufficient computation power for mimicking the cerebellar circuit in real-time. The system interacts with the biological system naturally at the spike level and can be generalized for including other neural components (neuron types and plasticity) and neural functions for potential neuroprosthetic applications.

  10. Transient stability analysis of electric energy systems via a fuzzy ART-ARTMAP neural network

    Energy Technology Data Exchange (ETDEWEB)

    Ferreira, Wagner Peron; Silveira, Maria do Carmo G.; Lotufo, AnnaDiva P.; Minussi, Carlos. R. [Department of Electrical Engineering, Sao Paulo State University (UNESP), P.O. Box 31, 15385-000, Ilha Solteira, SP (Brazil)

    2006-04-15

    This work presents a methodology to analyze transient stability (first oscillation) of electric energy systems, using a neural network based on ART architecture (adaptive resonance theory), named fuzzy ART-ARTMAP neural network for real time applications. The security margin is used as a stability analysis criterion, considering three-phase short circuit faults with a transmission line outage. The neural network operation consists of two fundamental phases: the training and the analysis. The training phase needs a great quantity of processing for the realization, while the analysis phase is effectuated almost without computation effort. This is, therefore the principal purpose to use neural networks for solving complex problems that need fast solutions, as the applications in real time. The ART neural networks have as primordial characteristics the plasticity and the stability, which are essential qualities to the training execution and to an efficient analysis. The fuzzy ART-ARTMAP neural network is proposed seeking a superior performance, in terms of precision and speed, when compared to conventional ARTMAP, and much more when compared to the neural networks that use the training by backpropagation algorithm, which is a benchmark in neural network area. (author)

  11. NMTPY: A Flexible Toolkit for Advanced Neural Machine Translation Systems

    Directory of Open Access Journals (Sweden)

    Caglayan Ozan

    2017-10-01

    Full Text Available In this paper, we present nmtpy, a flexible Python toolkit based on Theano for training Neural Machine Translation and other neural sequence-to-sequence architectures. nmtpy decouples the specification of a network from the training and inference utilities to simplify the addition of a new architecture and reduce the amount of boilerplate code to be written. nmtpy has been used for LIUM’s top-ranked submissions to WMT Multimodal Machine Translation and News Translation tasks in 2016 and 2017.

  12. Infectious exposure in the first years of life and risk of central nervous system tumours in children: analysis of birth order, childcare attendance and seasonality of birth.

    Science.gov (United States)

    Schmidt, L S; Kamper-Jørgensen, M; Schmiegelow, K; Johansen, C; Lähteenmäki, P; Träger, C; Stokland, T; Grell, K; Gustafson, G; Kogner, P; Sehested, A; Schüz, J

    2010-05-25

    An infective, mostly viral basis has been found in different human cancers. To test the hypothesis of a possible infectious aetiology for central nervous system (CNS) tumours in children, we investigated the associations with proxy measures of exposure to infectious disease. In a large case-control study nested in the populations of Denmark, Norway, Sweden, and Finland of 4.4 million children, we studied the association of birth order and seasonal variation of birth with subsequent risk for CNS tumours. We identified 3983 children from the national cancer registries, and information on exposure was obtained from the high-quality national administrative health registries. We investigated the association between childcare attendance during the first 2 years of life and the risk for CNS tumours in a subset of Danish children with CNS tumours, using information from the Danish Childcare database. We observed no association between birth order and risk of CNS tumours overall (odds ratio (OR) for second born or later born vs first born, 1.03; 95% confidence interval (CI), 0.96-1.10) or by histological subgroup, and children with CNS tumours did not show a seasonal variation of birth that was distinct from that of the background population. Childcare attendance compared with homecare showed a slightly increased OR (1.29; 95% CI, 0.90-1.86) for CNS tumours, with the highest risk observed in children attending a crèche. The strongest association was observed for embryonal CNS tumours. We found no effect of age at enrolment or duration of enrolment in childcare. These results do not support the hypothesis that the burden of exposure to infectious disease in early childhood has an important role in the aetiology of paediatric CNS tumours.

  13. Fundamentals of computational intelligence neural networks, fuzzy systems, and evolutionary computation

    CERN Document Server

    Keller, James M; Fogel, David B

    2016-01-01

    This book covers the three fundamental topics that form the basis of computational intelligence: neural networks, fuzzy systems, and evolutionary computation. The text focuses on inspiration, design, theory, and practical aspects of implementing procedures to solve real-world problems. While other books in the three fields that comprise computational intelligence are written by specialists in one discipline, this book is co-written by current former Editor-in-Chief of IEEE Transactions on Neural Networks and Learning Systems, a former Editor-in-Chief of IEEE Transactions on Fuzzy Systems, and the founding Editor-in-Chief of IEEE Transactions on Evolutionary Computation. The coverage across the three topics is both uniform and consistent in style and notation. Discusses single-layer and multilayer neural networks, radial-basi function networks, and recurrent neural networks Covers fuzzy set theory, fuzzy relations, fuzzy logic interference, fuzzy clustering and classification, fuzzy measures and fuzz...

  14. Adaptive Output-Feedback Neural Control of Switched Uncertain Nonlinear Systems With Average Dwell Time.

    Science.gov (United States)

    Long, Lijun; Zhao, Jun

    2015-07-01

    This paper investigates the problem of adaptive neural tracking control via output-feedback for a class of switched uncertain nonlinear systems without the measurements of the system states. The unknown control signals are approximated directly by neural networks. A novel adaptive neural control technique for the problem studied is set up by exploiting the average dwell time method and backstepping. A switched filter and different update laws are designed to reduce the conservativeness caused by adoption of a common observer and a common update law for all subsystems. The proposed controllers of subsystems guarantee that all closed-loop signals remain bounded under a class of switching signals with average dwell time, while the output tracking error converges to a small neighborhood of the origin. As an application of the proposed design method, adaptive output feedback neural tracking controllers for a mass-spring-damper system are constructed.

  15. Radial Basis Function Neural Network-based PID model for functional electrical stimulation system control.

    Science.gov (United States)

    Cheng, Longlong; Zhang, Guangju; Wan, Baikun; Hao, Linlin; Qi, Hongzhi; Ming, Dong

    2009-01-01

    Functional electrical stimulation (FES) has been widely used in the area of neural engineering. It utilizes electrical current to activate nerves innervating extremities affected by paralysis. An effective combination of a traditional PID controller and a neural network, being capable of nonlinear expression and adaptive learning property, supply a more reliable approach to construct FES controller that help the paraplegia complete the action they want. A FES system tuned by Radial Basis Function (RBF) Neural Network-based Proportional-Integral-Derivative (PID) model was designed to control the knee joint according to the desired trajectory through stimulation of lower limbs muscles in this paper. Experiment result shows that the FES system with RBF Neural Network-based PID model get a better performance when tracking the preset trajectory of knee angle comparing with the system adjusted by Ziegler- Nichols tuning PID model.

  16. Software implementation of artificial neural networks in automated intelligent systems

    Directory of Open Access Journals (Sweden)

    В.П. Харченко

    2009-02-01

    Full Text Available  Application of neural networks technologies effectively decides the task of synthesis of origin of accident risk and gives out the vector of managing signals of network on incomplete and distorted information about the phenomena, events and processes which influence on safety flights.

  17. Credit Risk Evaluation System: An Artificial Neural Network Approach

    African Journals Online (AJOL)

    In this paper, we proposed an architecture which uses the theory of artificial neural networks and business rules to correctly determine whether a customer is good or bad. In the first step, by using clustering algorithm, clients are segmented into groups with similar features. In the second step, decision trees are built based ...

  18. Integrating resource defence theory with a neural nonapeptide pathway to explain territory-based mating systems.

    Science.gov (United States)

    Oldfield, Ronald G; Harris, Rayna M; Hofmann, Hans A

    2015-01-01

    The ultimate-level factors that drive the evolution of mating systems have been well studied, but an evolutionarily conserved neural mechanism involved in shaping behaviour and social organization across species has remained elusive. Here, we review studies that have investigated the role of neural arginine vasopressin (AVP), vasotocin (AVT), and their receptor V1a in mediating variation in territorial behaviour. First, we discuss how aggression and territoriality are a function of population density in an inverted-U relationship according to resource defence theory, and how territoriality influences some mating systems. Next, we find that neural AVP, AVT, and V1a expression, especially in one particular neural circuit involving the lateral septum of the forebrain, are associated with territorial behaviour in males of diverse species, most likely due to their role in enhancing social cognition. Then we review studies that examined multiple species and find that neural AVP, AVT, and V1a expression is associated with territory size in mammals and fishes. Because territoriality plays an important role in shaping mating systems in many species, we present the idea that neural AVP, AVT, and V1a expression that is selected to mediate territory size may also influence the evolution of different mating systems. Future research that interprets proximate-level neuro-molecular mechanisms in the context of ultimate-level ecological theory may provide deep insight into the brain-behaviour relationships that underlie the diversity of social organization and mating systems seen across the animal kingdom.

  19. Multi-channel holographic birfurcative neural network system for real-time adaptive EOS data analysis

    Science.gov (United States)

    Liu, Hua-Kuang; Diep, J.; Huang, K.

    1991-01-01

    Viewgraphs on multi-channel holographic bifurcative neural network system for real-time adaptive Earth Observing System (EOS) data analysis are presented. The objective is to research and develop an optical bifurcating neuromorphic pattern recognition system for making optical data array comparisons and to evaluate the use of the system for EOS data classification, reduction, analysis, and other applications.

  20. Birth Control Pill

    Science.gov (United States)

    ... Counselors Kidney Stones Brain and Nervous System Birth Control Pill KidsHealth > For Teens > Birth Control Pill Print A A A What's in this ... La píldora anticonceptiva What Is It? The birth control pill (also called "the Pill") is a daily ...

  1. Birth Control Shot

    Science.gov (United States)

    ... Counselors Kidney Stones Brain and Nervous System Birth Control Shot KidsHealth > For Teens > Birth Control Shot Print A A A What's in this ... La inyección anticonceptiva What Is It? The birth control shot is a long-acting form of progesterone, ...

  2. An integrated systems biology approach to the study of preterm birth using "-omic" technology - a guideline for research

    Directory of Open Access Journals (Sweden)

    Kelley Maureen

    2011-10-01

    Full Text Available Abstract Preterm birth is the leading cause of neonatal mortality and perinatal morbidity. The etiology of preterm is multi-factorial and still unclear. As evidence increases for a genetic contribution to PTB, so does the need to explore genomics, transcriptomics, proteomics and metabolomics in its study. This review suggests research guidelines for the conduct of high throughput systems biology investigations into preterm birth with the expectation that this will facilitate the sharing of samples and data internationally through consortia, generating the power needed to study preterm birth using integrated "-omics" technologies. The issues to be addressed include: (1 integrated "-omics" approaches, (2 phenotyping, (3 sample collection, (4 data management-integrative databases, (5 international consortia and (6 translational feasibility. This manuscript is the product of discussions initiated by the "-Omics" Working Group at the Preterm Birth International Collaborative Meeting held at the World Health Organization, Geneva, Switzerland in April 2009.

  3. Design of intelligent systems based on fuzzy logic, neural networks and nature-inspired optimization

    CERN Document Server

    Castillo, Oscar; Kacprzyk, Janusz

    2015-01-01

    This book presents recent advances on the design of intelligent systems based on fuzzy logic, neural networks and nature-inspired optimization and their application in areas such as, intelligent control and robotics, pattern recognition, time series prediction and optimization of complex problems. The book is organized in eight main parts, which contain a group of papers around a similar subject. The first part consists of papers with the main theme of theoretical aspects of fuzzy logic, which basically consists of papers that propose new concepts and algorithms based on fuzzy systems. The second part contains papers with the main theme of neural networks theory, which are basically papers dealing with new concepts and algorithms in neural networks. The third part contains papers describing applications of neural networks in diverse areas, such as time series prediction and pattern recognition. The fourth part contains papers describing new nature-inspired optimization algorithms. The fifth part presents div...

  4. Developing and using expert systems and neural networks in medicine: a review on benefits and challenges.

    Science.gov (United States)

    Sheikhtaheri, Abbas; Sadoughi, Farahnaz; Hashemi Dehaghi, Zahra

    2014-09-01

    Complicacy of clinical decisions justifies utilization of information systems such as artificial intelligence (e.g. expert systems and neural networks) to achieve better decisions, however, application of these systems in the medical domain faces some challenges. We aimed at to review the applications of these systems in the medical domain and discuss about such challenges. Following a brief introduction of expert systems and neural networks by representing few examples, the challenges of these systems in the medical domain are discussed. We found that the applications of expert systems and artificial neural networks have been increased in the medical domain. These systems have shown many advantages such as utilization of experts' knowledge, gaining rare knowledge, more time for assessment of the decision, more consistent decisions, and shorter decision-making process. In spite of all these advantages, there are challenges ahead of developing and using such systems including maintenance, required experts, inputting patients' data into the system, problems for knowledge acquisition, problems in modeling medical knowledge, evaluation and validation of system performance, wrong recommendations and responsibility, limited domains of such systems and necessity of integrating such systems into the routine work flows. We concluded that expert systems and neural networks can be successfully used in medicine; however, there are many concerns and questions to be answered through future studies and discussions.

  5. Soft computing integrating evolutionary, neural, and fuzzy systems

    CERN Document Server

    Tettamanzi, Andrea

    2001-01-01

    Soft computing encompasses various computational methodologies, which, unlike conventional algorithms, are tolerant of imprecision, uncertainty, and partial truth. Soft computing technologies offer adaptability as a characteristic feature and thus permit the tracking of a problem through a changing environment. Besides some recent developments in areas like rough sets and probabilistic networks, fuzzy logic, evolutionary algorithms, and artificial neural networks are core ingredients of soft computing, which are all bio-inspired and can easily be combined synergetically. This book presents a well-balanced integration of fuzzy logic, evolutionary computing, and neural information processing. The three constituents are introduced to the reader systematically and brought together in differentiated combinations step by step. The text was developed from courses given by the authors and offers numerous illustrations as

  6. Psychological Processing in Chronic Pain: A Neural Systems Approach

    Science.gov (United States)

    Simons, Laura; Elman, Igor; Borsook, David

    2014-01-01

    Our understanding of chronic pain involves complex brain circuits that include sensory, emotional, cognitive and interoceptive processing. The feed-forward interactions between physical (e.g., trauma) and emotional pain and the consequences of altered psychological status on the expression of pain have made the evaluation and treatment of chronic pain a challenge in the clinic. By understanding the neural circuits involved in psychological processes, a mechanistic approach to the implementation of psychology-based treatments may be better understood. In this review we evaluate some of the principle processes that may be altered as a consequence of chronic pain in the context of localized and integrated neural networks. These changes are ongoing, vary in their magnitude, and their hierarchical manifestations, and may be temporally and sequentially altered by treatments, and all contribute to an overall pain phenotype. Furthermore, we link altered psychological processes to specific evidence-based treatments to put forth a model of pain neuroscience psychology. PMID:24374383

  7. Identification and adaptive neural network control of a DC motor system with dead-zone characteristics.

    Science.gov (United States)

    Peng, Jinzhu; Dubay, Rickey

    2011-10-01

    In this paper, an adaptive control approach based on the neural networks is presented to control a DC motor system with dead-zone characteristics (DZC), where two neural networks are proposed to formulate the traditional identification and control approaches. First, a Wiener-type neural network (WNN) is proposed to identify the motor DZC, which formulates the Wiener model with a linear dynamic block in cascade with a nonlinear static gain. Second, a feedforward neural network is proposed to formulate the traditional PID controller, termed as PID-type neural network (PIDNN), which is then used to control and compensate for the DZC. In this way, the DC motor system with DZC is identified by the WNN identifier, which provides model information to the PIDNN controller in order to make it adaptive. Back-propagation algorithms are used to train both neural networks. Also, stability and convergence analysis are conducted using the Lyapunov theorem. Finally, experiments on the DC motor system demonstrated accurate identification and good compensation for dead-zone with improved control performance over the conventional PID control. Copyright © 2011 ISA. Published by Elsevier Ltd. All rights reserved.

  8. Neuromorphic neural interfaces: from neurophysiological inspiration to biohybrid coupling with nervous systems

    Science.gov (United States)

    Broccard, Frédéric D.; Joshi, Siddharth; Wang, Jun; Cauwenberghs, Gert

    2017-08-01

    Objective. Computation in nervous systems operates with different computational primitives, and on different hardware, than traditional digital computation and is thus subjected to different constraints from its digital counterpart regarding the use of physical resources such as time, space and energy. In an effort to better understand neural computation on a physical medium with similar spatiotemporal and energetic constraints, the field of neuromorphic engineering aims to design and implement electronic systems that emulate in very large-scale integration (VLSI) hardware the organization and functions of neural systems at multiple levels of biological organization, from individual neurons up to large circuits and networks. Mixed analog/digital neuromorphic VLSI systems are compact, consume little power and operate in real time independently of the size and complexity of the model. Approach. This article highlights the current efforts to interface neuromorphic systems with neural systems at multiple levels of biological organization, from the synaptic to the system level, and discusses the prospects for future biohybrid systems with neuromorphic circuits of greater complexity. Main results. Single silicon neurons have been interfaced successfully with invertebrate and vertebrate neural networks. This approach allowed the investigation of neural properties that are inaccessible with traditional techniques while providing a realistic biological context not achievable with traditional numerical modeling methods. At the network level, populations of neurons are envisioned to communicate bidirectionally with neuromorphic processors of hundreds or thousands of silicon neurons. Recent work on brain-machine interfaces suggests that this is feasible with current neuromorphic technology. Significance. Biohybrid interfaces between biological neurons and VLSI neuromorphic systems of varying complexity have started to emerge in the literature. Primarily intended as a

  9. Birth Control

    Science.gov (United States)

    Birth control, also known as contraception, is designed to prevent pregnancy. Birth control methods may work in a number of different ... eggs that could be fertilized. Types include birth control pills, patches, shots, vaginal rings, and emergency contraceptive ...

  10. Clinical documentation variations and NLP system portability: a case study in asthma birth cohorts across institutions.

    Science.gov (United States)

    Sohn, Sunghwan; Wang, Yanshan; Wi, Chung-Il; Krusemark, Elizabeth A; Ryu, Euijung; Ali, Mir H; Juhn, Young J; Liu, Hongfang

    2017-11-30

    To assess clinical documentation variations across health care institutions using different electronic medical record systems and investigate how they affect natural language processing (NLP) system portability. Birth cohorts from Mayo Clinic and Sanford Children's Hospital (SCH) were used in this study (n = 298 for each). Documentation variations regarding asthma between the 2 cohorts were examined in various aspects: (1) overall corpus at the word level (ie, lexical variation), (2) topics and asthma-related concepts (ie, semantic variation), and (3) clinical note types (ie, process variation). We compared those statistics and explored NLP system portability for asthma ascertainment in 2 stages: prototype and refinement. There exist notable lexical variations (word-level similarity = 0.669) and process variations (differences in major note types containing asthma-related concepts). However, semantic-level corpora were relatively homogeneous (topic similarity = 0.944, asthma-related concept similarity = 0.971). The NLP system for asthma ascertainment had an F-score of 0.937 at Mayo, and produced 0.813 (prototype) and 0.908 (refinement) when applied at SCH. The criteria for asthma ascertainment are largely dependent on asthma-related concepts. Therefore, we believe that semantic similarity is important to estimate NLP system portability. As the Mayo Clinic and SCH corpora were relatively homogeneous at a semantic level, the NLP system, developed at Mayo Clinic, was imported to SCH successfully with proper adjustments to deal with the intrinsic corpus heterogeneity.

  11. A Comparative Study of Neural Networks and Fuzzy Systems in Modeling of a Nonlinear Dynamic System

    Directory of Open Access Journals (Sweden)

    Metin Demirtas

    2011-07-01

    Full Text Available The aim of this paper is to compare the neural networks and fuzzy modeling approaches on a nonlinear system. We have taken Permanent Magnet Brushless Direct Current (PMBDC motor data and have generated models using both approaches. The predictive performance of both methods was compared on the data set for model configurations. The paper describes the results of these tests and discusses the effects of changing model parameters on predictive and practical performance. Modeling sensitivity was used to compare for two methods.

  12. Attendance at prenatal care and adverse birth outcomes in China: A follow-up study based on Maternal and Newborn's Health Monitoring System.

    Science.gov (United States)

    Huang, Aiqun; Wu, Keye; Zhao, Wei; Hu, Huanqing; Yang, Qi; Chen, Dafang

    2018-02-01

    to evaluate the independent association between attendance at prenatal care and adverse birth outcomes in China, measured either as the occurrence of preterm birth or low birth weight. a follow-up study. the data was collected from maternal and newborn's health monitoring system at 6 provinces in China. all pregnant women registered in the system at their first prenatal care visit. We included 40152 registered pregnant women who had delivered between October 2013 and September 2014. attendance at prenatal care was evaluated using Kessner index. χ2 tests were used to examine the correlations between demographic characteristics and preterm birth or low birth weight. The associations between attendance at prenatal care and birth outcomes were explored using multilevel mixed-effects logistic regression models. the prevalence for preterm birth and low birth weight was 3.31% and 2.55%. The null models showed region clustering on birth outcomes. Compared with women who received adequate prenatal care, those with intermediate prenatal care (adjusted OR 1.62, 95%CI 1.37-1.92) or inadequate prenatal care (adjusted OR 2.78, 95%CI 2.24-3.44) had significantly increased risks for preterm birth, and women with intermediate prenatal care (adjusted OR 1.31, 95%CI 1.10-1.55) or inadequate prenatal care (adjusted OR 1.70, 95%CI 1.32-2.19) had significantly increased risks for low birth weight. We found very significant dose-response patterns for both preterm birth (p-trendattendance at prenatal care in China has independent effects on both preterm birth and low birth weight. Appropriate timing and number of prenatal care visits can help to reduce the occurrence of preterm birth or low birth weight. Copyright © 2017 Elsevier Ltd. All rights reserved.

  13. Fault detection and classification in electrical power transmission system using artificial neural network.

    Science.gov (United States)

    Jamil, Majid; Sharma, Sanjeev Kumar; Singh, Rajveer

    2015-01-01

    This paper focuses on the detection and classification of the faults on electrical power transmission line using artificial neural networks. The three phase currents and voltages of one end are taken as inputs in the proposed scheme. The feed forward neural network along with back propagation algorithm has been employed for detection and classification of the fault for analysis of each of the three phases involved in the process. A detailed analysis with varying number of hidden layers has been performed to validate the choice of the neural network. The simulation results concluded that the present method based on the neural network is efficient in detecting and classifying the faults on transmission lines with satisfactory performances. The different faults are simulated with different parameters to check the versatility of the method. The proposed method can be extended to the Distribution network of the Power System. The various simulations and analysis of signals is done in the MATLAB(®) environment.

  14. Nonoptimal component placement, but short processing paths, due to long-distance projections in neural systems.

    Directory of Open Access Journals (Sweden)

    Marcus Kaiser

    2006-07-01

    Full Text Available It has been suggested that neural systems across several scales of organization show optimal component placement, in which any spatial rearrangement of the components would lead to an increase of total wiring. Using extensive connectivity datasets for diverse neural networks combined with spatial coordinates for network nodes, we applied an optimization algorithm to the network layouts, in order to search for wire-saving component rearrangements. We found that optimized component rearrangements could substantially reduce total wiring length in all tested neural networks. Specifically, total wiring among 95 primate (Macaque cortical areas could be decreased by 32%, and wiring of neuronal networks in the nematode Caenorhabditis elegans could be reduced by 48% on the global level, and by 49% for neurons within frontal ganglia. Wiring length reductions were possible due to the existence of long-distance projections in neural networks. We explored the role of these projections by comparing the original networks with minimally rewired networks of the same size, which possessed only the shortest possible connections. In the minimally rewired networks, the number of processing steps along the shortest paths between components was significantly increased compared to the original networks. Additional benchmark comparisons also indicated that neural networks are more similar to network layouts that minimize the length of processing paths, rather than wiring length. These findings suggest that neural systems are not exclusively optimized for minimal global wiring, but for a variety of factors including the minimization of processing steps.

  15. Adaptive Control of Nonlinear Discrete-Time Systems by Using OS-ELM Neural Networks

    Directory of Open Access Journals (Sweden)

    Xiao-Li Li

    2014-01-01

    Full Text Available As a kind of novel feedforward neural network with single hidden layer, ELM (extreme learning machine neural networks are studied for the identification and control of nonlinear dynamic systems. The property of simple structure and fast convergence of ELM can be shown clearly. In this paper, we are interested in adaptive control of nonlinear dynamic plants by using OS-ELM (online sequential extreme learning machine neural networks. Based on data scope division, the problem that training process of ELM neural network is sensitive to the initial training data is also solved. According to the output range of the controlled plant, the data corresponding to this range will be used to initialize ELM. Furthermore, due to the drawback of conventional adaptive control, when the OS-ELM neural network is used for adaptive control of the system with jumping parameters, the topological structure of the neural network can be adjusted dynamically by using multiple model switching strategy, and an MMAC (multiple model adaptive control will be used to improve the control performance. Simulation results are included to complement the theoretical results.

  16. CENTRAL NERVOUS SYSTEM BIRTH DEFECTS IN SURGICALLY TREATED INFANTS IN SARAJEVO REGION OF BOSNIA AND HERZEGOVINA

    Science.gov (United States)

    Aličelebić, Selma; Arslanagić, Alma; Mornjaković, Zakira

    2007-01-01

    Congenital anomalies of the central nervous system (CNS) are common. The prevalence of these anomalies shows considerable geographical variation and female predominance. The aim of this work was to obtain the frequency of different CNS congenital anomalies types and their sex distribution among cases hospitalized in a Department of Neurosurgery, University of Sarajevo Clinics Center, Bosnia and Herzegovina, during the period January 2001 to December 2004. Retrospective study was carried out on the basis of the clinical records. Standard methods of descriptive statistics were performed for the data analysis. A total of 103 cases were surgically treated in the period from 2001 through 2004. Out of that number 56 (54,4%) were female patients, while 47 (46,6%) were male patients. Seven different CNS birth defect types were found in this investigation. These were: spina bifida (42 cases or 40,78%), congenital hydrocephalus (35 cases or 33,98%), arachnoid cyst (15 cases or 14,56%), Dandy-Walker syndrome (5 cases or 4,85%), dermoid cyst (4 cases or 3,88%), one of Arnold-Chiari syndrome (0,98%) and one of encefalocele (0,98%). According to this investigation, CNS congenital birth defects were slightly higher in females (54,4%). The most frequent types were spina bifida (40,78%) both in females (22,33%) and in males (18,45%), hydrocephalus (33,98%) and arachnoid cyst (14,56%). The anomalies of the other organ systems, associated with CNS anomalies obtained in this investigation, were pes equinovarus, cheiloshisis, cardiomegalia and palatoshisis. They were found in six cases (5,82%), equal in both sexes. PMID:18039185

  17. A Modular Neural Network Scheme Applied to Fault Diagnosis in Electric Power Systems

    Science.gov (United States)

    Flores, Agustín; Morant, Francisco

    2014-01-01

    This work proposes a new method for fault diagnosis in electric power systems based on neural modules. With this method the diagnosis is performed by assigning a neural module for each type of component comprising the electric power system, whether it is a transmission line, bus or transformer. The neural modules for buses and transformers comprise two diagnostic levels which take into consideration the logic states of switches and relays, both internal and back-up, with the exception of the neural module for transmission lines which also has a third diagnostic level which takes into account the oscillograms of fault voltages and currents as well as the frequency spectrums of these oscillograms, in order to verify if the transmission line had in fact been subjected to a fault. One important advantage of the diagnostic system proposed is that its implementation does not require the use of a network configurator for the system; it does not depend on the size of the power network nor does it require retraining of the neural modules if the power network increases in size, making its application possible to only one component, a specific area, or the whole context of the power system. PMID:25610897

  18. A Modular Neural Network Scheme Applied to Fault Diagnosis in Electric Power Systems

    Directory of Open Access Journals (Sweden)

    Agustín Flores

    2014-01-01

    Full Text Available This work proposes a new method for fault diagnosis in electric power systems based on neural modules. With this method the diagnosis is performed by assigning a neural module for each type of component comprising the electric power system, whether it is a transmission line, bus or transformer. The neural modules for buses and transformers comprise two diagnostic levels which take into consideration the logic states of switches and relays, both internal and back-up, with the exception of the neural module for transmission lines which also has a third diagnostic level which takes into account the oscillograms of fault voltages and currents as well as the frequency spectrums of these oscillograms, in order to verify if the transmission line had in fact been subjected to a fault. One important advantage of the diagnostic system proposed is that its implementation does not require the use of a network configurator for the system; it does not depend on the size of the power network nor does it require retraining of the neural modules if the power network increases in size, making its application possible to only one component, a specific area, or the whole context of the power system.

  19. Fuzzy wavelet plus a quantum neural network as a design base for power system stability enhancement.

    Science.gov (United States)

    Ganjefar, Soheil; Tofighi, Morteza; Karami, Hamidreza

    2015-11-01

    In this study, we introduce an indirect adaptive fuzzy wavelet neural controller (IAFWNC) as a power system stabilizer to damp inter-area modes of oscillations in a multi-machine power system. Quantum computing is an efficient method for improving the computational efficiency of neural networks, so we developed an identifier based on a quantum neural network (QNN) to train the IAFWNC in the proposed scheme. All of the controller parameters are tuned online based on the Lyapunov stability theory to guarantee the closed-loop stability. A two-machine, two-area power system equipped with a static synchronous series compensator as a series flexible ac transmission system was used to demonstrate the effectiveness of the proposed controller. The simulation and experimental results demonstrated that the proposed IAFWNC scheme can achieve favorable control performance. Copyright © 2015 Elsevier Ltd. All rights reserved.

  20. Child Maltreatment and Neural Systems Underlying Emotion Regulation.

    Science.gov (United States)

    McLaughlin, Katie A; Peverill, Matthew; Gold, Andrea L; Alves, Sonia; Sheridan, Margaret A

    2015-09-01

    The strong associations between child maltreatment and psychopathology have generated interest in identifying neurodevelopmental processes that are disrupted following maltreatment. Previous research has focused largely on neural response to negative facial emotion. We determined whether child maltreatment was associated with neural responses during passive viewing of negative and positive emotional stimuli and effortful attempts to regulate emotional responses. A total of 42 adolescents aged 13 to 19 years, half with exposure to physical and/or sexual abuse, participated. Blood oxygen level-dependent (BOLD) response was measured during passive viewing of negative and positive emotional stimuli and attempts to modulate emotional responses using cognitive reappraisal. Maltreated adolescents exhibited heightened response in multiple nodes of the salience network, including amygdala, putamen, and anterior insula, to negative relative to neutral stimuli. During attempts to decrease responses to negative stimuli relative to passive viewing, maltreatment was associated with greater recruitment of superior frontal gyrus, dorsal anterior cingulate cortex, and frontal pole; adolescents with and without maltreatment down-regulated amygdala response to a similar degree. No associations were observed between maltreatment and neural response to positive emotional stimuli during passive viewing or effortful regulation. Child maltreatment heightens the salience of negative emotional stimuli. Although maltreated adolescents modulate amygdala responses to negative cues to a degree similar to that of non-maltreated youths, they use regions involved in effortful control to a greater degree to do so, potentially because greater effort is required to modulate heightened amygdala responses. These findings are promising, given the centrality of cognitive restructuring in trauma-focused treatments for children. Copyright © 2015 American Academy of Child and Adolescent Psychiatry

  1. Neural Systems Underlying Individual Differences in Intertemporal Decision-making.

    Science.gov (United States)

    Elton, Amanda; Smith, Christopher T; Parrish, Michael H; Boettiger, Charlotte A

    2017-03-01

    Excessively choosing immediate over larger future rewards, or delay discounting (DD), associates with multiple clinical conditions. Individual differences in DD likely depend on variations in the activation of and functional interactions between networks, representing possible endophenotypes for associated disorders, including alcohol use disorders (AUDs). Numerous fMRI studies have probed the neural bases of DD, but investigations of large-scale networks remain scant. We addressed this gap by testing whether activation within large-scale networks during Now/Later decision-making predicts individual differences in DD. To do so, we scanned 95 social drinkers (18-40 years old; 50 women) using fMRI during hypothetical choices between small monetary amounts available "today" or larger amounts available later. We identified neural networks engaged during Now/Later choice using independent component analysis and tested the relationship between component activation and degree of DD. The activity of two components during Now/Later choice correlated with individual DD rates: A temporal lobe network positively correlated with DD, whereas a frontoparietal-striatal network negatively correlated with DD. Activation differences between these networks predicted individual differences in DD, and their negative correlation during Now/Later choice suggests functional competition. A generalized psychophysiological interactions analysis confirmed a decrease in their functional connectivity during decision-making. The functional connectivity of these two networks negatively correlates with alcohol-related harm, potentially implicating these networks in AUDs. These findings provide novel insight into the neural underpinnings of individual differences in impulsive decision-making with potential implications for addiction and related disorders in which impulsivity is a defining feature.

  2. Study of the neural dynamics for understanding communication in terms of complex hetero systems.

    Science.gov (United States)

    Tsuda, Ichiro; Yamaguchi, Yoko; Hashimoto, Takashi; Okuda, Jiro; Kawasaki, Masahiro; Nagasaka, Yasuo

    2015-01-01

    The purpose of the research project was to establish a new research area named "neural information science for communication" by elucidating its neural mechanism. The research was performed in collaboration with applied mathematicians in complex-systems science and experimental researchers in neuroscience. The project included measurements of brain activity during communication with or without languages and analyses performed with the help of extended theories for dynamical systems and stochastic systems. The communication paradigm was extended to the interactions between human and human, human and animal, human and robot, human and materials, and even animal and animal. Copyright © 2014 Elsevier Ireland Ltd and the Japan Neuroscience Society. All rights reserved.

  3. Sign Language Recognition System using Neural Network for Digital Hardware Implementation

    Energy Technology Data Exchange (ETDEWEB)

    Vargas, Lorena P [Lorena Vargas Quintero, Optic and Computer Science Group - Universidad Popular del Cesar (Colombia); Barba, Leiner; Torres, C O; Mattos, L, E-mail: vargas.lorena@yahoo.com [Optic and Computer Science Group - Popular of Cesar University, Km 12, Valledupar (Colombia)

    2011-01-01

    This work presents an image pattern recognition system using neural network for the identification of sign language to deaf people. The system has several stored image that show the specific symbol in this kind of language, which is employed to teach a multilayer neural network using a back propagation algorithm. Initially, the images are processed to adapt them and to improve the performance of discriminating of the network, including in this process of filtering, reduction and elimination noise algorithms as well as edge detection. The system is evaluated using the signs without including movement in their representation.

  4. Teaching artificial neural systems to drive: Manual training techniques for autonomous systems

    Science.gov (United States)

    Shepanski, J. F.; Macy, S. A.

    1987-01-01

    A methodology was developed for manually training autonomous control systems based on artificial neural systems (ANS). In applications where the rule set governing an expert's decisions is difficult to formulate, ANS can be used to extract rules by associating the information an expert receives with the actions taken. Properly constructed networks imitate rules of behavior that permits them to function autonomously when they are trained on the spanning set of possible situations. This training can be provided manually, either under the direct supervision of a system trainer, or indirectly using a background mode where the networks assimilates training data as the expert performs its day-to-day tasks. To demonstrate these methods, an ANS network was trained to drive a vehicle through simulated freeway traffic.

  5. Feasibility of Using Neural Network Models to Accelerate the Testing of Mechanical Systems

    Science.gov (United States)

    Fusaro, Robert L.

    1998-01-01

    Verification testing is an important aspect of the design process for mechanical mechanisms, and full-scale, full-length life testing is typically used to qualify any new component for use in space. However, as the required life specification is increased, full-length life tests become more costly and lengthen the development time. At the NASA Lewis Research Center, we theorized that neural network systems may be able to model the operation of a mechanical device. If so, the resulting neural network models could simulate long-term mechanical testing with data from a short-term test. This combination of computer modeling and short-term mechanical testing could then be used to verify the reliability of mechanical systems, thereby eliminating the costs associated with long-term testing. Neural network models could also enable designers to predict the performance of mechanisms at the conceptual design stage by entering the critical parameters as input and running the model to predict performance. The purpose of this study was to assess the potential of using neural networks to predict the performance and life of mechanical systems. To do this, we generated a neural network system to model wear obtained from three accelerated testing devices: 1) A pin-on-disk tribometer; 2) A line-contact rub-shoe tribometer; 3) A four-ball tribometer.

  6. Distributed neural system for emotional intelligence revealed by lesion mapping.

    Science.gov (United States)

    Barbey, Aron K; Colom, Roberto; Grafman, Jordan

    2014-03-01

    Cognitive neuroscience has made considerable progress in understanding the neural architecture of human intelligence, identifying a broadly distributed network of frontal and parietal regions that support goal-directed, intelligent behavior. However, the contributions of this network to social and emotional aspects of intellectual function remain to be well characterized. Here we investigated the neural basis of emotional intelligence in 152 patients with focal brain injuries using voxel-based lesion-symptom mapping. Latent variable modeling was applied to obtain measures of emotional intelligence, general intelligence and personality from the Mayer, Salovey, Caruso Emotional Intelligence Test (MSCEIT), the Wechsler Adult Intelligence Scale and the Neuroticism-Extroversion-Openness Inventory, respectively. Regression analyses revealed that latent scores for measures of general intelligence and personality reliably predicted latent scores for emotional intelligence. Lesion mapping results further indicated that these convergent processes depend on a shared network of frontal, temporal and parietal brain regions. The results support an integrative framework for understanding the architecture of executive, social and emotional processes and make specific recommendations for the interpretation and application of the MSCEIT to the study of emotional intelligence in health and disease.

  7. Distributed neural system for emotional intelligence revealed by lesion mapping

    Science.gov (United States)

    Colom, Roberto; Grafman, Jordan

    2014-01-01

    Cognitive neuroscience has made considerable progress in understanding the neural architecture of human intelligence, identifying a broadly distributed network of frontal and parietal regions that support goal-directed, intelligent behavior. However, the contributions of this network to social and emotional aspects of intellectual function remain to be well characterized. Here we investigated the neural basis of emotional intelligence in 152 patients with focal brain injuries using voxel-based lesion-symptom mapping. Latent variable modeling was applied to obtain measures of emotional intelligence, general intelligence and personality from the Mayer, Salovey, Caruso Emotional Intelligence Test (MSCEIT), the Wechsler Adult Intelligence Scale and the Neuroticism-Extroversion-Openness Inventory, respectively. Regression analyses revealed that latent scores for measures of general intelligence and personality reliably predicted latent scores for emotional intelligence. Lesion mapping results further indicated that these convergent processes depend on a shared network of frontal, temporal and parietal brain regions. The results support an integrative framework for understanding the architecture of executive, social and emotional processes and make specific recommendations for the interpretation and application of the MSCEIT to the study of emotional intelligence in health and disease. PMID:23171618

  8. WeAidU-a decision support system for myocardial perfusion images using artificial neural networks.

    Science.gov (United States)

    Ohlsson, Mattias

    2004-01-01

    This paper presents a computer-based decision support system for automated interpretation of diagnostic heart images (called WeAidU), which is made available via the Internet. The system is based on image processing techniques, artificial neural networks (ANNs) and large well-validated medical databases. We present results using artificial neural networks, and compare with two other classification methods, on a retrospective data set containing 1320 images from the clinical routine. The performance of the artificial neural networks detecting infarction and ischemia in different parts of the heart, measured as areas under the receiver operating characteristic curves, is in the range 0.83-0.96. These results indicate a high potential for the tool as a clinical decision support system.

  9. Modular Neural Networks and Type-2 Fuzzy Systems for Pattern Recognition

    CERN Document Server

    Melin, Patricia

    2012-01-01

    This book describes hybrid intelligent systems using type-2 fuzzy logic and modular neural networks for pattern recognition applications. Hybrid intelligent systems combine several intelligent computing paradigms, including fuzzy logic, neural networks, and bio-inspired optimization algorithms, which can be used to produce powerful pattern recognition systems. Type-2 fuzzy logic is an extension of traditional type-1 fuzzy logic that enables managing higher levels of uncertainty in complex real world problems, which are of particular importance in the area of pattern recognition. The book is organized in three main parts, each containing a group of chapters built around a similar subject. The first part consists of chapters with the main theme of theory and design algorithms, which are basically chapters that propose new models and concepts, which are the basis for achieving intelligent pattern recognition. The second part contains chapters with the main theme of using type-2 fuzzy models and modular neural ne...

  10. On-line identification of hybrid systems using an adaptive growing and pruning RBF neural network

    DEFF Research Database (Denmark)

    Alizadeh, Tohid

    2008-01-01

    This paper introduces an adaptive growing and pruning radial basis function (GAP-RBF) neural network for on-line identification of hybrid systems. The main idea is to identify a global nonlinear model that can predict the continuous outputs of hybrid systems. In the proposed approach, GAP-RBF neu...

  11. Absolute stability of nonlinear systems with time delays and applications to neural networks

    Directory of Open Access Journals (Sweden)

    Xinzhi Liu

    2001-01-01

    Full Text Available In this paper, absolute stability of nonlinear systems with time delays is investigated. Sufficient conditions on absolute stability are derived by using the comparison principle and differential inequalities. These conditions are simple and easy to check. In addition, exponential stability conditions for some special cases of nonlinear delay systems are discussed. Applications of those results to cellular neural networks are presented.

  12. Prediction of Phase Behavior in Microemulsion Systems Using Artificial Neural Networks

    Science.gov (United States)

    Richardson; Mbanefo; Aboofazeli; Lawrence; Barlow

    1997-03-15

    Preliminary investigations have been conducted to assess the potential for using (back-propagation, feed-forward) artificial neural networks to predict the phase behavior of quaternary microemulsion-forming systems, with a view to employing this type of methodology in the evaluation of novel cosurfactants for the formulation of pharmaceutically acceptable drug-delivery systems. The data employed in training the neural networks related to microemulsion systems containing lecithin, isopropyl myristate, and water, together with different types of cosurfactants, including short- and medium-chain alcohols, amines, acids, and ethylene glycol monoalkyl ethers. Previously unpublished phase diagrams are presented for four systems involving the cosurfactants 2-methyl-2-butanol, 2-methyl-1-propanol, 2-methyl-1-butanol, and isopropanol, which, along with eight other published sets of data, are used to test the predictive ability of the trained networks. The pseudo-ternary phase diagrams for these systems are predicted using only four computed physicochemical properties for the cosurfactants involved. The artificial neural networks are shown to be highly successful in predicting phase behavior for these systems, achieving mean success rates of 96.7 and 91.6% for training and test data, respectively. The conclusion is reached that artificial neural networks can provide useful tools for the development of microemulsion-based drug-delivery systems.

  13. Development of Novel Gas Brand Anti-Piracy System based on BP Neural Networks

    Energy Technology Data Exchange (ETDEWEB)

    Wang, L [School of Aeronautics and Astronautics, Tongji University, Shanghai (China); Zhang, Y Y [Chinese-German School of Postgraduate Studies, Tongji University (China); Ding, L [Chinese-German School of Postgraduate Studies, Tongji University (China)

    2006-10-15

    The Wireless-net Close-loop gas brand anti-piracy system introduced in this paper is a new type of brand piracy technical product based on BP neural network. It is composed by gas brand piracy label possessing gas exhalation resource, ARM embedded gas-detector, GPRS wireless module and data base of merchandise information. First, the system obtains the information on the special label through gas sensor array ,then the attained signals are transferred into ARM Embedded board and identified by artificial neural network, and finally turns back the outcome of data collection and identification to the manufactures with the help of GPRS module.

  14. Gain Scheduling Control of Nonlinear Systems Based on Neural State Space Models

    DEFF Research Database (Denmark)

    Bendtsen, Jan Dimon; Stoustrup, Jakob

    2003-01-01

    This paper presents a novel method for gain scheduling control of nonlinear systems based on extraction of local linear state space models from neural networks with direct application to robust control. A neural state space model of the system is first trained based on in- and output training sam...... control can be achieved by interpolating between each controller.In this paper, we propose to use the Youla-Jabr-Bongiorno-Kucera parameterization to achieve a smooth scheduling between the operating points with certain stability guarantees....

  15. Development of Novel Gas Brand Anti-Piracy System based on BP Neural Networks

    Science.gov (United States)

    Wang, L.; Zhang, Y. Y.; Ding, L.

    2006-10-01

    The Wireless-net Close-loop gas brand anti-piracy system introduced in this paper is a new type of brand piracy technical product based on BP neural network. It is composed by gas brand piracy label possessing gas exhalation resource, ARM embedded gas-detector, GPRS wireless module and data base of merchandise information. First, the system obtains the information on the special label through gas sensor array ,then the attained signals are transferred into ARM Embedded board and identified by artificial neural network, and finally turns back the outcome of data collection and identification to the manufactures with the help of GPRS module.

  16. Neural adaptive observer-based sensor and actuator fault detection in nonlinear systems: Application in UAV.

    Science.gov (United States)

    Abbaspour, Alireza; Aboutalebi, Payam; Yen, Kang K; Sargolzaei, Arman

    2017-03-01

    A new online detection strategy is developed to detect faults in sensors and actuators of unmanned aerial vehicle (UAV) systems. In this design, the weighting parameters of the Neural Network (NN) are updated by using the Extended Kalman Filter (EKF). Online adaptation of these weighting parameters helps to detect abrupt, intermittent, and incipient faults accurately. We apply the proposed fault detection system to a nonlinear dynamic model of the WVU YF-22 unmanned aircraft for its evaluation. The simulation results show that the new method has better performance in comparison with conventional recurrent neural network-based fault detection strategies. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

  17. Two inhibitory systems and CKIs regulate cell cycle exit of mammalian cardiomyocytes after birth

    Energy Technology Data Exchange (ETDEWEB)

    Tane, Shoji; Okayama, Hitomi; Ikenishi, Aiko; Amemiya, Yuki [School of Life Sciences, Faculty of Medicine, Tottori University, Yonago 683-8503 (Japan); Nakayama, Keiichi I. [Medical Institute of Bioregulation, Kyushu University, Fukuoka 812-8582 (Japan); Takeuchi, Takashi, E-mail: takeuchi@med.tottori-u.ac.jp [School of Life Sciences, Faculty of Medicine, Tottori University, Yonago 683-8503 (Japan)

    2015-10-16

    Mammalian cardiomyocytes actively proliferate during embryonic stages, following which they exit their cell cycle after birth, and the exit is maintained. Previously, we showed that two inhibitory systems (the G1-phase inhibitory system: repression of cyclin D1 expression; the M-phase inhibitory system: inhibition of CDK1 activation) maintain the cell cycle exit of mouse adult cardiomyocytes. We also showed that two CDK inhibitors (CKIs), p21{sup Cip1} and p27{sup Kip1}, regulate the cell cycle exit in a portion of postnatal cardiomyocytes. It remains unknown whether the two inhibitory systems are involved in the cell cycle exit of postnatal cardiomyocytes and whether p21{sup Cip1} and p27{sup Kip1} also inhibit entry to M-phase. Here, we showed that more than 40% of cardiomyocytes entered an additional cell cycle by induction of cyclin D1 expression at postnatal stages, but M-phase entry was inhibited in the majority of cardiomyocytes. Marked cell cycle progression and endoreplication were observed in cardiomyocytes of p21{sup Cip1} knockout mice at 4 weeks of age. In addition, tri- and tetranucleated cardiomyocytes increased significantly in p21{sup Cip1} knockout mice. These data showed that the G1-phase inhibitory system and two CKIs (p21{sup Cip1} and p27{sup Kip1}) inhibit entry to an additional cell cycle in postnatal cardiomyocytes, and that the M-phase inhibitory system and p21{sup Cip1} inhibit M-phase entry of cardiomyocytes which have entered the additional cell cycle. - Highlights: • Many postnatal cardiomyocytes entered an additional cell cycle by cyclin D1 induction. • The majority of cardiomyocytes could not enter M-phase after cyclin D1 induction. • Cell cycle progressed markedly in p21{sup Cip1} knockout mice after postnatal day 14. • Tri- and tetranucleated cardiomyocytes increased in p21{sup Cip1} knockout mice.

  18. Neural maps in insect versus vertebrate auditory systems.

    Science.gov (United States)

    Hildebrandt, K Jannis

    2014-02-01

    The convergent evolution of hearing in insects and vertebrates raises the question about similarity of the central representation of sound in these distant animal groups. Topographic representations of spectral, spatial and temporal cues have been widely described in mammals, but evidence for such maps is scarce in insects. Recent data on insect sound encoding provides evidence for an early integration of sound parameters to form highly-specific representation that predict behavioral output. In mammals, new studies investigating neural representation of perceptual features in behaving animals allow asking similar questions. A comparative approach may help in understanding principles underlying the formation of perceptual categories and behavioral plasticity. Copyright © 2013 Elsevier Ltd. All rights reserved.

  19. System Identification Using Multilayer Differential Neural Networks: A New Result

    Directory of Open Access Journals (Sweden)

    J. Humberto Pérez-Cruz

    2012-01-01

    Full Text Available In previous works, a learning law with a dead zone function was developed for multilayer differential neural networks. This scheme requires strictly a priori knowledge of an upper bound for the unmodeled dynamics. In this paper, the learning law is modified in such a way that this condition is relaxed. By this modification, the tuning process is simpler and the dead-zone function is not required anymore. On the basis of this modification and by using a Lyapunov-like analysis, a stronger result is here demonstrated: the exponential convergence of the identification error to a bounded zone. Besides, a value for upper bound of such zone is provided. The workability of this approach is tested by a simulation example.

  20. Neural systems for evaluating speaker (Un)believability.

    Science.gov (United States)

    Jiang, Xiaoming; Sanford, Ryan; Pell, Marc D

    2017-04-30

    Our voice provides salient cues about how confident we sound, which promotes inferences about how believable we are. However, the neural mechanisms involved in these social inferences are largely unknown. Employing functional magnetic resonance imaging, we examined the brain networks and individual differences underlying the evaluation of speaker believability from vocal expressions. Participants (n = 26) listened to statements produced in a confident, unconfident, or "prosodically unmarked" (neutral) voice, and judged how believable the speaker was on a 4-point scale. We found frontal-temporal networks were activated for different levels of confidence, with the left superior and inferior frontal gyrus more activated for confident statements, the right superior temporal gyrus for unconfident expressions, and bilateral cerebellum for statements in a neutral voice. Based on listener's believability judgment, we observed increased activation in the right superior parietal lobule (SPL) associated with higher believability, while increased left posterior central gyrus (PoCG) was associated with less believability. A psychophysiological interaction analysis found that the anterior cingulate cortex and bilateral caudate were connected to the right SPL when higher believability judgments were made, while supplementary motor area was connected with the left PoCG when lower believability judgments were made. Personal characteristics, such as interpersonal reactivity and the individual tendency to trust others, modulated the brain activations and the functional connectivity when making believability judgments. In sum, our data pinpoint neural mechanisms that are involved when inferring one's believability from a speaker's voice and establish ways that these mechanisms are modulated by individual characteristics of a listener. Hum Brain Mapp, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

  1. Comparison of the four proposed Apgar scoring systems in the assessment of birth asphyxia and adverse early neurologic outcomes.

    Science.gov (United States)

    Dalili, Hosein; Nili, Firouzeh; Sheikh, Mahdi; Hardani, Amir Kamal; Shariat, Mamak; Nayeri, Fatemeh

    2015-01-01

    To compare the Conventional, Specified, Expanded and Combined Apgar scoring systems in predicting birth asphyxia and the adverse early neurologic outcomes. This prospective cohort study was conducted on 464 admitted neonates. In the delivery room, after delivery the umbilical cord was double clamped and a blood samples was obtained from the umbilical artery for blood gas analysis, meanwhile on the 1- , 5- and 10- minutes Conventional, Specified, Expanded, and Combined Apgar scores were recorded. Then the neonates were followed and intracranial ultrasound imaging was performed, and the following information were recorded: the occurrence of birth asphyxia, hypoxic Ischemic Encephalopathy (HIE), intraventricular hemorrhage (IVH), and neonatal seizure. The Combined-Apgar score had the highest sensitivity (97%) and specificity (99%) in predicting birth asphyxia, followed by the Specified-Apgar score that was also highly sensitive (95%) and specific (97%). The Expanded-Apgar score was highly specific (95%) but not sensitive (67%) and the Conventional-Apgar score had the lowest sensitivity (81%) and low specificity (81%) in predicting birth asphyxia. When adjusted for gestational age, only the low 5-minute Combined-Apgar score was independently associated with the occurrence of HIE (B = 1.61, P = 0.02) and IVH (B = 2.8, P = 0.01). The newly proposed Combined-Apgar score is highly sensitive and specific in predicting birth asphyxia and also is a good predictor of the occurrence of HIE and IVH in asphyxiated neonates.

  2. RBF Neural Network of Sliding Mode Control for Time-Varying 2-DOF Parallel Manipulator System

    Directory of Open Access Journals (Sweden)

    Haizhong Chen

    2013-01-01

    Full Text Available This paper presents a radial basis function (RBF neural network control scheme for manipulators with actuator nonlinearities. The control scheme consists of a time-varying sliding mode control (TVSMC and an RBF neural network compensator. Since the actuator nonlinearities are usually included in the manipulator driving motor, a compensator using RBF network is proposed to estimate the actuator nonlinearities and their upper boundaries. Subsequently, an RBF neural network controller that requires neither the evaluation of off-line dynamical model nor the time-consuming training process is given. In addition, Barbalat Lemma is introduced to help prove the stability of the closed control system. Considering the SMC controller and the RBF network compensator as the whole control scheme, the closed-loop system is proved to be uniformly ultimately bounded. The whole scheme provides a general procedure to control the manipulators with actuator nonlinearities. Simulation results verify the effectiveness of the designed scheme and the theoretical discussion.

  3. High-dimensional neural-network potentials for multicomponent systems: Applications to zinc oxide

    Science.gov (United States)

    Artrith, Nongnuch; Morawietz, Tobias; Behler, Jörg

    2011-04-01

    Artificial neural networks represent an accurate and efficient tool to construct high-dimensional potential-energy surfaces based on first-principles data. However, so far the main drawback of this method has been the limitation to a single atomic species. We present a generalization to compounds of arbitrary chemical composition, which now enables simulations of a wide range of systems containing large numbers of atoms. The required incorporation of long-range interactions is achieved by combining the numerical accuracy of neural networks with an electrostatic term based on environment-dependent charges. Using zinc oxide as a benchmark system we show that the neural network potential-energy surface is in excellent agreement with density-functional theory reference calculations, while the evaluation is many orders of magnitude faster.

  4. Global neural dynamic surface tracking control of strict-feedback systems with application to hypersonic flight vehicle.

    Science.gov (United States)

    Xu, Bin; Yang, Chenguang; Pan, Yongping

    2015-10-01

    This paper studies both indirect and direct global neural control of strict-feedback systems in the presence of unknown dynamics, using the dynamic surface control (DSC) technique in a novel manner. A new switching mechanism is designed to combine an adaptive neural controller in the neural approximation domain, together with the robust controller that pulls the transient states back into the neural approximation domain from the outside. In comparison with the conventional control techniques, which could only achieve semiglobally uniformly ultimately bounded stability, the proposed control scheme guarantees all the signals in the closed-loop system are globally uniformly ultimately bounded, such that the conventional constraints on initial conditions of the neural control system can be relaxed. The simulation studies of hypersonic flight vehicle (HFV) are performed to demonstrate the effectiveness of the proposed global neural DSC design.

  5. Neural mirroring and social interaction: Motor system involvement during action observation relates to early peer cooperation

    Directory of Open Access Journals (Sweden)

    H.M. Endedijk

    2017-04-01

    Full Text Available Whether we hand over objects to someone, play a team sport, or make music together, social interaction often involves interpersonal action coordination, both during instances of cooperation and entrainment. Neural mirroring is thought to play a crucial role in processing other’s actions and is therefore considered important for social interaction. Still, to date, it is unknown whether interindividual differences in neural mirroring play a role in interpersonal coordination during different instances of social interaction. A relation between neural mirroring and interpersonal coordination has particularly relevant implications for early childhood, since successful early interaction with peers is predictive of a more favorable social development. We examined the relation between neural mirroring and children’s interpersonal coordination during peer interaction using EEG and longitudinal behavioral data. Results showed that 4-year-old children with higher levels of motor system involvement during action observation (as indicated by lower beta-power were more successful in early peer cooperation. This is the first evidence for a relation between motor system involvement during action observation and interpersonal coordination during other instances of social interaction. The findings suggest that interindividual differences in neural mirroring are related to interpersonal coordination and thus successful social interaction.

  6. Neural mirroring and social interaction: Motor system involvement during action observation relates to early peer cooperation.

    Science.gov (United States)

    Endedijk, H M; Meyer, M; Bekkering, H; Cillessen, A H N; Hunnius, S

    2017-04-01

    Whether we hand over objects to someone, play a team sport, or make music together, social interaction often involves interpersonal action coordination, both during instances of cooperation and entrainment. Neural mirroring is thought to play a crucial role in processing other's actions and is therefore considered important for social interaction. Still, to date, it is unknown whether interindividual differences in neural mirroring play a role in interpersonal coordination during different instances of social interaction. A relation between neural mirroring and interpersonal coordination has particularly relevant implications for early childhood, since successful early interaction with peers is predictive of a more favorable social development. We examined the relation between neural mirroring and children's interpersonal coordination during peer interaction using EEG and longitudinal behavioral data. Results showed that 4-year-old children with higher levels of motor system involvement during action observation (as indicated by lower beta-power) were more successful in early peer cooperation. This is the first evidence for a relation between motor system involvement during action observation and interpersonal coordination during other instances of social interaction. The findings suggest that interindividual differences in neural mirroring are related to interpersonal coordination and thus successful social interaction. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

  7. Biological neural networks as model systems for designing future parallel processing computers

    Science.gov (United States)

    Ross, Muriel D.

    1991-01-01

    One of the more interesting debates of the present day centers on whether human intelligence can be simulated by computer. The author works under the premise that neurons individually are not smart at all. Rather, they are physical units which are impinged upon continuously by other matter that influences the direction of voltage shifts across the units membranes. It is only the action of a great many neurons, billions in the case of the human nervous system, that intelligent behavior emerges. What is required to understand even the simplest neural system is painstaking analysis, bit by bit, of the architecture and the physiological functioning of its various parts. The biological neural network studied, the vestibular utricular and saccular maculas of the inner ear, are among the most simple of the mammalian neural networks to understand and model. While there is still a long way to go to understand even this most simple neural network in sufficient detail for extrapolation to computers and robots, a start was made. Moreover, the insights obtained and the technologies developed help advance the understanding of the more complex neural networks that underlie human intelligence.

  8. Selected Flight Test Results for Online Learning Neural Network-Based Flight Control System

    Science.gov (United States)

    Williams-Hayes, Peggy S.

    2004-01-01

    The NASA F-15 Intelligent Flight Control System project team developed a series of flight control concepts designed to demonstrate neural network-based adaptive controller benefits, with the objective to develop and flight-test control systems using neural network technology to optimize aircraft performance under nominal conditions and stabilize the aircraft under failure conditions. This report presents flight-test results for an adaptive controller using stability and control derivative values from an online learning neural network. A dynamic cell structure neural network is used in conjunction with a real-time parameter identification algorithm to estimate aerodynamic stability and control derivative increments to baseline aerodynamic derivatives in flight. This open-loop flight test set was performed in preparation for a future phase in which the learning neural network and parameter identification algorithm output would provide the flight controller with aerodynamic stability and control derivative updates in near real time. Two flight maneuvers are analyzed - pitch frequency sweep and automated flight-test maneuver designed to optimally excite the parameter identification algorithm in all axes. Frequency responses generated from flight data are compared to those obtained from nonlinear simulation runs. Flight data examination shows that addition of flight-identified aerodynamic derivative increments into the simulation improved aircraft pitch handling qualities.

  9. Genetic manipulation of specific neural circuits by use of a viral vector system.

    Science.gov (United States)

    Kobayashi, Kenta; Kato, Shigeki; Kobayashi, Kazuto

    2017-01-05

    To understand the mechanisms underlying higher brain functions, we need to analyze the roles of specific neuronal pathways or cell types forming the complex neural networks. In the neuroscience field, the transgenic approach has provided a useful gene engineering tool for experimental studies of neural functions. The conventional transgenic technique requires the appropriate promoter regions that drive a neuronal type-specific gene expression, but the promoter sequences specifically functioning in each neuronal type are limited. Previously, we developed novel types of lentiviral vectors showing high efficiency of retrograde gene transfer in the central nervous system, termed highly efficient retrograde gene transfer (HiRet) vector and neuron-specific retrograde gene transfer (NeuRet) vector. The HiRet and NeuRet vectors enable genetical manipulation of specific neural pathways in diverse model animals in combination with conditional cell targeting, synaptic transmission silencing, and gene expression systems. These newly developed vectors provide powerful experimental strategies to investigate, more precisely, the machineries exerting various neural functions. In this review, we give an outline of the HiRet and NeuRet vectors and describe recent representative applications of these viral vectors for studies on neural circuits.

  10. Lithofacies identification using multiple adaptive resonance theory neural networks and group decision expert system

    Science.gov (United States)

    Chang, H.-C.; Kopaska-Merkel, D. C.; Chen, H.-C.; Rocky, Durrans S.

    2000-01-01

    Lithofacies identification supplies qualitative information about rocks. Lithofacies represent rock textures and are important components of hydrocarbon reservoir description. Traditional techniques of lithofacies identification from core data are costly and different geologists may provide different interpretations. In this paper, we present a low-cost intelligent system consisting of three adaptive resonance theory neural networks and a rule-based expert system to consistently and objectively identify lithofacies from well-log data. The input data are altered into different forms representing different perspectives of observation of lithofacies. Each form of input is processed by a different adaptive resonance theory neural network. Among these three adaptive resonance theory neural networks, one neural network processes the raw continuous data, another processes categorial data, and the third processes fuzzy-set data. Outputs from these three networks are then combined by the expert system using fuzzy inference to determine to which facies the input data should be assigned. Rules are prioritized to emphasize the importance of firing order. This new approach combines the learning ability of neural networks, the adaptability of fuzzy logic, and the expertise of geologists to infer facies of the rocks. This approach is applied to the Appleton Field, an oil field located in Escambia County, Alabama. The hybrid intelligence system predicts lithofacies identity from log data with 87.6% accuracy. This prediction is more accurate than those of single adaptive resonance theory networks, 79.3%, 68.0% and 66.0%, using raw, fuzzy-set, and categorical data, respectively, and by an error-backpropagation neural network, 57.3%. (C) 2000 Published by Elsevier Science Ltd. All rights reserved.

  11. Networked neural spheroid by neuro-bundle mimicking nervous system created by topology effect.

    Science.gov (United States)

    Jeong, Gi Seok; Chang, Joon Young; Park, Ji Soo; Lee, Seung-A; Park, DoYeun; Woo, Junsung; An, Heeyoung; Lee, C Justin; Lee, Sang-Hoon

    2015-03-22

    In most animals, the nervous system consists of the central nervous system (CNS) and the peripheral nervous system (PNS), the latter of which connects the CNS to all parts of the body. Damage and/or malfunction of the nervous system causes serious pathologies, including neurodegenerative disorders, spinal cord injury, and Alzheimer's disease. Thus, not surprising, considerable research effort, both in vivo and in vitro, has been devoted to studying the nervous system and signal transmission through it. However, conventional in vitro cell culture systems do not enable control over diverse aspects of the neural microenvironment. Moreover, formation of certain nervous system growth patterns in vitro remains a challenge. In this study, we developed a deep hemispherical, microchannel-networked, concave array system and applied it to generate three-dimensional nerve-like neural bundles. The deep hemicylindrical channel network was easily fabricated by exploiting the meniscus induced by the surface tension of a liquid poly(dimethylsiloxane) (PDMS) prepolymer. Neurospheroids spontaneously aggregated in each deep concave microwell and were networked to neighboring spheroids through the deep hemicylindrical channel. Notably, two types of satellite spheroids also formed in deep hemispherical microchannels through self-aggregation and acted as an anchoring point to enhance formation of nerve-like networks with neighboring spheroids. During neural-network formation, neural progenitor cells successfully differentiated into glial and neuronal cells. These cells secreted laminin, forming an extracellular matrix around the host and satellite spheroids. Electrical stimuli were transmitted between networked neurospheroids in the resulting nerve-like neural bundle, as detected by imaging Ca(2+) signals in responding cells.

  12. Learning Efficiency of Consciousness System for Robot Using Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Osama Shoubaky

    2014-12-01

    Full Text Available This paper presents learning efficiency of a consciousness system for robot using artificial neural network. The proposed conscious system consists of reason system, feeling system and association system. The three systems are modeled using Module of Nerves for Advanced Dynamics (ModNAD. Artificial neural network of the type of supervised learning with the back propagation is used to train the ModNAD. The reason system imitates behaviour and represents self-condition and other-condition. The feeling system represents sensation and emotion. The association system represents behaviour of self and determines whether self is comfortable or not. A robot is asked to perform cognition and tasks using the consciousness system. Learning converges to about 0.01 within about 900 orders for imitation, pain, solitude and the association modules. It converges to about 0.01 within about 400 orders for the comfort and discomfort modules. It can be concluded that learning in the ModNAD completed after a relatively small number of times because the learning efficiency of the ModNAD artificial neural network is good. The results also show that each ModNAD has a function to imitate and cognize emotion. The consciousness system presented in this paper may be considered as a fundamental step for developing a robot having consciousness and feelings similar to humans.

  13. Comparison of five classification systems for interpreting electronic fetal monitoring in predicting neonatal status at birth.

    Science.gov (United States)

    Di Tommaso, Mariarosaria; Seravalli, Viola; Cordisco, Adalgisa; Consorti, Giada; Mecacci, Federico; Rizzello, Francesca

    2013-03-01

    To compare the accuracy of five different classification systems for interpreting electronic fetal monitoring (EFM) when predicting neonatal status at birth, as determined by the umbilical cord arterial pH. Ninety-seven cardiotocography traces were retrospectively interpreted according to five classification systems for EFM: Dublin Fetal Heart Rate Monitoring Trial (DFHRMT), Royal College of Obstetricians and Gynecologists (RCOG), Society of Obstetricians and Gynaecologists of Canada (SOGC), National Institute of Child Health and Human Development (NICHD) and Parer & Ikeda's. For each classification system, sensitivity, specificity, positive and negative predictive values were calculated. The capacity of the classifications to predict neonatal pH was also evaluated by receiver-operating characteristic (ROC) curves. Agreement between the five systems was estimated using weighted kappa statistic. Considering pH ≤7.15 as the cutoff for low pH, the sensitivity and specificity values were 100 and 18% (DFHRMT); 100 and 15% (RCOG); 88 and 37% (SOGC); 67 and 92% (NICHD); 55 and 67% (Parer & Ikeda). The ROC curves showed that all classifications analyzed had a low discriminative capacity when predicting umbilical artery pH ≤7.15. An excellent agreement was observed between DFHRMT and RCOG (weighted κ value: 0.860). Parer & Ikeda and NICHD classifications had the highest specificity in detecting umbilical cord arterial pH ≤7.15. The high specificity of the NICHD classification is hindered by a high percentage of "intermediate" traces (80%). Parer & Ikeda classification is the one that best classify as pathological only the traces of fetuses that are truly at risk of acidemia, thus avoiding unnecessary intervention. It also showed the best trade-off between sensitivity and specificity and the lowest rate of traces considered "intermediate."

  14. Modified Neural Network for Dynamic Control and Operation of a Hybrid Generation Systems

    Directory of Open Access Journals (Sweden)

    Cong-Hui Huang

    2014-12-01

    Full Text Available This paper presents modified neural network for dynamic control and operation of a hybrid generation systems. PV and wind power are the primary power sources of the system to take full advantages of renewable energy, and the diesel-engine is used as a backup system. The simulation model of the hybrid system was developed using MATLAB Simulink. To achieve a fast and stable response for the real power control, the intelligent controller consists of a Radial Basis Function Network (RBFN and an modified Elman Neural Network (ENN for maximum power point tracking (MPPT. The pitch angle of wind turbine is controlled by ENN, and the PV system uses RBFN, where the output signal is used to control the DC I DC boost converters to achieve the MPPT. And the results show the hybrid generation system can effectively extract the maximum power from the PV and wind energy sources.

  15. Engineering applications of fpgas chaotic systems, artificial neural networks, random number generators, and secure communication systems

    CERN Document Server

    Tlelo-Cuautle, Esteban; de la Fraga, Luis Gerardo

    2016-01-01

    This book offers readers a clear guide to implementing engineering applications with FPGAs, from the mathematical description to the hardware synthesis, including discussion of VHDL programming and co-simulation issues. Coverage includes FPGA realizations such as: chaos generators that are described from their mathematical models; artificial neural networks (ANNs) to predict chaotic time series, for which a discussion of different ANN topologies is included, with different learning techniques and activation functions; random number generators (RNGs) that are realized using different chaos generators, and discussions of their maximum Lyapunov exponent values and entropies. Finally, optimized chaotic oscillators are synchronized and realized to implement a secure communication system that processes black and white and grey-scale images. In each application, readers will find VHDL programming guidelines and computer arithmetic issues, along with co-simulation examples with Active-HDL and Simulink. Readers will b...

  16. How instructed knowledge modulates the neural systems of reward learning.

    Science.gov (United States)

    Li, Jian; Delgado, Mauricio R; Phelps, Elizabeth A

    2011-01-04

    Recent research in neuroeconomics has demonstrated that the reinforcement learning model of reward learning captures the patterns of both behavioral performance and neural responses during a range of economic decision-making tasks. However, this powerful theoretical model has its limits. Trial-and-error is only one of the means by which individuals can learn the value associated with different decision options. Humans have also developed efficient, symbolic means of communication for learning without the necessity for committing multiple errors across trials. In the present study, we observed that instructed knowledge of cue-reward probabilities improves behavioral performance and diminishes reinforcement learning-related blood-oxygen level-dependent (BOLD) responses to feedback in the nucleus accumbens, ventromedial prefrontal cortex, and hippocampal complex. The decrease in BOLD responses in these brain regions to reward-feedback signals was functionally correlated with activation of the dorsolateral prefrontal cortex (DLPFC). These results suggest that when learning action values, participants use the DLPFC to dynamically adjust outcome responses in valuation regions depending on the usefulness of action-outcome information.

  17. Animal Recognition System Based on Convolutional Neural Network

    Directory of Open Access Journals (Sweden)

    Tibor Trnovszky

    2017-01-01

    Full Text Available In this paper, the performances of well-known image recognition methods such as Principal Component Analysis (PCA, Linear Discriminant Analysis (LDA, Local Binary Patterns Histograms (LBPH and Support Vector Machine (SVM are tested and compared with proposed convolutional neural network (CNN for the recognition rate of the input animal images. In our experiments, the overall recognition accuracy of PCA, LDA, LBPH and SVM is demonstrated. Next, the time execution for animal recognition process is evaluated. The all experimental results on created animal database were conducted. This created animal database consist of 500 different subjects (5 classes/ 100 images for each class. The experimental result shows that the PCA features provide better results as LDA and LBPH for large training set. On the other hand, LBPH is better than PCA and LDA for small training data set. For proposed CNN we have obtained a recognition accuracy of 98%. The proposed method based on CNN outperforms the state of the art methods.

  18. Neural systemic impairment from whole-body vibration.

    Science.gov (United States)

    Yan, Ji-Geng; Zhang, Lin-ling; Agresti, Michael; LoGiudice, John; Sanger, James R; Matloub, Hani S; Havlik, Robert

    2015-05-01

    Insidious brain microinjury from motor vehicle-induced whole-body vibration (WBV) has not yet been investigated. For a long time we have believed that WBV would cause cumulative brain microinjury and impair cerebral function, which suggests an important risk factor for motor vehicle accidents and secondary cerebral vascular diseases. Fifty-six Sprague-Dawley rats were divided into seven groups (n = 8): 1) 2-week normal control group, 2) 2-week sham control group (restrained in the tube without vibration), 3) 2-week vibration group (exposed to whole-body vibration at 30 Hz and 0.5g acceleration for 4 hr/day, 5 days/week, for 2 weeks), 4) 4-week sham control group, 5) 4-week vibration group, 6) 8-week sham control group, and 7) 8-week vibration group. At the end point, all rats were evaluated in behavior, physiological, and brain histopathological studies. The cerebral injury from WBV is a cumulative process starting with vasospasm squeezing of the endothelial cells, followed by constriction of the cerebral arteries. After the 4-week vibration, brain neuron apoptosis started. After the 8-week vibration, vacuoles increased further in the brain arteries. Brain capillary walls thickened, mean neuron size was obviously reduced, neuron necrosis became prominent, and wide-ranging chronic cerebral edema was seen. These pathological findings are strongly correlated with neural functional impairments. © 2014 Wiley Periodicals, Inc.

  19. A New Method for Studying the Periodic System Based on a Kohonen Neural Network

    Science.gov (United States)

    Chen, David Zhekai

    2010-01-01

    A new method for studying the periodic system is described based on the combination of a Kohonen neural network and a set of chemical and physical properties. The classification results are directly shown in a two-dimensional map and easy to interpret. This is one of the major advantages of this approach over other methods reported in the…

  20. Artificial neural networks and support vector machine in banking computer systems

    Directory of Open Access Journals (Sweden)

    Jerzy Balicki

    2013-12-01

    Full Text Available In this paper, some artificial neural networks as well as a support vector machines have been studied due to bank computer system development. These approaches with the contact-less microprocessor technologies can upsurge the bank competitiveness by adding new functionalities. Moreover, some financial crisis influences can be declines.

  1. Neural Network Based Model of an Industrial Oil-Fired Boiler System ...

    African Journals Online (AJOL)

    In this study, an oil-fired boiler system is modeled as a multivariable plant with two inputs (feed water rate and oil-fired flow rate) and two outputs (steam temperature and pressure). The plant parameters are modeled using artificial neural network, based on experimental data collected directly from the physical plant.

  2. Image Classification System Based on Cortical Representations and Unsupervised Neural Network Learning

    NARCIS (Netherlands)

    Petkov, Nikolay

    1995-01-01

    A preprocessor based on a computational model of simple cells in the mammalian primary visual cortex is combined with a self-organising artificial neural network classifier. After learning with a sequence of input images, the output units of the system turn out to correspond to classes of input

  3. Comparable mechanisms for action and language: Neural systems behind intentions, goals and means

    NARCIS (Netherlands)

    Schie, H.T. van; Toni, I.; Bekkering, H.

    2006-01-01

    In this position paper we explore correspondence between neural systems for language and action starting from recent electrophysiological findings on the roles of posterior and frontal areas in goal-directed grasping actions. The paper compares the perceptual and motor organization for action and

  4. Comparison of different computer models of the neural control system of the lower urinary tract

    NARCIS (Netherlands)

    van Duin, F.; Rosier, P. F.; Bemelmans, B. L.; Wijkstra, H.; Debruyne, F. M.; van Oosterom, A.

    2000-01-01

    This paper presents a series of five models that were formulated for describing the neural control of the lower urinary tract in humans. A parsimonious formulation of the effect of the sympathetic system, the pre-optic area, and urethral afferents on the simulated behavior are included. In spite of

  5. A Drone Remote Sensing for Virtual Reality Simulation System for Forest Fires: Semantic Neural Network Approach

    Science.gov (United States)

    Narasimha Rao, Gudikandhula; Jagadeeswara Rao, Peddada; Duvvuru, Rajesh

    2016-09-01

    Wild fires have significant impact on atmosphere and lives. The demand of predicting exact fire area in forest may help fire management team by using drone as a robot. These are flexible, inexpensive and elevated-motion remote sensing systems that use drones as platforms are important for substantial data gaps and supplementing the capabilities of manned aircraft and satellite remote sensing systems. In addition, powerful computational tools are essential for predicting certain burned area in the duration of a forest fire. The reason of this study is to built up a smart system based on semantic neural networking for the forecast of burned areas. The usage of virtual reality simulator is used to support the instruction process of fire fighters and all users for saving of surrounded wild lives by using a naive method Semantic Neural Network System (SNNS). Semantics are valuable initially to have a enhanced representation of the burned area prediction and better alteration of simulation situation to the users. In meticulous, consequences obtained with geometric semantic neural networking is extensively superior to other methods. This learning suggests that deeper investigation of neural networking in the field of forest fires prediction could be productive.

  6. Predictive Control of Hydronic Floor Heating Systems using Neural Networks and Genetic Algorithms

    DEFF Research Database (Denmark)

    Vinther, Kasper; Green, Torben; Østergaard, Søren

    2017-01-01

    . Additionally, weather disturbances such as solar heat gain can be anticipated and compensated for, while taking into account the slow dynamics of the floor. Together with a genetic algorithm, they provide a way to search for optimal future set-point sequences, when convexity and continuity in the solution......This paper presents the use a neural network and a micro genetic algorithm to optimize future set-points in existing hydronic floor heating systems for improved energy efficiency. The neural network can be trained to predict the impact of changes in set-points on future room temperatures...

  7. Building an Early Warning System for Crude Oil Price Using Neural Network

    Directory of Open Access Journals (Sweden)

    Wonho Song

    2010-12-01

    Full Text Available In this paper, a crisis index for the oil price shock is defined and a neural network model is specified for the prediction of the crisis index. This paper contributes to the literature in three ways. First, we build an early warning system for crude oil price. Although the oil price became one of the most important price index recently, no research efforts have been made to build an early warning system for crude oil price. Second, the neural network (NN model is used to construct the early warning sysIn this paper, a crisis index for the oil price shock is defined and a neural network model is specified for the prediction of the crisis index. This paper contributes to the literature in three ways. First, we build an early warning system for crude oil price. Although the oil price became one of the most important price index recently, no research efforts have been made to build an early warning system for crude oil price. Second, the neural network (NN model is used to construct the early warning system. Most early warning systems are built based on the signaling approach. In this paper, we show that the neural network models are more flexible and have greater potential as EWS than the signaling approach. Third, we allow the multi-level crisis index. Previous models allowed only a zero/one crisis index whereas our model permits as many levels as possible. With this new model, we try to answer whether the oil price collapse following the historical peak in 2008 was predictable. We compare the results from the NN model with those from the ordered probit (OP model, and show that the oil price crisis and the following crash were predictable by the NN model, but not by the OP model.

  8. Exploratory spatial data analysis for the identification of risk factors to birth defects

    Directory of Open Access Journals (Sweden)

    Song Xinming

    2004-06-01

    Full Text Available Abstract Background Birth defects, which are the major cause of infant mortality and a leading cause of disability, refer to "Any anomaly, functional or structural, that presents in infancy or later in life and is caused by events preceding birth, whether inherited, or acquired (ICBDMS". However, the risk factors associated with heredity and/or environment are very difficult to filter out accurately. This study selected an area with the highest ratio of neural-tube birth defect (NTBD occurrences worldwide to identify the scale of environmental risk factors for birth defects using exploratory spatial data analysis methods. Methods By birth defect registers based on hospital records and investigation in villages, the number of birth defects cases within a four-year period was acquired and classified by organ system. The neural-tube birth defect ratio was calculated according to the number of births planned for each village in the study area, as the family planning policy is strictly adhered to in China. The Bayesian modeling method was used to estimate the ratio in order to remove the dependence of variance caused by different populations in each village. A recently developed statistical spatial method for detecting hotspots, Getis's 7, was used to detect the high-risk regions for neural-tube birth defects in the study area. Results After the Bayesian modeling method was used to calculate the ratio of neural-tube birth defects occurrences, Getis's statistics method was used in different distance scales. Two typical clustering phenomena were present in the study area. One was related to socioeconomic activities, and the other was related to soil type distributions. Conclusion The fact that there were two typical hotspot clustering phenomena provides evidence that the risk for neural-tube birth defect exists on two different scales (a socioeconomic scale at 6.84 km and a soil type scale at 22.8 km for the area studied. Although our study has limited

  9. A neural network approach to fault detection in spacecraft attitude determination and control systems

    Science.gov (United States)

    Schreiner, John N.

    This thesis proposes a method of performing fault detection and isolation in spacecraft attitude determination and control systems. The proposed method works by deploying a trained neural network to analyze a set of residuals that are defined such that they encompass the attitude control, guidance, and attitude determination subsystems. Eight neural networks were trained using either the resilient backpropagation, Levenberg-Marquardt, or Levenberg-Marquardt with Bayesian regularization training algorithms. The results of each of the neural networks were analyzed to determine the accuracy of the networks with respect to isolating the faulty component or faulty subsystem within the ADCS. The performance of the proposed neural network-based fault detection and isolation method was compared and contrasted with other ADCS FDI methods. The results obtained via simulation showed that the best neural networks employing this method successfully detected the presence of a fault 79% of the time. The faulty subsystem was successfully isolated 75% of the time and the faulty components within the faulty subsystem were isolated 37% of the time.

  10. Time Series Forecasting of Daily Reference Evapotranspiration by Neural Network Ensemble Learning for Irrigation System

    Science.gov (United States)

    Manikumari, N.; Murugappan, A.; Vinodhini, G.

    2017-07-01

    Time series forecasting has gained remarkable interest of researchers in the last few decades. Neural networks based time series forecasting have been employed in various application areas. Reference Evapotranspiration (ETO) is one of the most important components of the hydrologic cycle and its precise assessment is vital in water balance and crop yield estimation, water resources system design and management. This work aimed at achieving accurate time series forecast of ETO using a combination of neural network approaches. This work was carried out using data collected in the command area of VEERANAM Tank during the period 2004 – 2014 in India. In this work, the Neural Network (NN) models were combined by ensemble learning in order to improve the accuracy for forecasting Daily ETO (for the year 2015). Bagged Neural Network (Bagged-NN) and Boosted Neural Network (Boosted-NN) ensemble learning were employed. It has been proved that Bagged-NN and Boosted-NN ensemble models are better than individual NN models in terms of accuracy. Among the ensemble models, Boosted-NN reduces the forecasting errors compared to Bagged-NN and individual NNs. Regression co-efficient, Mean Absolute Deviation, Mean Absolute Percentage error and Root Mean Square Error also ascertain that Boosted-NN lead to improved ETO forecasting performance.

  11. Neural Fuzzy Inference System-Based Weather Prediction Model and Its Precipitation Predicting Experiment

    Directory of Open Access Journals (Sweden)

    Jing Lu

    2014-11-01

    Full Text Available We propose a weather prediction model in this article based on neural network and fuzzy inference system (NFIS-WPM, and then apply it to predict daily fuzzy precipitation given meteorological premises for testing. The model consists of two parts: the first part is the “fuzzy rule-based neural network”, which simulates sequential relations among fuzzy sets using artificial neural network; and the second part is the “neural fuzzy inference system”, which is based on the first part, but could learn new fuzzy rules from the previous ones according to the algorithm we proposed. NFIS-WPM (High Pro and NFIS-WPM (Ave are improved versions of this model. It is well known that the need for accurate weather prediction is apparent when considering the benefits. However, the excessive pursuit of accuracy in weather prediction makes some of the “accurate” prediction results meaningless and the numerical prediction model is often complex and time-consuming. By adapting this novel model to a precipitation prediction problem, we make the predicted outcomes of precipitation more accurate and the prediction methods simpler than by using the complex numerical forecasting model that would occupy large computation resources, be time-consuming and which has a low predictive accuracy rate. Accordingly, we achieve more accurate predictive precipitation results than by using traditional artificial neural networks that have low predictive accuracy.

  12. A configurable realtime DWT-based neural data compression and communication VLSI system for wireless implants.

    Science.gov (United States)

    Yang, Yuning; Kamboh, Awais M; Mason, Andrew J

    2014-04-30

    This paper presents the design of a complete multi-channel neural recording compression and communication system for wireless implants that addresses the challenging simultaneous requirements for low power, high bandwidth and error-free communication. The compression engine implements discrete wavelet transform (DWT) and run length encoding schemes and offers a practical data compression solution that faithfully preserves neural information. The communication engine encodes data and commands separately into custom-designed packet structures utilizing a protocol capable of error handling. VLSI hardware implementation of these functions, within the design constraints of a 32-channel neural compression implant, is presented. Designed in 0.13μm CMOS, the core of the neural compression and communication chip occupies only 1.21mm(2) and consumes 800μW of power (25μW per channel at 26KS/s) demonstrating an effective solution for intra-cortical neural interfaces. Copyright © 2014 Elsevier B.V. All rights reserved.

  13. Different in vitro culture systems affect the birth weight of lambs from vitrified ovine embryos.

    Science.gov (United States)

    Mara, L; Sanna, D; Dattena, M; Mayorga Muñoz, I M

    2015-02-01

    It has been reported that different in vitro culture systems affect the birth weight of lambs. The aim of this study was to test body weight and lambing rate of lambs born from five different in vitro culture systems after vitrification. Oocytes of Sarda sheep were matured in TCM-199 plus 0.4% bovine serum albumin (BSA) using systems: (i) 4 mg/ml fatty acid-free BSA (BSA4); (ii) 8 mg/ml fatty acid-free BSA (BSA8); (iii) BSA8-hyaluronan (BSA8-HA); (iv) BSA8-charcoal-stripped FBS (BSA8-CH); or (v) with 10% fetal bovine serum (FBS; serum) and fertilized with fresh semen. The presumptive zygotes were cultured up to the blastocyst stage with BSA8, BSA8-HA, BSA8-CH or serum or BSA4. In the third and fifth days of culture 5% charcoal-stripped FBS was added into BSA8-CH and serum, while 8 mg/ml or 4 mg/ml fatty acid-free BSA was added as BSA8, BSA8-HA and BSA4 respectively; 6 mg/ml HA was added to BSA8-HA. In total, 240 vitrified blastocysts were transferred into synchronized ewes. The lambing rate was not significant different between BSA groups or between serum groups (BSA8-CH and serum), while serum groups showed significant lower values when compared with BSA groups. Only BSA8 groups produced heavy lambs (≥4.5 kg) with a significant difference between BSA4 and BSA8 groups (P < 0.05).

  14. Design and Implementation of Behavior Recognition System Based on Convolutional Neural Network

    Directory of Open Access Journals (Sweden)

    Yu Bo

    2017-01-01

    Full Text Available We build a set of human behavior recognition system based on the convolution neural network constructed for the specific human behavior in public places. Firstly, video of human behavior data set will be segmented into images, then we process the images by the method of background subtraction to extract moving foreground characters of body. Secondly, the training data sets are trained into the designed convolution neural network, and the depth learning network is constructed by stochastic gradient descent. Finally, the various behaviors of samples are classified and identified with the obtained network model, and the recognition results are compared with the current mainstream methods. The result show that the convolution neural network can study human behavior model automatically and identify human’s behaviors without any manually annotated trainings.

  15. Targeted drug delivery system to neural cells utilizes the nicotinic acetylcholine receptor.

    Science.gov (United States)

    Huey, Rachel; O'Hagan, Barry; McCarron, Paul; Hawthorne, Susan

    2017-06-15

    Drug delivery to the brain is still a major challenge in the field of therapeutics, especially for large and hydrophilic compounds. In order to achieve drug delivery of therapeutic concentration in the central nervous system, the problematic blood brain barrier (BBB) must be overcome. This work presents the formulation of a targeted nanoparticle-based drug delivery system using a specific neural cell targeting ligand, rabies virus derived peptide (RDP). Characterization studies revealed that RDP could be conjugated to drug-loaded PLGA nanoparticles of average diameter 257.10±22.39nm and zeta potential of -5.51±0.73mV. In vitro studies showed that addition of RDP to nanoparticles enhanced drug accumulation in a neural cell line specifically as opposed to non-neural cell lines. It was revealed that this drug delivery system is reliant upon nicotinic acetylcholine receptor (nAChR) function for RDP-facilitated effects, supporting a cellular uptake mechanism of action. The specific neural cell targeting capabilities of RDP via the nAChR offers a non-toxic, non-invasive and promising approach to the delivery of therapeutics to the brain. Crown Copyright © 2017. Published by Elsevier B.V. All rights reserved.

  16. CITY TRANSPORT SYSTEM ECOLOGICAL STATE FORECASTING WITH THE USE OF NEURAL NETWORKS

    Directory of Open Access Journals (Sweden)

    Andrey Lyamzin

    2017-11-01

    Full Text Available Purpose: The purpose of this work is to develop an effective model for city transport system ecological state assessment using neural networks general concept. Methods: The proposed model is based on two neural networks work, taking into account the traffic density effect and the transit capacity level on urban areas. Results: Based on the synthesis of the fuzzy sets theory and neural networks basic principles, the city transport system ecological state assessing model is developed. The graphical representation of the model is given. A forecast reliability high degree is provided even at low learning rates and high dynamics of changing statistical data in the city transit traffic conditions. Conclusions: The use of fuzzy neural networks makes it possible to state a complete correspondence between fuzzy inference procedure mathematical representation and the urban transport system structure. The proposed model allows to formulate well-defined environmental guidelines when making decisions in the transit traffic field, taking into account the interests of enterprises, transport and the urban population, with the subsequent distribution of traffic flows in time and geographical space of the city industrial areas.

  17. Stochastic Oscillation in Self-Organized Critical States of Small Systems: Sensitive Resting State in Neural Systems

    Science.gov (United States)

    Wang, Sheng-Jun; Ouyang, Guang; Guang, Jing; Zhang, Mingsha; Wong, K. Y. Michael; Zhou, Changsong

    2016-01-01

    Self-organized critical states (SOCs) and stochastic oscillations (SOs) are simultaneously observed in neural systems, which appears to be theoretically contradictory since SOCs are characterized by scale-free avalanche sizes but oscillations indicate typical scales. Here, we show that SOs can emerge in SOCs of small size systems due to temporal correlation between large avalanches at the finite-size cutoff, resulting from the accumulation-release process in SOCs. In contrast, the critical branching process without accumulation-release dynamics cannot exhibit oscillations. The reconciliation of SOCs and SOs is demonstrated both in the sandpile model and robustly in biologically plausible neuronal networks. The oscillations can be suppressed if external inputs eliminate the prominent slow accumulation process, providing a potential explanation of the widely studied Berger effect or event-related desynchronization in neural response. The features of neural oscillations and suppression are confirmed during task processing in monkey eye-movement experiments. Our results suggest that finite-size, columnar neural circuits may play an important role in generating neural oscillations around the critical states, potentially enabling functional advantages of both SOCs and oscillations for sensitive response to transient stimuli.

  18. An Intelligent Active Video Surveillance System Based on the Integration of Virtual Neural Sensors and BDI Agents

    Science.gov (United States)

    Gregorio, Massimo De

    In this paper we present an intelligent active video surveillance system currently adopted in two different application domains: railway tunnels and outdoor storage areas. The system takes advantages of the integration of Artificial Neural Networks (ANN) and symbolic Artificial Intelligence (AI). This hybrid system is formed by virtual neural sensors (implemented as WiSARD-like systems) and BDI agents. The coupling of virtual neural sensors with symbolic reasoning for interpreting their outputs, makes this approach both very light from a computational and hardware point of view, and rather robust in performances. The system works on different scenarios and in difficult light conditions.

  19. Birth Control

    Science.gov (United States)

    ... to have sex makes sense Talking to your parents about sex Deciding about sex Birth control Types of birth ... not planned. Some young people are afraid their parents will find out they’re having sex. If you get birth control from a doctor, ...

  20. DEVELOPMENT OF A COMPUTER SYSTEM FOR IDENTITY AUTHENTICATION USING ARTIFICIAL NEURAL NETWORKS

    Directory of Open Access Journals (Sweden)

    Timur Kartbayev

    2017-03-01

    Full Text Available The aim of the study is to increase the effectiveness of automated face recognition to authenticate identity, considering features of change of the face parameters over time. The improvement of the recognition accuracy, as well as consideration of the features of temporal changes in a human face can be based on the methodology of artificial neural networks. Hybrid neural networks, combining the advantages of classical neural networks and fuzzy logic systems, allow using the network learnability along with the explanation of the findings. The structural scheme of intelligent system for identification based on artificial neural networks is proposed in this work. It realizes the principles of digital information processing and identity recognition taking into account the forecast of key characteristics’ changes over time (e.g., due to aging. The structural scheme has a three-tier architecture and implements preliminary processing, recognition and identification of images obtained as a result of monitoring. On the basis of expert knowledge, the fuzzy base of products is designed. It allows assessing possible changes in key characteristics, used to authenticate identity based on the image. To take this possibility into consideration, a neuro-fuzzy network of ANFIS type was used, which implements the algorithm of Tagaki-Sugeno. The conducted experiments showed high efficiency of the developed neural network and a low value of learning errors, which allows recommending this approach for practical implementation. Application of the developed system of fuzzy production rules that allow predicting changes in individuals over time, will improve the recognition accuracy, reduce the number of authentication failures and improve the efficiency of information processing and decision-making in applications, such as authentication of bank customers, users of mobile applications, or in video monitoring systems of sensitive sites.

  1. A four-channel microelectronic system for neural signal regeneration

    Energy Technology Data Exchange (ETDEWEB)

    Xie Shushan; Wang Zhigong; Li Wenyuan [Institute of RF- and OE-ICs, Southeast University, Nanjing 210096 (China); Lue Xiaoying; Pan Haixian, E-mail: zgwang@seu.edu.c [State Key Laboratory of Bio-Electronics, Southeast University, Nanjing 210096 (China)

    2009-12-15

    This paper presents a microelectronic system which is capable of making a signal record and functional electric stimulation of an injured spinal cord. As a requirement of implantable engineering for the regeneration microelectronic system, the system is of low noise, low power, small size and high performance. A front-end circuit and two high performance OPAs (operational amplifiers) have been designed for the system with different functions, and the two OPAs are a low-noise low-power two-stage OPA and a constant-g{sub m} RTR input and output OPA. The system has been realized in CSMC 0.5-{mu}m CMOS technology. The test results show that the system satisfies the demands of neuron signal regeneration. (semiconductor integrated circuits)

  2. Prediction of a model enzymatic acidolysis system using neural networks

    Directory of Open Access Journals (Sweden)

    Güven, Aytaç

    2008-12-01

    Full Text Available A model for the acidolysis of trinolein and palmitic acid under the catalysis of immobilized sn-1,3 specific lipase was presented in this study. A neural networks (NN based model was developed for the prediction of the concentrations of the major reaction products of this reaction (1-palmitoyl-2,3-oleoyl-glycerol (POO 1,3-dipalmitoyl-2-oleoyl-glycerol (POP and triolein (OOO. Substrate ratio (SR, reaction temperature (T and reaction time (t were used as input parameters. The optimal architecture of the proposed NN model, which consists of one input layer with three inputs, one hidden layer with seven neurons and one output layer with three outputs, wass able to predict the reaction products concentration with a mean square error (MSE of less than 1.5 and R2 of 0.999. and explicit formulation of the proposed NN is presented. Considerable good performance is achieved in modeling the acidolysis reaction using neuronal networks.En este estudio se presenta un modelo para la acidólisis de la trilinoleina y el ácido palmítico mediante la catálisis con una lipasa específica sn-1,3 inmovilizada. Un modelo basado en redes neuronales (NN ha sido desarrollado para la predicción de la concentración de los principales productos de esta reacción (1-palmitoil-2,3-oleoil-glicerol (POO, 1,3-dipalmitoil-2-oleoil-glicerol (POP y trioleina (OOO. Se han usado como parámetros de entrada: la proporción del sustrato (SR, la temperatura de reacción (T y el tiempo de reacción (t. La arquitectura óptima del modelo de NN propuesto, que consiste en una capa de entrada con tres entradas, una capa oculta con siete neuronas y una capa de salida con tres salidas, fue capaz de predecir la concentración de los productos de reacción con un error cuadrático medio (MSE de menos de 1.5 y una R2 de 0.999 . Se presenta una formulación explícita del modelo NN propuesto. Se obtienen muy buenos resultados en la predicción de la reacciones de acidólisis mediante el uso de

  3. Robust Fault Detection of Wind Energy Conversion Systems Based on Dynamic Neural Networks

    Directory of Open Access Journals (Sweden)

    Nasser Talebi

    2014-01-01

    Full Text Available Occurrence of faults in wind energy conversion systems (WECSs is inevitable. In order to detect the occurred faults at the appropriate time, avoid heavy economic losses, ensure safe system operation, prevent damage to adjacent relevant systems, and facilitate timely repair of failed components; a fault detection system (FDS is required. Recurrent neural networks (RNNs have gained a noticeable position in FDSs and they have been widely used for modeling of complex dynamical systems. One method for designing an FDS is to prepare a dynamic neural model emulating the normal system behavior. By comparing the outputs of the real system and neural model, incidence of the faults can be identified. In this paper, by utilizing a comprehensive dynamic model which contains both mechanical and electrical components of the WECS, an FDS is suggested using dynamic RNNs. The presented FDS detects faults of the generator's angular velocity sensor, pitch angle sensors, and pitch actuators. Robustness of the FDS is achieved by employing an adaptive threshold. Simulation results show that the proposed scheme is capable to detect the faults shortly and it has very low false and missed alarms rate.

  4. Robust fault detection of wind energy conversion systems based on dynamic neural networks.

    Science.gov (United States)

    Talebi, Nasser; Sadrnia, Mohammad Ali; Darabi, Ahmad

    2014-01-01

    Occurrence of faults in wind energy conversion systems (WECSs) is inevitable. In order to detect the occurred faults at the appropriate time, avoid heavy economic losses, ensure safe system operation, prevent damage to adjacent relevant systems, and facilitate timely repair of failed components; a fault detection system (FDS) is required. Recurrent neural networks (RNNs) have gained a noticeable position in FDSs and they have been widely used for modeling of complex dynamical systems. One method for designing an FDS is to prepare a dynamic neural model emulating the normal system behavior. By comparing the outputs of the real system and neural model, incidence of the faults can be identified. In this paper, by utilizing a comprehensive dynamic model which contains both mechanical and electrical components of the WECS, an FDS is suggested using dynamic RNNs. The presented FDS detects faults of the generator's angular velocity sensor, pitch angle sensors, and pitch actuators. Robustness of the FDS is achieved by employing an adaptive threshold. Simulation results show that the proposed scheme is capable to detect the faults shortly and it has very low false and missed alarms rate.

  5. Constrained adaptive neural network control of an MIMO aeroelastic system with input nonlinearities

    Directory of Open Access Journals (Sweden)

    Yiyong Gou

    2017-04-01

    Full Text Available A constrained adaptive neural network control scheme is proposed for a multi-input and multi-output (MIMO aeroelastic system in the presence of wind gust, system uncertainties, and input nonlinearities consisting of input saturation and dead-zone. In regard to the input nonlinearities, the right inverse function block of the dead-zone is added before the input nonlinearities, which simplifies the input nonlinearities into an equivalent input saturation. To deal with the equivalent input saturation, an auxiliary error system is designed to compensate for the impact of the input saturation. Meanwhile, uncertainties in pitch stiffness, plunge stiffness, and pitch damping are all considered, and radial basis function neural networks (RBFNNs are applied to approximate the system uncertainties. In combination with the designed auxiliary error system and the backstepping control technique, a constrained adaptive neural network controller is designed, and it is proven that all the signals in the closed-loop system are semi-globally uniformly bounded via the Lyapunov stability analysis method. Finally, extensive digital simulation results demonstrate the effectiveness of the proposed control scheme towards flutter suppression in spite of the integrated effects of wind gust, system uncertainties, and input nonlinearities.

  6. Command Filtered Adaptive Fuzzy Neural Network Backstepping Control for Marine Power System

    Directory of Open Access Journals (Sweden)

    Xin Zhang

    2014-01-01

    Full Text Available In order to retrain chaotic oscillation of marine power system which is excited by periodic electromagnetism perturbation, a novel command-filtered adaptive fuzzy neural network backstepping control method is designed. First, the mathematical model of marine power system is established based on the two parallel nonlinear model. Then, main results of command-filtered adaptive fuzzy neural network backstepping control law are given. And the Lyapunov stability theory is applied to prove that the system can remain closed-loop asymptotically stable with this controller. Finally, simulation results indicate that the designed controller can suppress chaotic oscillation with fast convergence speed that makes the system return to the equilibrium point quickly; meanwhile, the parameter which induces chaotic oscillation can also be discriminated.

  7. Silicon synaptic transistor for hardware-based spiking neural network and neuromorphic system

    Science.gov (United States)

    Kim, Hyungjin; Hwang, Sungmin; Park, Jungjin; Park, Byung-Gook

    2017-10-01

    Brain-inspired neuromorphic systems have attracted much attention as new computing paradigms for power-efficient computation. Here, we report a silicon synaptic transistor with two electrically independent gates to realize a hardware-based neural network system without any switching components. The spike-timing dependent plasticity characteristics of the synaptic devices are measured and analyzed. With the help of the device model based on the measured data, the pattern recognition capability of the hardware-based spiking neural network systems is demonstrated using the modified national institute of standards and technology handwritten dataset. By comparing systems with and without inhibitory synapse part, it is confirmed that the inhibitory synapse part is an essential element in obtaining effective and high pattern classification capability.

  8. Neural network L1 adaptive control of MIMO systems with nonlinear uncertainty.

    Science.gov (United States)

    Zhen, Hong-tao; Qi, Xiao-hui; Li, Jie; Tian, Qing-min

    2014-01-01

    An indirect adaptive controller is developed for a class of multiple-input multiple-output (MIMO) nonlinear systems with unknown uncertainties. This control system is comprised of an L 1 adaptive controller and an auxiliary neural network (NN) compensation controller. The L 1 adaptive controller has guaranteed transient response in addition to stable tracking. In this architecture, a low-pass filter is adopted to guarantee fast adaptive rate without generating high-frequency oscillations in control signals. The auxiliary compensation controller is designed to approximate the unknown nonlinear functions by MIMO RBF neural networks to suppress the influence of uncertainties. NN weights are tuned on-line with no prior training and the project operator ensures the weights bounded. The global stability of the closed-system is derived based on the Lyapunov function. Numerical simulations of an MIMO system coupled with nonlinear uncertainties are used to illustrate the practical potential of our theoretical results.

  9. Adaptive Neural Tracking Control for Discrete-Time Switched Nonlinear Systems with Dead Zone Inputs

    Directory of Open Access Journals (Sweden)

    Jidong Wang

    2017-01-01

    Full Text Available In this paper, the adaptive neural controllers of subsystems are proposed for a class of discrete-time switched nonlinear systems with dead zone inputs under arbitrary switching signals. Due to the complicated framework of the discrete-time switched nonlinear systems and the existence of the dead zone, it brings about difficulties for controlling such a class of systems. In addition, the radial basis function neural networks are employed to approximate the unknown terms of each subsystem. Switched update laws are designed while the parameter estimation is invariable until its corresponding subsystem is active. Then, the closed-loop system is stable and all the signals are bounded. Finally, to illustrate the effectiveness of the proposed method, an example is employed.

  10. Using the Society for Assisted Reproductive Technology Clinic Outcome System morphological measures to predict live birth after assisted reproductive technology.

    Science.gov (United States)

    Luke, Barbara; Brown, Morton B; Stern, Judy E; Jindal, Sangita K; Racowsky, Catherine; Ball, G David

    2014-11-01

    To model morphological assessments of embryo quality that are predictive of live birth. Longitudinal cohort using cycles reported in the Society for Assisted Reproductive Technology Clinic Outcomes Reporting System (SART CORS) between 2007 and 2011. Clinic-based data. Fresh autologous assisted reproductive technology (ART) cycles with ETs on day 3 or day 5 and morphological assessments reported (25,409 cycles with one embryo transferred and 96,093 cycles with two embryos transferred). Live-birth rates were modeled by morphological assessments using backward-stepping logistic regression for cycle 1 and over five cycles, separately for day 3 and day 5 transfers and number of embryos transferred (1 or 2). Additional models for each day of transfer also included the number of oocytes retrieved and the number of embryos cryopreserved. None. Live births. Morphological assessments of grade, stage, fragmentation, and symmetry were significant for the day 3 models; grade, stage, and trophectoderm were significant in the day 5 model; inner-cell mass was significant in the models when two embryos were transferred. Number of oocytes retrieved and number of embryos cryopreserved were significant for both day 3 and day 5 models. These findings confirm the significant association between embryo quality parameters reported to SART CORS and live-birth rate after ART. Copyright © 2014 American Society for Reproductive Medicine. Published by Elsevier Inc. All rights reserved.

  11. School children with low birth weight inserted in system of Embu's education: construction of sentences.

    Science.gov (United States)

    Pessoa, Rebeca Rodrigues; Isotani, Selma Mie; Perissinoto, Jacy; Puccini, Rosana Fiorini

    2014-01-01

    To characterize the construction of sentences in schoolchildren born with low weight. We selected 413 students from Embu das Artes (SP), Brazil. Application of Recreating Speech Acts of Test of Language Competence sub-test. We analyzed the number of words and the holistic score. Age group of 6-10 years old, female/male, with low birth weight (<2,500 g) in Study Group (SG; n=238), and birth weight above or equal to 2,500 g composing the Control Group (CG; n=175). Children with anomalies were excluded. The both groups' responses were considered as well as the association of the responses with variables birth weight, gender, age of child and maternal age and education, through Student's t-test, χ2test and linear regression. The SG scored less on the total number of words and had worst performance in items that involved prepositions with a sense of temporality and place, with adverb functions. There was no difference between groups regarding the holistic score. It was found positive impact of the variables birth weight (p=0.002), age of child, age of mother and maternal education on standard test scores in both groups. The SG had fewer words compared to CG. The higher the birth weight, the higher the score test pattern. It was evident the age-related changes in morpho-syntactic skills addressed in the study, and protection factors mother's schooling and age had a positive impact on language performance.

  12. Review of Data Preprocessing Methods for Sign Language Recognition Systems based on Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Zorins Aleksejs

    2016-12-01

    Full Text Available The article presents an introductory analysis of relevant research topic for Latvian deaf society, which is the development of the Latvian Sign Language Recognition System. More specifically the data preprocessing methods are discussed in the paper and several approaches are shown with a focus on systems based on artificial neural networks, which are one of the most successful solutions for sign language recognition task.

  13. Central Neural Control of the Cardiovascular System: Current Perspectives

    Science.gov (United States)

    Dampney, Roger A. L.

    2016-01-01

    This brief review, which is based on a lecture presented at the American Physiological Society Teaching Refresher Course on the Brain and Systems Control as part of the Experimental Biology meeting in 2015, aims to summarize current concepts of the principal mechanisms in the brain that regulate the autonomic outflow to the cardiovascular system.…

  14. Plasticity and Neural Stem Cells in the Enteric Nervous System

    NARCIS (Netherlands)

    Schaefer, Karl-Herbert; Van Ginneken, Chris; Copray, Sjef

    2009-01-01

    The enteric nervous system (ENS) is a highly organized part of the autonomic nervous system, which innervates the whole gastrointestinal tract by several interconnected neuronal networks. The ENS changes during development and keeps throughout its lifespan a significant capacity to adapt to

  15. Point-and-Click Cursor Control With an Intracortical Neural Interface System by Humans With Tetraplegia

    Science.gov (United States)

    Kim, Sung-Phil; Simeral, John D.; Hochberg, Leigh R.; Donoghue, John P.; Friehs, Gerhard M.; Black, Michael J.

    2012-01-01

    We present a point-and-click intracortical neural interface system (NIS) that enables humans with tetraplegia to volitionally move a 2-D computer cursor in any desired direction on a computer screen, hold it still, and click on the area of interest. This direct brain–computer interface extracts both discrete (click) and continuous (cursor velocity) signals from a single small population of neurons in human motor cortex. A key component of this system is a multi-state probabilistic decoding algorithm that simultaneously decodes neural spiking activity of a small population of neurons and outputs either a click signal or the velocity of the cursor. The algorithm combines a linear classifier, which determines whether the user is intending to click or move the cursor, with a Kalman filter that translates the neural population activity into cursor velocity. We present a paradigm for training the multi-state decoding algorithm using neural activity observed during imagined actions. Two human participants with tetraplegia (paralysis of the four limbs) performed a closed-loop radial target acquisition task using the point-and-click NIS over multiple sessions. We quantified point-and-click performance using various human-computer interaction measurements for pointing devices. We found that participants could control the cursor motion and click on specified targets with a small error rate (click 2-D cursor control of a personal computer. PMID:21278024

  16. A wireless transmission neural interface system for unconstrained non-human primates

    Science.gov (United States)

    Fernandez-Leon, Jose A.; Parajuli, Arun; Franklin, Robert; Sorenson, Michael; Felleman, Daniel J.; Hansen, Bryan J.; Hu, Ming; Dragoi, Valentin

    2015-10-01

    Objective. Studying the brain in large animal models in a restrained laboratory rig severely limits our capacity to examine brain circuits in experimental and clinical applications. Approach. To overcome these limitations, we developed a high-fidelity 96-channel wireless system to record extracellular spikes and local field potentials from the neocortex. A removable, external case of the wireless device is attached to a titanium pedestal placed in the animal skull. Broadband neural signals are amplified, multiplexed, and continuously transmitted as TCP/IP data at a sustained rate of 24 Mbps. A Xilinx Spartan 6 FPGA assembles the digital signals into serial data frames for transmission at 20 kHz though an 802.11n wireless data link on a frequency-shift key-modulated signal at 5.7-5.8 GHz to a receiver up to 10 m away. The system is powered by two CR123A, 3 V batteries for 2 h of operation. Main results. We implanted a multi-electrode array in visual area V4 of one anesthetized monkey (Macaca fascicularis) and in the dorsolateral prefrontal cortex (dlPFC) of a freely moving monkey (Macaca mulatta). The implanted recording arrays were electrically stable and delivered broadband neural data over a year of testing. For the first time, we compared dlPFC neuronal responses to the same set of stimuli (food reward) in restrained and freely moving conditions. Although we did not find differences in neuronal responses as a function of reward type in the restrained and unrestrained conditions, there were significant differences in correlated activity. This demonstrates that measuring neural responses in freely moving animals can capture phenomena that are absent in the traditional head-fixed paradigm. Significance. We implemented a wireless neural interface for multi-electrode recordings in freely moving non-human primates, which can potentially move systems neuroscience to a new direction by allowing one to record neural signals while animals interact with their environment.

  17. High-dimensional neural network potentials for multicomponent systems: First applications to zinc oxide

    Energy Technology Data Exchange (ETDEWEB)

    Artrith, Nongnuch; Morawietz, Tobias; Maschke, Marcus; Behler, Joerg [Lehrstuhl fuer Theoretische Chemie, Ruhr-Universitaet Bochum, D-44780 Bochum (Germany)

    2010-07-01

    Recently, artificial neural networks (NN) trained to first-principles data have shown to provide accurate potential energy surfaces for systems containing a single atomic species. In this work we present an extension of the NN approach to multicomponent systems by introducing physically motivated terms to deal with long-range interactions. This is a necessary condition for studying binary systems and general multicomponent systems with significant charge transfer. The capabilities of the method are demonstrated for crystal structures, amorphous structures, clusters, and surfaces of zinc oxide as a benchmark system. We show that the predicted energies and forces are in excellent agreement with reference density-functional theory calculations.

  18. Application of hierarchical dissociated neural network in closed-loop hybrid system integrating biological and mechanical intelligence.

    Directory of Open Access Journals (Sweden)

    Yongcheng Li

    Full Text Available Neural networks are considered the origin of intelligence in organisms. In this paper, a new design of an intelligent system merging biological intelligence with artificial intelligence was created. It was based on a neural controller bidirectionally connected to an actual mobile robot to implement a novel vehicle. Two types of experimental preparations were utilized as the neural controller including 'random' and '4Q' (cultured neurons artificially divided into four interconnected parts neural network. Compared to the random cultures, the '4Q' cultures presented absolutely different activities, and the robot controlled by the '4Q' network presented better capabilities in search tasks. Our results showed that neural cultures could be successfully employed to control an artificial agent; the robot performed better and better with the stimulus because of the short-term plasticity. A new framework is provided to investigate the bidirectional biological-artificial interface and develop new strategies for a future intelligent system using these simplified model systems.

  19. Application of hierarchical dissociated neural network in closed-loop hybrid system integrating biological and mechanical intelligence.

    Science.gov (United States)

    Li, Yongcheng; Sun, Rong; Zhang, Bin; Wang, Yuechao; Li, Hongyi

    2015-01-01

    Neural networks are considered the origin of intelligence in organisms. In this paper, a new design of an intelligent system merging biological intelligence with artificial intelligence was created. It was based on a neural controller bidirectionally connected to an actual mobile robot to implement a novel vehicle. Two types of experimental preparations were utilized as the neural controller including 'random' and '4Q' (cultured neurons artificially divided into four interconnected parts) neural network. Compared to the random cultures, the '4Q' cultures presented absolutely different activities, and the robot controlled by the '4Q' network presented better capabilities in search tasks. Our results showed that neural cultures could be successfully employed to control an artificial agent; the robot performed better and better with the stimulus because of the short-term plasticity. A new framework is provided to investigate the bidirectional biological-artificial interface and develop new strategies for a future intelligent system using these simplified model systems.

  20. Antenatal corticosteroids for management of preterm birth: a multi-country analysis of health system bottlenecks and potential solutions

    Science.gov (United States)

    2015-01-01

    Background Preterm birth complications are the leading cause of deaths for children under five years. Antenatal corticosteroids (ACS) are effective at reducing mortality and serious morbidity amongst infants born at 75%) reported very major or significant bottlenecks. Health information systems should include improved gestational age assessment and track ACS coverage, use and outcomes. Better health service delivery requires clarified policy assigning roles by level of care and cadre of provider, dependent on capability to assess gestational age and risk of preterm birth, and the implementation of guidelines with adequate supervision, mentoring and quality improvement systems, including audit and feedback. National essential medicines lists should include dexamethasone for antenatal use, and dexamethasone should be integrated into supply logistics. PMID:26390927

  1. Vehicle Signal Analysis Using Artificial Neural Networks for a Bridge Weigh-in-Motion System.

    Science.gov (United States)

    Kim, Sungkon; Lee, Jungwhee; Park, Min-Seok; Jo, Byung-Wan

    2009-01-01

    This paper describes the procedures for development of signal analysis algorithms using artificial neural networks for Bridge Weigh-in-Motion (B-WIM) systems. Through the analysis procedure, the extraction of information concerning heavy traffic vehicles such as weight, speed, and number of axles from the time domain strain data of the B-WIM system was attempted. As one of the several possible pattern recognition techniques, an Artificial Neural Network (ANN) was employed since it could effectively include dynamic effects and bridge-vehicle interactions. A number of vehicle traveling experiments with sufficient load cases were executed on two different types of bridges, a simply supported pre-stressed concrete girder bridge and a cable-stayed bridge. Different types of WIM systems such as high-speed WIM or low-speed WIM were also utilized during the experiments for cross-checking and to validate the performance of the developed algorithms.

  2. Vehicle Signal Analysis Using Artificial Neural Networks for a Bridge Weigh-in-Motion System

    Directory of Open Access Journals (Sweden)

    Min-Seok Park

    2009-10-01

    Full Text Available This paper describes the procedures for development of signal analysis algorithms using artificial neural networks for Bridge Weigh-in-Motion (B-WIM systems. Through the analysis procedure, the extraction of information concerning heavy traffic vehicles such as weight, speed, and number of axles from the time domain strain data of the B-WIM system was attempted. As one of the several possible pattern recognition techniques, an Artificial Neural Network (ANN was employed since it could effectively include dynamic effects and bridge-vehicle interactions. A number of vehicle traveling experiments with sufficient load cases were executed on two different types of bridges, a simply supported pre-stressed concrete girder bridge and a cable-stayed bridge. Different types of WIM systems such as high-speed WIM or low-speed WIM were also utilized during the experiments for cross-checking and to validate the performance of the developed algorithms.

  3. Comparative Study of Neural Network Frameworks for the Next Generation of Adaptive Optics Systems.

    Science.gov (United States)

    González-Gutiérrez, Carlos; Santos, Jesús Daniel; Martínez-Zarzuela, Mario; Basden, Alistair G; Osborn, James; Díaz-Pernas, Francisco Javier; De Cos Juez, Francisco Javier

    2017-06-02

    Many of the next generation of adaptive optics systems on large and extremely large telescopes require tomographic techniques in order to correct for atmospheric turbulence over a large field of view. Multi-object adaptive optics is one such technique. In this paper, different implementations of a tomographic reconstructor based on a machine learning architecture named "CARMEN" are presented. Basic concepts of adaptive optics are introduced first, with a short explanation of three different control systems used on real telescopes and the sensors utilised. The operation of the reconstructor, along with the three neural network frameworks used, and the developed CUDA code are detailed. Changes to the size of the reconstructor influence the training and execution time of the neural network. The native CUDA code turns out to be the best choice for all the systems, although some of the other frameworks offer good performance under certain circumstances.

  4. Simulation and stability analysis of neural network based control scheme for switched linear systems.

    Science.gov (United States)

    Singh, H P; Sukavanam, N

    2012-01-01

    This paper proposes a new adaptive neural network based control scheme for switched linear systems with parametric uncertainty and external disturbance. A key feature of this scheme is that the prior information of the possible upper bound of the uncertainty is not required. A feedforward neural network is employed to learn this upper bound. The adaptive learning algorithm is derived from Lyapunov stability analysis so that the system response under arbitrary switching laws is guaranteed uniformly ultimately bounded. A comparative simulation study with robust controller given in [Zhang L, Lu Y, Chen Y, Mastorakis NE. Robust uniformly ultimate boundedness control for uncertain switched linear systems. Computers and Mathematics with Applications 2008; 56: 1709-14] is presented. Copyright © 2011 ISA. Published by Elsevier Ltd. All rights reserved.

  5. Control Strategy Based on Wavelet Transform and Neural Network for Hybrid Power System

    Directory of Open Access Journals (Sweden)

    Y. D. Song

    2013-01-01

    Full Text Available This paper deals with an energy management of a hybrid power generation system. The proposed control strategy for the energy management is based on the combination of wavelet transform and neural network arithmetic. The hybrid system in this paper consists of an emulated wind turbine generator, PV panels, DC and AC loads, lithium ion battery, and super capacitor, which are all connected on a DC bus with unified DC voltage. The control strategy is responsible for compensating the difference between the generated power from the wind and solar generators and the demanded power by the loads. Wavelet transform decomposes the power difference into smoothed component and fast fluctuated component. In consideration of battery protection, the neural network is introduced to calculate the reference power of battery. Super capacitor (SC is controlled to regulate the DC bus voltage. The model of the hybrid system is developed in detail under Matlab/Simulink software environment.

  6. Robust MPC for a non-linear system - a neural network approach

    Science.gov (United States)

    Luzar, Marcel; Witczak, Marcin

    2014-12-01

    The aim of the paper is to design a robust actuator fault-tolerant control for a non-linear discrete-time system. Considered system is described by the Linear Parameter-Varying (LPV) model obtained with recurrent neural network. The proposed solution starts with a discretetime quasi-LPV system identification using artificial neural network. Subsequently, the robust controller is proposed, which does not take into account actuator saturation level and deals with the previously estimated faults. To check if the compensation problem is feasible, the robust invariant set is employed, which takes into account actuator saturation level. When the current state does not belong to the set, then a predictive control is performed in order to make such set larger. This makes it possible to increase the domain of attraction, which makes the proposed methodology an efficient solution for the fault-tolerant control. The last part of the paper presents an experimental results regarding wind turbines.

  7. Investigation of neural-net based control strategies for improved power system dynamic performance

    Energy Technology Data Exchange (ETDEWEB)

    Sobajic, D.J. [Electric Power Research Institute, Palo Alto, CA (United States)

    1995-12-31

    The ability to accurately predict the behavior of a dynamic system is of essential importance in monitoring and control of complex processes. In this regard recent advances in neural-net base system identification represent a significant step toward development and design of a new generation of control tools for increased system performance and reliability. The enabling functionality is the one of accurate representation of a model of a nonlinear and nonstationary dynamic system. This functionality provides valuable new opportunities including: (1) The ability to predict future system behavior on the basis of actual system observations, (2) On-line evaluation and display of system performance and design of early warning systems, and (3) Controller optimization for improved system performance. In this presentation, we discuss the issues involved in definition and design of learning control systems and their impact on power system control. Several numerical examples are provided for illustrative purpose.

  8. Neural systems and hormones mediating attraction to infant and child faces

    Directory of Open Access Journals (Sweden)

    Lizhu eLuo

    2015-07-01

    Full Text Available We find infant faces highly attractive as a result of specific features which Konrad Lorenz termed Kindchenschema or baby schema, and this is considered to be an important adaptive trait for promoting protective and caregiving behaviors in adults, thereby increasing the chances of infant survival. This review first examines the behavioral support for this effect and physical and behavioral factors which can influence it. It next reviews the increasing number of neuroimaging and electrophysiological studies investigating the neural circuitry underlying this baby schema effect in both parents and non-parents of both sexes. Next it considers potential hormonal contributions to the baby schema effect in both sexes and then neural effects associated with reduced responses to infant cues in post-partum depression, anxiety and drug taking. Overall the findings reviewed reveal a very extensive neural circuitry involved in our perception of cutenessin infant faces with enhanced activation compared to adult faces being found in brain regions involved in face perception, attention, emotion, empathy, memory, reward and attachment, theory of mind and also control of motor responses.Both mothers and fathers also show evidence for enhanced responses in these same neural systems when viewing their own as opposed to another child. Furthermore, responses to infant cues in many of these neural systems are reduced in mothers with post-partum depression or anxiety or have taken addictive drugs throughout pregnancy. In general reproductively active women tend to rate infant faces as cuter than men, which may reflect both heightened attention to relevant cues and a stronger activation in their brain reward circuitry. Perception of infant cuteness may also be influenced by reproductive hormones with the hypothalamic neuropeptide oxytocin being most strongly associated to date with increased attention andattractionto infant cues in both sexes.

  9. How age of bilingual exposure can change the neural systems for language in the developing brain: a functional near infrared spectroscopy investigation of syntactic processing in monolingual and bilingual children.

    Science.gov (United States)

    Jasinska, K K; Petitto, L A

    2013-10-01

    Is the developing bilingual brain fundamentally similar to the monolingual brain (e.g., neural resources supporting language and cognition)? Or, does early-life bilingual language experience change the brain? If so, how does age of first bilingual exposure impact neural activation for language? We compared how typically-developing bilingual and monolingual children (ages 7-10) and adults recruit brain areas during sentence processing using functional Near Infrared Spectroscopy (fNIRS) brain imaging. Bilingual participants included early-exposed (bilingual exposure from birth) and later-exposed individuals (bilingual exposure between ages 4-6). Both bilingual children and adults showed greater neural activation in left-hemisphere classic language areas, and additionally, right-hemisphere homologues (Right Superior Temporal Gyrus, Right Inferior Frontal Gyrus). However, important differences were observed between early-exposed and later-exposed bilinguals in their earliest-exposed language. Early bilingual exposure imparts fundamental changes to classic language areas instead of alterations to brain regions governing higher cognitive executive functions. However, age of first bilingual exposure does matter. Later-exposed bilinguals showed greater recruitment of the prefrontal cortex relative to early-exposed bilinguals and monolinguals. The findings provide fascinating insight into the neural resources that facilitate bilingual language use and are discussed in terms of how early-life language experiences can modify the neural systems underlying human language processing. Copyright © 2013 The Authors. Published by Elsevier Ltd.. All rights reserved.

  10. System control fuzzy neural sewage pumping stations using genetic algorithms

    Directory of Open Access Journals (Sweden)

    Владлен Николаевич Кузнецов

    2015-06-01

    Full Text Available It is considered the system of management of sewage pumping station with regulators based on a neuron network with fuzzy logic. Linguistic rules for the controller based on fuzzy logic, maintaining the level of effluent in the receiving tank within the prescribed limits are developed. The use of genetic algorithms for neuron network training is shown.

  11. An Artificial Neural System for Autonomous Undersea Vehicles

    Science.gov (United States)

    1988-07-01

    Scale. Phylum Example Advance Ability Protozoa Paramecium No Nervous System Swim Food Discrimination Coelenterata Hydra Nerve Nets Spontaneity Anemone...Statolith Righting Jellyfish Escape Flatworms Planaria Bilateral Symmetry Kinesis Head Ganglion Taxis Commissures Conditioning Muhisensors Roundworms...to examine how nature has developed these capabilities. First, basic reflexes were established to orient the animal to critical features in its

  12. Power prediction in mobile communication systems using an optimal neural-network structure.

    Science.gov (United States)

    Gao, X M; Gao, X Z; Tanskanen, J A; Ovaska, S J

    1997-01-01

    Presents a novel neural-network-based predictor for received power level prediction in direct sequence code division multiple access (DS/CDMA) systems. The predictor consists of an adaptive linear element (Adaline) followed by a multilayer perceptron (MLP). An important but difficult problem in designing such a cascade predictor is to determine the complexity of the networks. We solve this problem by using the predictive minimum description length (PMDL) principle to select the optimal numbers of input and hidden nodes. This approach results in a predictor with both good noise attenuation and excellent generalization capability. The optimized neural networks are used for predictive filtering of very noisy Rayleigh fading signals with 1.8 GHz carrier frequency. Our results show that the optimal neural predictor can provide smoothed in-phase and quadrature signals with signal-to-noise ratio (SNR) gains of about 12 and 7 dB at the urban mobile speeds of 5 and 50 km/h, respectively. The corresponding power signal SNR gains are about 11 and 5 dB. Therefore, the neural predictor is well suitable for power control applications where ldquodelaylessrdquo noise attenuation and efficient reduction of fast fading are required.

  13. Comparison of MLP neural network and neuro-fuzzy system in transcranial Doppler signals recorded from the cerebral vessels.

    Science.gov (United States)

    Hardalaç, Firat

    2008-04-01

    Transcranial Doppler signals recorded from cerebral vessels of 110 patients were transferred to a personal computer by using a 16 bit sound card. Spectral analyses of Transcranial Doppler signals were performed for determining the Multi Layer Perceptron (MLP) neural network and neuro Ankara-fuzzy system inputs. In order to do a good interpretation and rapid diagnosis, FFT parameters of Transcranial Doppler signals classified using MLP neural network and neuro-fuzzy system. Our findings demonstrated that 92% correct classification rate was obtained from MLP neural network, and 86% correct classification rate was obtained from neuro-fuzzy system.

  14. Birth, growth and progresses through the last twelve years of a regional scale landslide warning system

    Science.gov (United States)

    Fanti, Riccardo; Segoni, Samuele; Rosi, Ascanio; Lagomarsino, Daniela; Catani, Filippo

    2017-04-01

    SIGMA is a regional landslide warning system that operates in the Emilia Romagna region (Italy). In this work, we depict its birth and the continuous development process, still ongoing, after over a decade of operational employ. Traditionally, landslide rainfall thresholds are defined by the empirical correspondence between a rainfall database and a landslide database. However, in the early stages of the research, a complete catalogue of dated landslides was not available. Therefore, the prototypal version of SIGMA was based on rainfall thresholds defined by means of a statistical analysis performed over the rainfall time series. SIGMA was purposely designed to take into account both shallow and deep seated landslides and it was based on the hypothesis that anomalous or extreme values of accumulated rainfall are responsible for landslide triggering. The statistical distribution of the rainfall series was analyzed, and multiples of the standard deviation (σ) were used as thresholds to discriminate between ordinary and extraordinary rainfall events. In the warning system, the measured and the forecasted rainfall are compared with these thresholds. Since the response of slope stability to rainfall may be complex, SIGMA is based on a decision algorithm aimed at identifying short but exceptionally intense rainfalls and mild but exceptionally prolonged rains: while the former are commonly associated with shallow landslides, the latter are mainly associated with deep-seated landslides. In the first case, the rainfall threshold is defined by high σ values and short durations (i.e. a few days); in the second case, σ values are lower but the decision algorithm checks long durations (i.e. some months). The exact definition of "high" and "low" σ values and of "short" and "long" duration varied through time according as it was adjusted during the evolution of the model. Indeed, since 2005, a constant work was carried out to gather and organize newly available data (rainfall

  15. Distributed Energy Neural Network Integration System: Year One Final Report

    Energy Technology Data Exchange (ETDEWEB)

    Regan, T.; Sinnock, H.; Davis, A.

    2003-06-01

    This report describes the work of Orion Engineering Corp. to develop a DER household controller module and demonstrate the ability of a group of these controllers to operate through an intelligent, neighborhood controller. The controllers will provide a smart, technologically advanced, simple, efficient, and economic solution for aggregating a community of small distributed generators into a larger single, virtual generator capable of selling power or other services to a utility, independent system operator (ISO), or other entity in a coordinated manner.

  16. Internal models and neural computation in the vestibular system

    OpenAIRE

    Green, Andrea M.; Dora E. Angelaki

    2010-01-01

    The vestibular system is vital for motor control and spatial self-motion perception. Afferents from the otolith organs and the semicircular canals converge with optokinetic, somatosensory and motor-related signals in the vestibular nuclei, which are reciprocally interconnected with the vestibulocerebellar cortex and deep cerebellar nuclei. Here, we review the properties of the many cell types in the vestibular nuclei, as well as some fundamental computations implemented within this brainstem–...

  17. A Parallel Strategy for Convolutional Neural Network Based on Heterogeneous Cluster for Mobile Information System

    Directory of Open Access Journals (Sweden)

    Jilin Zhang

    2017-01-01

    Full Text Available With the development of the mobile systems, we gain a lot of benefits and convenience by leveraging mobile devices; at the same time, the information gathered by smartphones, such as location and environment, is also valuable for business to provide more intelligent services for customers. More and more machine learning methods have been used in the field of mobile information systems to study user behavior and classify usage patterns, especially convolutional neural network. With the increasing of model training parameters and data scale, the traditional single machine training method cannot meet the requirements of time complexity in practical application scenarios. The current training framework often uses simple data parallel or model parallel method to speed up the training process, which is why heterogeneous computing resources have not been fully utilized. To solve these problems, our paper proposes a delay synchronization convolutional neural network parallel strategy, which leverages the heterogeneous system. The strategy is based on both synchronous parallel and asynchronous parallel approaches; the model training process can reduce the dependence on the heterogeneous architecture in the premise of ensuring the model convergence, so the convolution neural network framework is more adaptive to different heterogeneous system environments. The experimental results show that the proposed delay synchronization strategy can achieve at least three times the speedup compared to the traditional data parallelism.

  18. Artificial neural networks and neuro-fuzzy inference systems as virtual sensors for hydrogen safety prediction

    Energy Technology Data Exchange (ETDEWEB)

    Karri, Vishy; Ho, Tien [School of Engineering, University of Tasmania, GPO Box 252-65, Hobart, Tasmania 7001 (Australia); Madsen, Ole [Department of Production, Aalborg University, Fibigerstraede 16, DK-9220 Aalborg (Denmark)

    2008-06-15

    Hydrogen is increasingly investigated as an alternative fuel to petroleum products in running internal combustion engines and as powering remote area power systems using generators. The safety issues related to hydrogen gas are further exasperated by expensive instrumentation required to measure the percentage of explosive limits, flow rates and production pressure. This paper investigates the use of model based virtual sensors (rather than expensive physical sensors) in connection with hydrogen production with a Hogen 20 electrolyzer system. The virtual sensors are used to predict relevant hydrogen safety parameters, such as the percentage of lower explosive limit, hydrogen pressure and hydrogen flow rate as a function of different input conditions of power supplied (voltage and current), the feed of de-ionized water and Hogen 20 electrolyzer system parameters. The virtual sensors are developed by means of the application of various Artificial Intelligent techniques. To train and appraise the neural network models as virtual sensors, the Hogen 20 electrolyzer is instrumented with necessary sensors to gather experimental data which together with MATLAB neural networks toolbox and tailor made adaptive neuro-fuzzy inference systems (ANFIS) were used as predictive tools to estimate hydrogen safety parameters. It was shown that using the neural networks hydrogen safety parameters were predicted to less than 3% of percentage average root mean square error. The most accurate prediction was achieved by using ANFIS. (author)

  19. Adaptive neural networks control for camera stabilization with active suspension system

    Directory of Open Access Journals (Sweden)

    Feng Zhao

    2015-08-01

    Full Text Available The camera always suffers from image instability on the moving vehicle due to unintentional vibrations caused by road roughness. This article presents an adaptive neural network approach mixed with linear quadratic regulator control for a quarter-car active suspension system to stabilize the image captured area of the camera. An active suspension system provides extra force through the actuator which allows it to suppress vertical vibration of sprung mass. First, to deal with the road disturbance and the system uncertainties, radial basis function neural network is proposed to construct the map between the state error and the compensation component, which can correct the optimal state-feedback control law. The weights matrix of radial basis function neural network is adaptively tuned online. Then, the closed-loop stability and asymptotic convergence performance is guaranteed by Lyapunov analysis. Finally, the simulation results demonstrate that the proposed controller effectively suppresses the vibration of the camera and enhances the stabilization of the entire camera, where different excitations are considered to validate the system performance.

  20. Neural Network Model Of The PXIE RFQ Cooling System and Resonant Frequency Response

    Energy Technology Data Exchange (ETDEWEB)

    Edelen, Auralee [Fermilab; Biedron, Sandra [Colorado State U., Fort Collins; Bowring, Daniel [Fermilab; Chase, Brian [Fermilab; Edelen, Jonathan [Fermilab; Milton, Stephen [Colorado State U., Fort Collins; Steimel, Jim [Fermilab

    2016-06-01

    As part of the PIP-II Injector Experiment (PXIE) accel-erator, a four-vane radio frequency quadrupole (RFQ) accelerates a 30-keV, 1-mA to 10-mA H' ion beam to 2.1 MeV. It is designed to operate at a frequency of 162.5 MHz with arbitrary duty factor, including continuous wave (CW) mode. The resonant frequency is controlled solely by a water-cooling system. We present an initial neural network model of the RFQ frequency response to changes in the cooling system and RF power conditions during pulsed operation. A neural network model will be used in a model predictive control scheme to regulate the resonant frequency of the RFQ.

  1. The NPY system and its neural and neuroendocrine regulation of bone.

    Science.gov (United States)

    Khor, Ee Cheng; Baldock, Paul

    2012-06-01

    The past decade has seen a significant expansion of our understanding of the interaction between the neural system and bone. While innervation of bone was long appreciated, the discovery of central relays from the hypothalamus to the cells of bone has seen the identification of a number of efferent neural pathways to bone. The neuropeptide Y (NPY) system has proven to represent a major central pathway, regulating the activity of osteoblasts and osteoclasts, through signaling of central and peripheral ligands, through specific receptors within the hypothalamus and the osteoblast. Moreover, this pathway is now recognized as acting to coordinate both skeletal and energy homeostasis. This review examines the mechanism and actions of the NPY pathway to regulate bone mass and bone cell activity.

  2. Immature visual neural system in children reflected by contrast sensitivity with adaptive optics correction

    Science.gov (United States)

    Liu, Rong; Zhou, Jiawei; Zhao, Haoxin; Dai, Yun; Zhang, Yudong; Tang, Yong; Zhou, Yifeng

    2014-01-01

    This study aimed to explore the neural development status of the visual system of children (around 8 years old) using contrast sensitivity. We achieved this by eliminating the influence of higher order aberrations (HOAs) with adaptive optics correction. We measured HOAs, modulation transfer functions (MTFs) and contrast sensitivity functions (CSFs) of six children and five adults with both corrected and uncorrected HOAs. We found that when HOAs were corrected, children and adults both showed improvements in MTF and CSF. However, the CSF of children was still lower than the adult level, indicating the difference in contrast sensitivity between groups cannot be explained by differences in optical factors. Further study showed that the difference between the groups also could not be explained by differences in non-visual factors. With these results we concluded that the neural systems underlying vision in children of around 8 years old are still immature in contrast sensitivity. PMID:24732728

  3. Based on Artificial Neural Network to Realize K-Parameter Analysis of Vehicle Air Spring System

    Science.gov (United States)

    Hung, San-Shan; Hsu, Chia-Ning; Hwang, Chang-Chou; Chen, Wen-Jan

    2017-10-01

    In recent years, because of the air-spring control technique is more mature, that air- spring suspension systems already can be used to replace the classical vehicle suspension system. Depend on internal pressure variation of the air-spring, thestiffnessand the damping factor can be adjusted. Because of air-spring has highly nonlinear characteristic, therefore it isn’t easy to construct the classical controller to control the air-spring effectively. The paper based on Artificial Neural Network to propose a feasible control strategy. By using offline way for the neural network design and learning to the air-spring in different initial pressures and different loads, offline method through, predict air-spring stiffness parameter to establish a model. Finally, through adjusting air-spring internal pressure to change the K-parameter of the air-spring, realize the well dynamic control performance of air-spring suspension.

  4. Adaptive complementary fuzzy self-recurrent wavelet neural network controller for the electric load simulator system

    Directory of Open Access Journals (Sweden)

    Wang Chao

    2016-03-01

    Full Text Available Due to the complexities existing in the electric load simulator, this article develops a high-performance nonlinear adaptive controller to improve the torque tracking performance of the electric load simulator, which mainly consists of an adaptive fuzzy self-recurrent wavelet neural network controller with variable structure (VSFSWC and a complementary controller. The VSFSWC is clearly and easily used for real-time systems and greatly improves the convergence rate and control precision. The complementary controller is designed to eliminate the effect of the approximation error between the proposed neural network controller and the ideal feedback controller without chattering phenomena. Moreover, adaptive learning laws are derived to guarantee the system stability in the sense of the Lyapunov theory. Finally, the hardware-in-the-loop simulations are carried out to verify the feasibility and effectiveness of the proposed algorithms in different working styles.

  5. A neural learning classifier system with self-adaptive constructivism for mobile robot control.

    Science.gov (United States)

    Hurst, Jacob; Bull, Larry

    2006-01-01

    For artificial entities to achieve true autonomy and display complex lifelike behavior, they will need to exploit appropriate adaptable learning algorithms. In this context adaptability implies flexibility guided by the environment at any given time and an open-ended ability to learn appropriate behaviors. This article examines the use of constructivism-inspired mechanisms within a neural learning classifier system architecture that exploits parameter self-adaptation as an approach to realize such behavior. The system uses a rule structure in which each rule is represented by an artificial neural network. It is shown that appropriate internal rule complexity emerges during learning at a rate controlled by the learner and that the structure indicates underlying features of the task. Results are presented in simulated mazes before moving to a mobile robot platform.

  6. Chaotic Extension Neural Network-Based Fault Diagnosis Method for Solar Photovoltaic Systems

    Directory of Open Access Journals (Sweden)

    Kuo-Nan Yu

    2014-01-01

    Full Text Available At present, the solar photovoltaic system is extensively used. However, once a fault occurs, it is inspected manually, which is not economical. In order to remedy the defect of unavailable fault diagnosis at any irradiance and temperature in the literature with chaos synchronization based intelligent fault diagnosis for photovoltaic systems proposed by Hsieh et al., this study proposed a chaotic extension fault diagnosis method combined with error back propagation neural network to overcome this problem. It used the nn toolbox of matlab 2010 for simulation and comparison, measured current irradiance and temperature, and used the maximum power point tracking (MPPT for chaotic extraction of eigenvalue. The range of extension field was determined by neural network. Finally, the voltage eigenvalue obtained from current temperature and irradiance was used for the fault diagnosis. Comparing the diagnostic rates with the results by Hsieh et al., this scheme can obtain better diagnostic rates when the irradiances or the temperatures are changed.

  7. Anomaly Detection for Resilient Control Systems Using Fuzzy-Neural Data Fusion Engine

    Energy Technology Data Exchange (ETDEWEB)

    Ondrej Linda; Milos Manic; Timothy R. McJunkin

    2011-08-01

    Resilient control systems in critical infrastructures require increased cyber-security and state-awareness. One of the necessary conditions for achieving the desired high level of resiliency is timely reporting and understanding of the status and behavioral trends of the control system. This paper describes the design and development of a neural-network based data-fusion system for increased state-awareness of resilient control systems. The proposed system consists of a dedicated data-fusion engine for each component of the control system. Each data-fusion engine implements three-layered alarm system consisting of: (1) conventional threshold-based alarms, (2) anomalous behavior detector using self-organizing maps, and (3) prediction error based alarms using neural network based signal forecasting. The proposed system was integrated with a model of the Idaho National Laboratory Hytest facility, which is a testing facility for hybrid energy systems. Experimental results demonstrate that the implemented data fusion system provides timely plant performance monitoring and cyber-state reporting.

  8. Neural Systems Involved When Attending to a Speaker.

    Science.gov (United States)

    Kamourieh, Salwa; Braga, Rodrigo M; Leech, Robert; Newbould, Rexford D; Malhotra, Paresh; Wise, Richard J S

    2015-11-01

    Remembering what a speaker said depends on attention. During conversational speech, the emphasis is on working memory, but listening to a lecture encourages episodic memory encoding. With simultaneous interference from background speech, the need for auditory vigilance increases. We recreated these context-dependent demands on auditory attention in 2 ways. The first was to require participants to attend to one speaker in either the absence or presence of a distracting background speaker. The second was to alter the task demand, requiring either an immediate or delayed recall of the content of the attended speech. Across 2 fMRI studies, common activated regions associated with segregating attended from unattended speech were the right anterior insula and adjacent frontal operculum (aI/FOp), the left planum temporale, and the precuneus. In contrast, activity in a ventral right frontoparietal system was dependent on both the task demand and the presence of a competing speaker. Additional multivariate analyses identified other domain-general frontoparietal systems, where activity increased during attentive listening but was modulated little by the need for speech stream segregation in the presence of 2 speakers. These results make predictions about impairments in attentive listening in different communicative contexts following focal or diffuse brain pathology. © The Author 2015. Published by Oxford University Press.

  9. The Neural Systems of Forgiveness: An Evolutionary Psychological Perspective

    Directory of Open Access Journals (Sweden)

    Joseph Billingsley

    2017-05-01

    Full Text Available Evolution-minded researchers posit that the suite of human cognitive adaptations may include forgiveness systems. According to these researchers, forgiveness systems regulate interpersonal motivation toward a transgressor in the wake of harm by weighing multiple factors that influence both the potential gains of future interaction with the transgressor and the likelihood of future harm. Although behavioral research generally supports this evolutionary model of forgiveness, the model’s claims have not been examined with available neuroscience specifically in mind, nor has recent neuroscientific research on forgiveness generally considered the evolutionary literature. The current review aims to help bridge this gap by using evolutionary psychology and cognitive neuroscience to mutually inform and interrogate one another. We briefly summarize the evolutionary research on forgiveness, then review recent neuroscientific findings on forgiveness in light of the evolutionary model. We emphasize neuroscientific research that links desire for vengeance to reward-based areas of the brain, that singles out prefrontal areas likely associated with inhibition of vengeful feelings, and that correlates the activity of a theory-of-mind network with assessments of the intentions and blameworthiness of those who commit harm. In addition, we identify gaps in the existing neuroscientific literature, and propose future research directions that might address them, at least in part.

  10. The Neural Systems of Forgiveness: An Evolutionary Psychological Perspective.

    Science.gov (United States)

    Billingsley, Joseph; Losin, Elizabeth A R

    2017-01-01

    Evolution-minded researchers posit that the suite of human cognitive adaptations may include forgiveness systems. According to these researchers, forgiveness systems regulate interpersonal motivation toward a transgressor in the wake of harm by weighing multiple factors that influence both the potential gains of future interaction with the transgressor and the likelihood of future harm. Although behavioral research generally supports this evolutionary model of forgiveness, the model's claims have not been examined with available neuroscience specifically in mind, nor has recent neuroscientific research on forgiveness generally considered the evolutionary literature. The current review aims to help bridge this gap by using evolutionary psychology and cognitive neuroscience to mutually inform and interrogate one another. We briefly summarize the evolutionary research on forgiveness, then review recent neuroscientific findings on forgiveness in light of the evolutionary model. We emphasize neuroscientific research that links desire for vengeance to reward-based areas of the brain, that singles out prefrontal areas likely associated with inhibition of vengeful feelings, and that correlates the activity of a theory-of-mind network with assessments of the intentions and blameworthiness of those who commit harm. In addition, we identify gaps in the existing neuroscientific literature, and propose future research directions that might address them, at least in part.

  11. Calculation of transmission system losses for the Taiwan Power Company by the artificial neural network with time decayed weight

    Energy Technology Data Exchange (ETDEWEB)

    Chu, W.C.; Chen, B.K.; Mo, P.C. [Tatung Inst. of Tech., Taipei (Taiwan, Province of China)

    1995-12-31

    For energy conservation and improvement of power system operation efficiency, how to reduce the transmission system losses becomes an important topic of grave concern. To understand the cause, and to evaluate the amount, of the losses are the prior steps to diminish them. To simplify the evaluation procedure without losing too much accuracy, this paper adopts the artificial neural network, which is a model free network, to analyze the transmission system losses. As the artificial neural network with time decayed weight has the capability of learning, memorizing, and forgetting, it is more suitable for a power system with gradually changing characteristics. By using this artificial neural network, the estimation of transmission system losses will be more precise. In this paper, comparison will be made between the results of artificial neural network analysis and polynomial loss equations analysis.

  12. Use of neural networks in the capacitance imaging system. Technical note

    Energy Technology Data Exchange (ETDEWEB)

    Fasching, G.E.; Loudin, W.J.; Paton, D.E.; Smith, N.S. Jr.

    1993-10-01

    The US Department of Energy`s Morgantown Energy Technology Center (METC) has developed a capacitance imaging system (CIS) to support its fluidized-bed research programs. The CIS uses 400 electric displacement current measurements taken between combinations of pairs of 32 electrodes to obtain a measure of the fluidized-bed material density in the volume between the electrodes. The measurements are simultaneously made for three other sets of horizontally-oriented 32 electrodes with the four sets evenly spaced vertically. This report describes the development of a method of using the 400 current measurements per level as the input to a neural network to produce the 193-pixel density estimates defined for each level. A 417-neuron subnetwork using 4,047 weights is defined as the system used to determine a set of 32-pixel densities in one of the annular regions of the fluidized-bed cross section. The same subnetwork with different values of weights is used for the other five annular regions that cover the rest of the cross section. An averaging technique is used to determine the density of the small central region. The methods used to optimize the set of weights for each of the six subnetworks are described. The results of tests using calibration electric current data as inputs to the neural system showed that these density estimates have less error than three previously developed methods of converting current measurements into pixel density maps. A comparison of the density maps produced by the neural system and the alternate three methods using input fluidization data also indicates the superior performance of the neural network approach.

  13. Reconfigurable embedded system architecture for next-generation Neural Signal Processing.

    Science.gov (United States)

    Balasubramanian, Karthikeyan; Obeid, Iyad

    2010-01-01

    This work presents a new architectural framework for next generation Neural Signal Processing (NSP). The essential features of the NSP hardware platform include scalability, reconfigurability, real-time processing ability and data storage. This proposed framework has been implemented in a proof-of-concept NSP prototype using an embedded system architecture synthesized in a Xilinx(®)Virtex(®)5 development board. The prototype includes a threshold-based spike detector and a fuzzy logic-based spike sorter.

  14. Functioning of Neural Systems Supporting Emotion Regulation in Anxiety-Prone Individuals

    OpenAIRE

    Campbell-Sills, Laura; Simmons, Alan N.; Lovero, Kathryn L.; Rochlin, Alexis A.; Martin P Paulus; Stein, Murray B.

    2010-01-01

    Previous neuroimaging studies suggest that prefrontal cortex (PFC) modulation of the amygdala and related limbic structures is an underlying neural substrate of effortful emotion regulation. Anxiety-prone individuals experience excessive negative emotions, signaling potential dysfunction of systems supporting down-regulation of negative emotions. We examined the hypothesis that anxious individuals require increased recruitment of lateral and medial PFC to decrease negative emotions. An emotio...

  15. Aplikasi Model Artificial Neural Network Terintegrasi dengan Geographycal Information System untuk Evaluasi Kesesuaian Lahan Perkebunan Kakao

    OpenAIRE

    Hermantoro; Rudiyanto; Slamet Suprayogi

    2008-01-01

    Land evaluation for specific purpose in plantation sector become very important due to increasing the competition in land use and the development of plantation sector. Land evaluation produces information of land economic values for specific land use. The objective of the research is to develop land evaluation method for cocoa estate using integrated model Artificial Neural Network (ANN) and Geographical Information System (GIS). Back propagation ANN model were used to predict cocoa yield bas...

  16. Placebo-Activated Neural Systems are Linked to Antidepressant Responses

    Science.gov (United States)

    Peciña, Marta; Bohnert, Amy S. B.; Sikora, Magdalena; Avery, Erich T.; Langenecker, Scott A.; Mickey, Brian J.; Zubieta, Jon-Kar

    2016-01-01

    Importance High placebo responses have been observed across a wide range of pathologies, severely impacting drug development. Objective Here we examined neurochemical mechanisms underlying the formation of placebo effects in patients with Major Depressive Disorder (MDD). Participants Thirty-five medication-free MDD patients. Design and Intervention We performed a single-blinded two-week cross-over randomized controlled trial of two identical oral placebos (described as having either “active” or “inactive” fast-acting antidepressant-like effects) followed by a 10-week open-label treatment with a selective serotonin reuptake inhibitor (SSRI) or in some cases, another agent as clinically indicated. The volunteers were studied with PET and the μ-opioid receptor (MOR)-selective radiotracer [11C]carfentanil after each 1-week “inactive” and “active” oral placebo treatment. In addition, 1 mL of isotonic saline was administered intravenously (i.v.) within sight of the volunteer during PET scanning every 4 min over 20 min only after the 1-week active placebo treatment, with instructions that the compound may be associated with the activation of brain systems involved in mood improvement. This challenge stimulus was utilized to test the individual capacity to acutely activate endogenous opioid neurotransmision under expectations of antidepressant effect. Setting A University Health System. Main Outcomes and Measures Changes in depressive symptoms in response to “active” placebo and antidepressant. Baseline and activation measures of MOR binding. Results Higher baseline MOR binding in the nucleus accumbens (NAc) was associated with better response to antidepressant treatment (r=0.48; p=0.02). Reductions in depressive symptoms after 1-week of “active” placebo treatment, compared to the “inactive”, were associated with increased placebo-induced μ-opioid neurotransmission in a network of regions implicated in emotion, stress regulation, and the

  17. Disruptions to the cerebellar GABAergic system in juvenile guinea pigs following preterm birth.

    Science.gov (United States)

    Shaw, Julia C; Palliser, Hannah K; Dyson, Rebecca M; Berry, Mary J; Hirst, Jonathan J

    2018-04-01

    Children that are born preterm are at an increased risk of developing cognitive problems and behavioural disorders, such as attention deficit hyperactivity disorder (ADHD). There is increasing interest in the role of the cerebellum in these processes and the potential involvement of GABAergic pathways in neurodevelopmental disorders. We propose that preterm birth, and the associated loss of the trophic intrauterine environment, alters the development of the cerebellum, contributing to ongoing neurobehavioral disorders. Guinea pigs were delivered preterm (GA62) or spontaneously at term (GA69), and tissues collected at corrected postnatal day (PND) 28. Neurodevelopmental and GABAergic markers myelin basic protein (MBP), neuronal nuclei (NeuN), calbindin (Purkinje cells), and GAD67 (GABA synthesis enzyme) were analysed in cerebellar lobules IX and X by immunohistochemistry. Protein expression of GAD67 and GAT1 (GABA transporter enzyme) were quantified by western blot, whilst neurosteroid-sensitive GABA A receptor subunits were measured by RT-PCR. MBP immunostaining was increased in lobule IX of preterm males, and reduced in lobule X of preterm females when compared to their term counterparts. GAD67 staining was decreased in lobule IX and X of the preterm males, but only in lobule X of the preterm females compared to term cohorts for each sex. Internal granule cell layer width of lobule X was decreased in preterm cohorts of both sexes compared to terms. There were no differences between gestational age groups for NeuN staining, GAD67 and GAT1 protein expression as measured by western blotting, or GABA A receptor subunits as measured by RT-PCR between preterm and term for either sex. The present findings suggest that components of the cerebellar GABAergic system of the ex-preterm cerebellum are disrupted. The higher expression of myelin in the preterm males may be due to a deficit in axonal pruning, whereas females have a deficit in myelination at 28 corrected days of

  18. [Polymorphisms of genes of rennin-angiotensine system and their correlation with psychological manifestations of birth stress].

    Science.gov (United States)

    Spivak, I M; Seĭlieva, N A; Smirnova, T Iu; Bolotskikh, V M; Abramchenko, V V; Spivak, D L

    2008-01-01

    Study of correlation of polymorphisms of genes of the rennin-angiotensine system--insertion-deletion (I/D) polymorphism of the angiotensine-converting enzime (ACE), and the polymorphism of angiotensinogen (AGT), consisting in submission of T into C in the 704 position (M235T)--with various characteristics of psychological functioning and clinical characteristics in women giving birth, was conducted. The group consisted of 56 young female Ss, without chronic diseases in the anamnesis, no complication in the course of pregnancy, birth normal and timely, state of the child normal in all cases. Basing on factor analysis of 7 psychological induces, providing integral assessment of state of the Ss in the course la late pregnancy, giving birth, and the immediately following post partum period, statistically reliable correlation between such first-rate psychological process as creativity, and polymorphism of the gene of angiotensinogene, was demonstrated, as well as difference in distribution of genotypes between the group of women giving birth, and general population. Statistically relevant correlation between presence of the deletion (D) allele of the ACE gene, and heightened level of neuroticization, reported by us earlier, was observed in this case, as well. No significant links between religiosity/spirituality, and genetic data was found. These results are in concordance with present-day views concerning the role of the rennin-angiotensine system in the providing of active adaptation to stressful conditions; they tend to provide fresh outlook upon the comparative study of input of heredity, and education, into the ontogenesis of higher psychological functions in normal humans.

  19. Theoretical system dynamics modeling for Taiwan pediatric workforce in an era of national health insurance and low birth rates.

    Science.gov (United States)

    Wu, Mei-Hwan; Yu, Jiun-Yu; Huang, Chung-Hsing

    2013-12-01

    In an era of declining birth rates and a single-payer health care system, the pediatric workforce might overreact to its demands. System dynamics (SD) were therefore applied to establish models to predict the future need and demand for the pediatric workforce. Data of population and workforce were extracted from national databases and models developed using Vensim software. In the past decade, the child-to-pediatrician ratio correlated with infant mortality in Taiwan (p < 0.001, r(2) = 0.88, child-to-pediatrician ratio = 146 + 354 × infant mortality/1000 live births). Currently, the child-to-pediatrician ratio is 1742:1. Using the Millennium Development Goals (2.437/1000 live births) for infant mortality, the child-to-pediatrician ratio was estimated as 1009:1. The pediatric population model incorporated the birth and mortality rates in each age category, accurately predicted population changes between 1974 and 2010, and projected a decreasing trend until 2017. The pediatric workforce model, which considered rates of enrollment, completion, certification, and retention, predicted a decrease in the supply of pediatricians in the mid-2010s that could be delayed by policy incentives. When targeting the base scenario, the model indicated that discrepancies between demands and supply of pediatricians would occur in the late 2010s toward 2020. When targeting the Millennium Development Goals scenario, however, the discrepancies would be consistent. Effective SD models were developed for the population and health care workforce. The strengths of the SD models are derived from simulation, which is subject to influence from new policies. Policies can, therefore, be examined and intervened in a timely manner. Copyright © 2013. Published by Elsevier B.V.

  20. Enhancement of Spike-Timing-Dependent Plasticity in Spiking Neural Systems with Noise.

    Science.gov (United States)

    Nobukawa, Sou; Nishimura, Haruhiko

    2016-08-01

    Synaptic plasticity is widely recognized to support adaptable information processing in the brain. Spike-timing-dependent plasticity, one subtype of plasticity, can lead to synchronous spike propagation with temporal spiking coding information. Recently, it was reported that in a noisy environment, like the actual brain, the spike-timing-dependent plasticity may be made efficient by the effect of stochastic resonance. In the stochastic resonance, the presence of noise helps a nonlinear system in amplifying a weak (under barrier) signal. However, previous studies have ignored the full variety of spiking patterns and many relevant factors in neural dynamics. Thus, in order to prove the physiological possibility for the enhancement of spike-timing-dependent plasticity by stochastic resonance, it is necessary to demonstrate that this stochastic resonance arises in realistic cortical neural systems. In this study, we evaluate this stochastic resonance phenomenon in the realistic cortical neural system described by the Izhikevich neuron model and compare the characteristics of typical spiking patterns of regular spiking, intrinsically bursting and chattering experimentally observed in the cortex.

  1. Switched-Observer-Based Adaptive Neural Control of MIMO Switched Nonlinear Systems With Unknown Control Gains.

    Science.gov (United States)

    Long, Lijun; Zhao, Jun

    2017-07-01

    In this paper, the problem of adaptive neural output-feedback control is addressed for a class of multi-input multioutput (MIMO) switched uncertain nonlinear systems with unknown control gains. Neural networks (NNs) are used to approximate unknown nonlinear functions. In order to avoid the conservativeness caused by adoption of a common observer for all subsystems, an MIMO NN switched observer is designed to estimate unmeasurable states. A new switched observer-based adaptive neural control technique for the problem studied is then provided by exploiting the classical average dwell time (ADT) method and the backstepping method and the Nussbaum gain technique. It effectively handles the obstacle about the coexistence of multiple Nussbaum-type function terms, and improves the classical ADT method, since the exponential decline property of Lyapunov functions for individual subsystems is no longer satisfied. It is shown that the technique proposed is able to guarantee semiglobal uniformly ultimately boundedness of all the signals in the closed-loop system under a class of switching signals with ADT, and the tracking errors converge to a small neighborhood of the origin. The effectiveness of the approach proposed is illustrated by its application to a two inverted pendulum system.

  2. Altered Dynamics Between Neural Systems Sub-serving Decisions for Unhealthy Food

    Directory of Open Access Journals (Sweden)

    Qinghua eHe

    2014-11-01

    Full Text Available Using BOLD functional magnetic resonance imaging (fMRI techniques, we examined the relationships between activities in the neural systems elicited by the decision stage of the Iowa Gambling Task (IGT, and food choices of either vegetables or snacks high in fat and sugar. Twenty-three healthy normal weight adolescents and young adults, ranging in age from 14-21, were studied. Neural systems implicated in decision-making and inhibitory control were engaged by having participants perform the IGT during fMRI scanning. The Youth/Adolescent Questionnaire, a food frequency questionnaire, was used to obtain daily food choices. Higher consumption of vegetables correlated with higher activity in prefrontal cortical regions, namely the left superior frontal gyrus (SFG, and lower activity in sub-cortical regions, namely the right insular cortex. In contrast, higher consumption of fatty and sugary snacks correlated with lower activity in the prefrontal regions, combined with higher activity in the sub-cortical, insular cortex.These results provide preliminary support for our hypotheses that unhealthy food choices in real life are reflected by neuronal changes in key neural systems involved in habits, decision-making and self-control processes. These findings have implications for the creation of decision-making based intervention strategies that promote healthier eating.

  3. Inverse simulation system for manual-controlled rendezvous and docking based on artificial neural network

    Science.gov (United States)

    Zhou, Wanmeng; Wang, Hua; Tang, Guojin; Guo, Shuai

    2016-09-01

    The time-consuming experimental method for handling qualities assessment cannot meet the increasing fast design requirements for the manned space flight. As a tool for the aircraft handling qualities research, the model-predictive-control structured inverse simulation (MPC-IS) has potential applications in the aerospace field to guide the astronauts' operations and evaluate the handling qualities more effectively. Therefore, this paper establishes MPC-IS for the manual-controlled rendezvous and docking (RVD) and proposes a novel artificial neural network inverse simulation system (ANN-IS) to further decrease the computational cost. The novel system was obtained by replacing the inverse model of MPC-IS with the artificial neural network. The optimal neural network was trained by the genetic Levenberg-Marquardt algorithm, and finally determined by the Levenberg-Marquardt algorithm. In order to validate MPC-IS and ANN-IS, the manual-controlled RVD experiments on the simulator were carried out. The comparisons between simulation results and experimental data demonstrated the validity of two systems and the high computational efficiency of ANN-IS.

  4. Evolving a neural olfactorimotor system in virtual and real olfactory environments.

    Science.gov (United States)

    Rhodes, Paul A; Anderson, Todd O

    2012-01-01

    To provide a platform to enable the study of simulated olfactory circuitry in context, we have integrated a simulated neural olfactorimotor system with a virtual world which simulates both computational fluid dynamics as well as a robotic agent capable of exploring the simulated plumes. A number of the elements which we developed for this purpose have not, to our knowledge, been previously assembled into an integrated system, including: control of a simulated agent by a neural olfactorimotor system; continuous interaction between the simulated robot and the virtual plume; the inclusion of multiple distinct odorant plumes and background odor; the systematic use of artificial evolution driven by olfactorimotor performance (e.g., time to locate a plume source) to specify parameter values; the incorporation of the realities of an imperfect physical robot using a hybrid model where a physical robot encounters a simulated plume. We close by describing ongoing work toward engineering a high dimensional, reversible, low power electronic olfactory sensor which will allow olfactorimotor neural circuitry evolved in the virtual world to control an autonomous olfactory robot in the physical world. The platform described here is intended to better test theories of olfactory circuit function, as well as provide robust odor source localization in realistic environments.

  5. TCSC Nonlinear Adaptive Damping Controller Design Based on RBF Neural Network to Enhance Power System Stability

    DEFF Research Database (Denmark)

    Yao, Wei; Fang, Jiakun; Zhao, Ping

    2013-01-01

    In this paper, a nonlinear adaptive damping controller based on radial basis function neural network (RBFNN), which can infinitely approximate to nonlinear system, is proposed for thyristor controlled series capacitor (TCSC). The proposed TCSC adaptive damping controller can not only have...... system and a four-machine two-area power system under different operating conditions in comparison with the lead-lag damping controller tuned by evolutionary algorithm (EA). Simulation results show that the proposed damping controller achieves good robust performance for damping the low frequency...

  6. Passivation and control of partially known SISO nonlinear systems via dynamic neural networks

    Directory of Open Access Journals (Sweden)

    Reyes-Reyes J.

    2000-01-01

    Full Text Available In this paper, an adaptive technique is suggested to provide the passivity property for a class of partially known SISO nonlinear systems. A simple Dynamic Neural Network (DNN, containing only two neurons and without any hidden-layers, is used to identify the unknown nonlinear system. By means of a Lyapunov-like analysis the new learning law for this DNN, guarantying both successful identification and passivation effects, is derived. Based on this adaptive DNN model, an adaptive feedback controller, serving for wide class of nonlinear systems with an a priori incomplete model description, is designed. Two typical examples illustrate the effectiveness of the suggested approach.

  7. Vestibular system and neural correlates of motion sickness

    Science.gov (United States)

    Miller, Alan D.

    1986-01-01

    Initial studies re-examine the role of certain central nervous system structures in the production of vestibular-induced vomiting and vomiting in general. All experiments were conducted using cats. Since these studies demonstrated that the essential role of various central structures in vestibular-induced vomiting is only poorly understood, efforts were re-directed to study the control of the effector muscles (diaphragm and abdominal muscles) that produce the pressure changes responsible for vomiting, with the goal of determining how this control mechanism is engaged during motion sickness. Experiments were conducted to localize the motoneurons that innervate the individual abdominal muscles and the portion of the diaphragm that surrounds the esophagus. A central question regarding respiratory muscle control during vomiting is whether these muscles are activated via the same brain stem pre-motor neurons that provide descending respiratory drive and/or by other descending input(s). In other experiments, the use of a combination of pitch and roll motions to produce motion sickness in unrestrained cats was evaluated. This stimulus combination can produce vomiting in only the most susceptible cats and is thus not as provacative a stimulus for cats as vertical linear acceleration.

  8. A Multilayer Feed Forward Small-World Neural Network Controller and Its Application on Electrohydraulic Actuation System

    Directory of Open Access Journals (Sweden)

    Xiaohu Li

    2013-01-01

    Full Text Available Being difficult to attain the precise mathematical models, traditional control methods such as proportional integral (PI and proportional integral differentiation (PID cannot meet the demands for real time and robustness when applied in some nonlinear systems. The neural network controller is a good replacement to overcome these shortcomings. However, the performance of neural network controller is directly determined by neural network model. In this paper, a new neural network model is constructed with a structure topology between the regular and random connection modes based on complex network, which simulates the brain neural network as far as possible, to design a better neural network controller. Then, a new controller is designed under small-world neural network model and is investigated in both linear and nonlinear systems control. The simulation results show that the new controller basing on small-world network model can improve the control precision by 30% in the case of system with random disturbance. Besides the good performance of the new controller in tracking square wave signals, which is demonstrated by the experiment results of direct drive electro-hydraulic actuation position control system, it works well on anti-interference performance.

  9. A low-cost multichannel wireless neural stimulation system for freely roaming animals

    Science.gov (United States)

    Alam, Monzurul; Chen, Xi; Fernandez, Eduardo

    2013-12-01

    Objectives. Electrical stimulation of nerve tissue and recording of neural activity are the basis of many therapies and neural prostheses. Conventional stimulation systems have a number of practical limitations, especially in experiments involving freely roaming subjects. Our main objective was to develop a modular, versatile and inexpensive multichannel wireless system able to overcome some of these constraints. Approach. We have designed and implemented a new multichannel wireless neural stimulator based on commercial components. The system is small (2 cm × 4 cm × 0.5 cm) and light in weight (9 g) which allows it to be easily carried in a small backpack. To test and validate the performance and reliability of the whole system we conducted several bench tests and in vivo experiments. Main results. The performance and accuracy of the stimulator were comparable to commercial threaded systems. Stimulation sequences can be constructed on-the-fly with 251 selectable current levels (from 0 to 250 µA) with 1 µA step resolution. The pulse widths and intervals can be as long as 65 ms in 2 µs time resolution. The system covers approximately 10 m of transmission range in a regular laboratory environment and 100 m in free space (line of sight). Furthermore it provides great flexibility for experiments since it allows full control of the stimulator and the stimulation parameters in real time. When there is no stimulation, the device automatically goes into low-power sleep mode to preserve battery power. Significance. We introduce the design of a powerful multichannel wireless stimulator assembled from commercial components. Key features of the system are their reliability, robustness and small size. The system has a flexible design that can be modified straightforwardly to tailor it to any specific experimental need. Furthermore it can be effortlessly adapted for use with any kind of multielectrode arrays.

  10. Stochastic resonance in FitzHugh-Nagumo neural system driven by correlated non-Gaussian noise and Gaussian noise

    Science.gov (United States)

    Guo, Yong-Feng; Xi, Bei; Wei, Fang; Tan, Jian-Guo

    2017-12-01

    In this paper, the phenomenon of stochastic resonance in FitzHugh-Nagumo (FHN) neural system driven by correlated non-Gaussian noise and Gaussian white noise is investigated. First, the analytical expression of the stationary probability distribution is derived by using the path integral approach and the unified colored noise approximation. Then, we obtain the expression of signal-to-noise ratio (SNR) by applying the theory of two-state model. The results show that the phenomena of stochastic resonance and multiple stochastic resonance appear in FHN neural system under different values of parameters. The effects of the multiplicative noise intensity D and the additive noise intensity Q on the SNR are entirely different. In addition, the discharge behavior of FHN neural system is restrained when the value of Q is smaller. But, it is conducive to enhance signal response of FHN neural system when the values of Q and D are relatively larger.

  11. Cancer and birth defects surveillance system for communities around the Savannah River Site. Annual progress report

    Energy Technology Data Exchange (ETDEWEB)

    Dunbar, J.B.

    1994-05-01

    The US DOE funded this grant to the Medical University of South Carolina for a cancer and birth defects registry for an initial three year period which was completed as of April 29, 1994. While this Technical Progress Report is prepared principally to document the activities of year 03, it also summarizes the accomplishments of the first two years in order to put into perspective the energy and progress of the program over the entire three year funding cycle.

  12. Healthcare system intervention for prevention of birth injuries - process evaluation of self-assessment, peer review, feedback and agreement for change

    National Research Council Canada - National Science Library

    Nyström, Monica E; Westerlund, Anna; Höög, Elisabet; Millde-Luthander, Charlotte; Högberg, Ulf; Grunewald, Charlotta

    2012-01-01

    Patient safety is fundamental in high quality healthcare systems but despite an excellent record of perinatal care in Sweden some children still suffer from substandard care and unnecessary birth injuries...

  13. Systemic lupus erythematosus and risk of preterm birth: a systematic review and meta-analysis of observational studies.

    Science.gov (United States)

    Wei, S; Lai, K; Yang, Z; Zeng, K

    2017-05-01

    We performed a meta-analysis to identify the association between systemic lupus erythematosus (SLE) and preterm birth. In this study, we studied the effects of SLE, SLE disease activity, a history of nephritis and active nephritis on preterm birth. Searches were conducted before 20 May 2016 of PubMed, Embase, Medline and Cochrane Library of literature and article reference lists. Eleven observational case-control studies and thirteen cohort studies met the inclusion criteria. The pooled relative risk (RR) for the risk of preterm birth in SLE patients versus controls was 2.05 (95% confidence interval (CI): 1.72-3.32); for active SLE patients versus inactive was 2.98 (95% CI: 2.32-3.83); for SLE patients with a history of lupus nephritis versus those without nephritis it was 1.62 (95% CI: 1.35-1.95); and for SLE patients with active nephritis versus those with quiescent nephritis it was 1.78 (95% CI: 1.17-2.70). In summary, this study identified a significant association in the above results. This association was more significant in active SLE patients versus inactive. With respect to SLE itself, active inflammation (such as disease activity) may be more hazardous for the management of the pregnancy. This suggests that it is essential to control disease activity in order to achieve a better outcome of SLE pregnancy.

  14. Social cognitive conflict resolution: Contributions of domain general and domain specific neural systems

    Science.gov (United States)

    Zaki, Jamil; Hennigan, Kelly; Weber, Jochen; Ochsner, Kevin N.

    2010-01-01

    Cognitive control mechanisms allow individuals to behave adaptively in the face of complex and sometimes conflicting information. While the neural bases of these control mechanisms have been examined in many contexts, almost no attention has been paid to their role in resolving conflicts between competing social cues, which is surprising, given that cognitive conflicts are part of many social interactions. Evidence about the neural processing of social information suggests that two systems—the mirror neuron system (MNS) and mental state attribution system (MSAS)—are specialized for processing nonverbal and contextual social cues, respectively. This could support a model of social cognitive conflict resolution in which competition between social cues would recruit domain-general cognitive control mechanisms, which in turn would bias processing towards the MNS or MSAS. Such biasing could also alter social behaviors, such as inferences made about the internal states of others. We tested this model by scanning participants using fMRI while they drew inferences about social targets' emotional states based on congruent or incongruent nonverbal and contextual social cues. Conflicts between social cues recruited the anterior cingulate and lateral prefrontal cortex, brain areas associated with domain-general control processes. This activation was accompanied by biasing of neural activity towards areas in the MNS or MSAS, which tracked, respectively, with perceivers' behavioral reliance on nonverbal or contextual cues when drawing inferences about targets' emotions. Together, these data provide evidence about both domain general and domain specific mechanisms involved in resolving social cognitive conflicts. PMID:20573895

  15. Learning from a carbon dioxide capture system dataset: Application of the piecewise neural network algorithm

    Directory of Open Access Journals (Sweden)

    Veronica Chan

    2017-03-01

    Full Text Available This paper presents the application of a neural network rule extraction algorithm, called the piece-wise linear artificial neural network or PWL-ANN algorithm, on a carbon capture process system dataset. The objective of the application is to enhance understanding of the intricate relationships among the key process parameters. The algorithm extracts rules in the form of multiple linear regression equations by approximating the sigmoid activation functions of the hidden neurons in an artificial neural network (ANN. The PWL-ANN algorithm overcomes the weaknesses of the statistical regression approach, in which accuracies of the generated predictive models are often not satisfactory, and the opaqueness of the ANN models. The results show that the generated PWL-ANN models have accuracies that are as high as the originally trained ANN models of the four datasets of the carbon capture process system. An analysis of the extracted rules and the magnitude of the coefficients in the equations revealed that the three most significant parameters of the CO2 production rate are the steam flow rate through reboiler, reboiler pressure, and the CO2 concentration in the flue gas.

  16. Indirect adaptive fuzzy wavelet neural network with self- recurrent consequent part for AC servo system.

    Science.gov (United States)

    Hou, Runmin; Wang, Li; Gao, Qiang; Hou, Yuanglong; Wang, Chao

    2017-09-01

    This paper proposes a novel indirect adaptive fuzzy wavelet neural network (IAFWNN) to control the nonlinearity, wide variations in loads, time-variation and uncertain disturbance of the ac servo system. In the proposed approach, the self-recurrent wavelet neural network (SRWNN) is employed to construct an adaptive self-recurrent consequent part for each fuzzy rule of TSK fuzzy model. For the IAFWNN controller, the online learning algorithm is based on back propagation (BP) algorithm. Moreover, an improved particle swarm optimization (IPSO) is used to adapt the learning rate. The aid of an adaptive SRWNN identifier offers the real-time gradient information to the adaptive fuzzy wavelet neural controller to overcome the impact of parameter variations, load disturbances and other uncertainties effectively, and has a good dynamic. The asymptotical stability of the system is guaranteed by using the Lyapunov method. The result of the simulation and the prototype test prove that the proposed are effective and suitable. Copyright © 2017. Published by Elsevier Ltd.

  17. Consensus-based distributed cooperative learning from closed-loop neural control systems.

    Science.gov (United States)

    Chen, Weisheng; Hua, Shaoyong; Zhang, Huaguang

    2015-02-01

    In this paper, the neural tracking problem is addressed for a group of uncertain nonlinear systems where the system structures are identical but the reference signals are different. This paper focuses on studying the learning capability of neural networks (NNs) during the control process. First, we propose a novel control scheme called distributed cooperative learning (DCL) control scheme, by establishing the communication topology among adaptive laws of NN weights to share their learned knowledge online. It is further proved that if the communication topology is undirected and connected, all estimated weights of NNs can converge to small neighborhoods around their optimal values over a domain consisting of the union of all state orbits. Second, as a corollary it is shown that the conclusion on the deterministic learning still holds in the decentralized adaptive neural control scheme where, however, the estimated weights of NNs just converge to small neighborhoods of the optimal values along their own state orbits. Thus, the learned controllers obtained by DCL scheme have the better generalization capability than ones obtained by decentralized learning method. A simulation example is provided to verify the effectiveness and advantages of the control schemes proposed in this paper.

  18. NONLINEAR SYSTEM MODELING USING SINGLE NEURON CASCADED NEURAL NETWORK FOR REAL-TIME APPLICATIONS

    Directory of Open Access Journals (Sweden)

    S. Himavathi

    2012-04-01

    Full Text Available Neural Networks (NN have proved its efficacy for nonlinear system modeling. NN based controllers and estimators for nonlinear systems provide promising alternatives to the conventional counterpart. However, NN models have to meet the stringent requirements on execution time for its effective use in real time applications. This requires the NN model to be structurally compact and computationally less complex. In this paper a parametric method of analysis is adopted to determine the compact and faster NN model among various neural network architectures. This work proves through analysis and examples that the Single Neuron Cascaded (SNC architecture is distinct in providing compact and simpler models requiring lower execution time. The unique structural growth of SNC architecture enables automation in design. The SNC Network is shown to combine the advantages of both single and multilayer neural network architectures. Extensive analysis on selected architectures and their models for four benchmark nonlinear theoretical plants and a practical application are tested. A performance comparison of the NN models is presented to demonstrate the superiority of the single neuron cascaded architecture for online real time applications.

  19. Intrusion Detection System Using Deep Neural Network for In-Vehicle Network Security

    Science.gov (United States)

    Kang, Min-Joo

    2016-01-01

    A novel intrusion detection system (IDS) using a deep neural network (DNN) is proposed to enhance the security of in-vehicular network. The parameters building the DNN structure are trained with probability-based feature vectors that are extracted from the in-vehicular network packets. For a given packet, the DNN provides the probability of each class discriminating normal and attack packets, and, thus the sensor can identify any malicious attack to the vehicle. As compared to the traditional artificial neural network applied to the IDS, the proposed technique adopts recent advances in deep learning studies such as initializing the parameters through the unsupervised pre-training of deep belief networks (DBN), therefore improving the detection accuracy. It is demonstrated with experimental results that the proposed technique can provide a real-time response to the attack with a significantly improved detection ratio in controller area network (CAN) bus. PMID:27271802

  20. Multi-layer holographic bifurcative neural network system for real-time adaptive EOS data analysis

    Science.gov (United States)

    Liu, Hua-Kuang; Huang, K. S.; Diep, J.

    1993-01-01

    Optical data processing techniques have the inherent advantage of high data throughout, low weight and low power requirements. These features are particularly desirable for onboard spacecraft in-situ real-time data analysis and data compression applications. the proposed multi-layer optical holographic neural net pattern recognition technique will utilize the nonlinear photorefractive devices for real-time adaptive learning to classify input data content and recognize unexpected features. Information can be stored either in analog or digital form in a nonlinear photofractive device. The recording can be accomplished in time scales ranging from milliseconds to microseconds. When a system consisting of these devices is organized in a multi-layer structure, a feedforward neural net with bifurcating data classification capability is formed. The interdisciplinary research will involve the collaboration with top digital computer architecture experts at the University of Southern California.

  1. Neural network approximation of nonlinearity in laser nano-metrology system based on TLMI

    Science.gov (United States)

    Olyaee, Saeed; Hamedi, Samaneh

    2011-02-01

    In this paper, an approach based on neural network (NN) for nonlinearity modeling in a nano-metrology system using three-longitudinal-mode laser heterodyne interferometer (TLMI) for length and displacement measurements is presented. We model nonlinearity errors that arise from elliptically and non-orthogonally polarized laser beams, rotational error in the alignment of laser head with respect to the polarizing beam splitter, rotational error in the alignment of the mixing polarizer, and unequal transmission coefficients in the polarizing beam splitter. Here we use a neural network algorithm based on the multi-layer perceptron (MLP) network. The simulation results show that multi-layer feed forward perceptron network is successfully applicable to real noisy interferometer signals.

  2. Intrusion Detection System Using Deep Neural Network for In-Vehicle Network Security.

    Science.gov (United States)

    Kang, Min-Joo; Kang, Je-Won

    2016-01-01

    A novel intrusion detection system (IDS) using a deep neural network (DNN) is proposed to enhance the security of in-vehicular network. The parameters building the DNN structure are trained with probability-based feature vectors that are extracted from the in-vehicular network packets. For a given packet, the DNN provides the probability of each class discriminating normal and attack packets, and, thus the sensor can identify any malicious attack to the vehicle. As compared to the traditional artificial neural network applied to the IDS, the proposed technique adopts recent advances in deep learning studies such as initializing the parameters through the unsupervised pre-training of deep belief networks (DBN), therefore improving the detection accuracy. It is demonstrated with experimental results that the proposed technique can provide a real-time response to the attack with a significantly improved detection ratio in controller area network (CAN) bus.

  3. Intrusion Detection System Using Deep Neural Network for In-Vehicle Network Security.

    Directory of Open Access Journals (Sweden)

    Min-Joo Kang

    Full Text Available A novel intrusion detection system (IDS using a deep neural network (DNN is proposed to enhance the security of in-vehicular network. The parameters building the DNN structure are trained with probability-based feature vectors that are extracted from the in-vehicular network packets. For a given packet, the DNN provides the probability of each class discriminating normal and attack packets, and, thus the sensor can identify any malicious attack to the vehicle. As compared to the traditional artificial neural network applied to the IDS, the proposed technique adopts recent advances in deep learning studies such as initializing the parameters through the unsupervised pre-training of deep belief networks (DBN, therefore improving the detection accuracy. It is demonstrated with experimental results that the proposed technique can provide a real-time response to the attack with a significantly improved detection ratio in controller area network (CAN bus.

  4. Trade-off between Multiple Constraints Enables Simultaneous Formation of Modules and Hubs in Neural Systems

    Science.gov (United States)

    Chen, Yuhan; Wang, Shengjun; Hilgetag, Claus C.; Zhou, Changsong

    2013-01-01

    The formation of the complex network architecture of neural systems is subject to multiple structural and functional constraints. Two obvious but apparently contradictory constraints are low wiring cost and high processing efficiency, characterized by short overall wiring length and a small average number of processing steps, respectively. Growing evidence shows that neural networks are results from a trade-off between physical cost and functional value of the topology. However, the relationship between these competing constraints and complex topology is not well understood quantitatively. We explored this relationship systematically by reconstructing two known neural networks, Macaque cortical connectivity and C. elegans neuronal connections, from combinatory optimization of wiring cost and processing efficiency constraints, using a control parameter , and comparing the reconstructed networks to the real networks. We found that in both neural systems, the reconstructed networks derived from the two constraints can reveal some important relations between the spatial layout of nodes and the topological connectivity, and match several properties of the real networks. The reconstructed and real networks had a similar modular organization in a broad range of , resulting from spatial clustering of network nodes. Hubs emerged due to the competition of the two constraints, and their positions were close to, and partly coincided, with the real hubs in a range of values. The degree of nodes was correlated with the density of nodes in their spatial neighborhood in both reconstructed and real networks. Generally, the rebuilt network matched a significant portion of real links, especially short-distant ones. These findings provide clear evidence to support the hypothesis of trade-off between multiple constraints on brain networks. The two constraints of wiring cost and processing efficiency, however, cannot explain all salient features in the real networks. The discrepancy

  5. Hybrid intelligence systems and artificial neural network (ANN approach for modeling of surface roughness in drilling

    Directory of Open Access Journals (Sweden)

    Ch. Sanjay

    2014-12-01

    Full Text Available In machining processes, drilling operation is material removal process that has been widely used in manufacturing since industrial revolution. The useful life of cutting tool and its operating conditions largely controls the economics of machining operations. Drilling is most frequently performed material removing process and is used as a preliminary step for many operations, such as reaming, tapping, and boring. Drill wear has a bad effect on the surface finish and dimensional accuracy of the work piece. The surface finish of a machined part is one of the most important quality characteristics in manufacturing industries. The primary objective of this research is the prediction of suitable parameters for surface roughness in drilling. Cutting speed, cutting force, and machining time were given as inputs to the adaptive fuzzy neural network and neuro-fuzzy analysis for estimating the values of surface roughness by using 2, 3, 4, and 5 membership functions. The best structures were selected based on minimum of summation of square with the actual values with the estimated values by artificial neural fuzzy inference system (ANFIS and neuro-fuzzy systems. For artificial neural network (ANN analysis, the number of neurons was selected from 1, 2, 3, … , 20. The learning rate was selected as .5 and .5 smoothing factor was used. The inputs were selected as cutting speed, feed, machining time, and thrust force. The best structures of neural networks were selected based on the criteria as the minimum of summation of square with the actual value of surface roughness. Drilling experiments with 10 mm size were performed at two cutting speeds and feeds. Comparative analysis has been done between the actual values and the estimated values obtained by ANFIS, neuro-fuzzy, and ANN analysis.

  6. Trade-off between multiple constraints enables simultaneous formation of modules and hubs in neural systems.

    Directory of Open Access Journals (Sweden)

    Yuhan Chen

    Full Text Available The formation of the complex network architecture of neural systems is subject to multiple structural and functional constraints. Two obvious but apparently contradictory constraints are low wiring cost and high processing efficiency, characterized by short overall wiring length and a small average number of processing steps, respectively. Growing evidence shows that neural networks are results from a trade-off between physical cost and functional value of the topology. However, the relationship between these competing constraints and complex topology is not well understood quantitatively. We explored this relationship systematically by reconstructing two known neural networks, Macaque cortical connectivity and C. elegans neuronal connections, from combinatory optimization of wiring cost and processing efficiency constraints, using a control parameter α, and comparing the reconstructed networks to the real networks. We found that in both neural systems, the reconstructed networks derived from the two constraints can reveal some important relations between the spatial layout of nodes and the topological connectivity, and match several properties of the real networks. The reconstructed and real networks had a similar modular organization in a broad range of α, resulting from spatial clustering of network nodes. Hubs emerged due to the competition of the two constraints, and their positions were close to, and partly coincided, with the real hubs in a range of α values. The degree of nodes was correlated with the density of nodes in their spatial neighborhood in both reconstructed and real networks. Generally, the rebuilt network matched a significant portion of real links, especially short-distant ones. These findings provide clear evidence to support the hypothesis of trade-off between multiple constraints on brain networks. The two constraints of wiring cost and processing efficiency, however, cannot explain all salient features in the real

  7. Point-and-click cursor control with an intracortical neural interface system by humans with tetraplegia.

    Science.gov (United States)

    Kim, Sung-Phil; Simeral, John D; Hochberg, Leigh R; Donoghue, John P; Friehs, Gerhard M; Black, Michael J

    2011-04-01

    We present a point-and-click intracortical neural interface system (NIS) that enables humans with tetraplegia to volitionally move a 2-D computer cursor in any desired direction on a computer screen, hold it still, and click on the area of interest. This direct brain-computer interface extracts both discrete (click) and continuous (cursor velocity) signals from a single small population of neurons in human motor cortex. A key component of this system is a multi-state probabilistic decoding algorithm that simultaneously decodes neural spiking activity of a small population of neurons and outputs either a click signal or the velocity of the cursor. The algorithm combines a linear classifier, which determines whether the user is intending to click or move the cursor, with a Kalman filter that translates the neural population activity into cursor velocity. We present a paradigm for training the multi-state decoding algorithm using neural activity observed during imagined actions. Two human participants with tetraplegia (paralysis of the four limbs) performed a closed-loop radial target acquisition task using the point-and-click NIS over multiple sessions. We quantified point-and-click performance using various human-computer interaction measurements for pointing devices. We found that participants could control the cursor motion and click on specified targets with a small error rate (one participant). This study suggests that signals from a small ensemble of motor cortical neurons (∼40) can be used for natural point-and-click 2-D cursor control of a personal computer.

  8. Designing an artificial neural network for prediction of pregnancy outcomes in women with systemic lupus erythematosus in Iran

    Directory of Open Access Journals (Sweden)

    Mahmoud Akbarian

    2015-07-01

    Results: Twelve features with P<0.05 and four features with P<0.1 were identified by using binary logistic regression as effective features. These sixteen features were used as input variables in artificial neural networks. The accuracy, sensitivity and specificity of the test data for the MLP network were 90.9%, 80.0%, and 94.1% respectively and for the total data were 97.3%, 93.5%, and 99.0% respectively. Conclusion: According to the results, we concluded that feed-forward Multi-Layer Perceptron (MLP neural network with scaled conjugate gradient (trainscg back propagation learning algorithm can help physicians to predict the pregnancy outcomes (spontaneous abortion and live birth among pregnant women with lupus by using identified effective variables.

  9. Convergence of Asymptotic Systems of Non-autonomous Neural Network Models with Infinite Distributed Delays

    Science.gov (United States)

    Oliveira, José J.

    2017-10-01

    In this paper, we investigate the global convergence of solutions of non-autonomous Hopfield neural network models with discrete time-varying delays, infinite distributed delays, and possible unbounded coefficient functions. Instead of using Lyapunov functionals, we explore intrinsic features between the non-autonomous systems and their asymptotic systems to ensure the boundedness and global convergence of the solutions of the studied models. Our results are new and complement known results in the literature. The theoretical analysis is illustrated with some examples and numerical simulations.

  10. Nonlinear System Identification Using Neural Networks Trained with Natural Gradient Descent

    Directory of Open Access Journals (Sweden)

    Ibnkahla Mohamed

    2003-01-01

    Full Text Available We use natural gradient (NG learning neural networks (NNs for modeling and identifying nonlinear systems with memory. The nonlinear system is comprised of a discrete-time linear filter followed by a zero-memory nonlinearity . The NN model is composed of a linear adaptive filter followed by a two-layer memoryless nonlinear NN. A Kalman filter-based technique and a search-and-converge method have been employed for the NG algorithm. It is shown that the NG descent learning significantly outperforms the ordinary gradient descent and the Levenberg-Marquardt (LM procedure in terms of convergence speed and mean squared error (MSE performance.

  11. Intelligent control of robotic arm/hand systems for the NASA EVA retriever using neural networks

    Science.gov (United States)

    Mclauchlan, Robert A.

    1989-01-01

    Adaptive/general learning algorithms using varying neural network models are considered for the intelligent control of robotic arm plus dextrous hand/manipulator systems. Results are summarized and discussed for the use of the Barto/Sutton/Anderson neuronlike, unsupervised learning controller as applied to the stabilization of an inverted pendulum on a cart system. Recommendations are made for the application of the controller and a kinematic analysis for trajectory planning to simple object retrieval (chase/approach and capture/grasp) scenarios in two dimensions.

  12. Backstepping fuzzy-neural-network control design for hybrid maglev transportation system.

    Science.gov (United States)

    Wai, Rong-Jong; Yao, Jing-Xiang; Lee, Jeng-Dao

    2015-02-01

    This paper focuses on the design of a backstepping fuzzy-neural-network control (BFNNC) for the online levitated balancing and propulsive positioning of a hybrid magnetic levitation (maglev) transportation system. The dynamic model of the hybrid maglev transportation system including levitated hybrid electromagnets to reduce the suspension power loss and the friction force during linear movement and a propulsive linear induction motor based on the concepts of mechanical geometry and motion dynamics is first constructed. The ultimate goal is to design an online fuzzy neural network (FNN) control methodology to cope with the problem of the complicated control transformation and the chattering control effort in backstepping control (BSC) design, and to directly ensure the stability of the controlled system without the requirement of strict constraints, detailed system information, and auxiliary compensated controllers despite the existence of uncertainties. In the proposed BFNNC scheme, an FNN control is utilized to be the major control role by imitating the BSC strategy, and adaptation laws for network parameters are derived in the sense of projection algorithm and Lyapunov stability theorem to ensure the network convergence as well as stable control performance. The effectiveness of the proposed control strategy for the hybrid maglev transportation system is verified by experimental results, and the superiority of the BFNNC scheme is indicated in comparison with the BSC strategy and the backstepping particle-swarm-optimization control system in previous research.

  13. Dynamical systems, attractors, and neural circuits [version 1; referees: 3 approved

    Directory of Open Access Journals (Sweden)

    Paul Miller

    2016-05-01

    Full Text Available Biology is the study of dynamical systems. Yet most of us working in biology have limited pedagogical training in the theory of dynamical systems, an unfortunate historical fact that can be remedied for future generations of life scientists. In my particular field of systems neuroscience, neural circuits are rife with nonlinearities at all levels of description, rendering simple methodologies and our own intuition unreliable. Therefore, our ideas are likely to be wrong unless informed by good models. These models should be based on the mathematical theories of dynamical systems since functioning neurons are dynamic—they change their membrane potential and firing rates with time. Thus, selecting the appropriate type of dynamical system upon which to base a model is an important first step in the modeling process. This step all too easily goes awry, in part because there are many frameworks to choose from, in part because the sparsely sampled data can be consistent with a variety of dynamical processes, and in part because each modeler has a preferred modeling approach that is difficult to move away from. This brief review summarizes some of the main dynamical paradigms that can arise in neural circuits, with comments on what they can achieve computationally and what signatures might reveal their presence within empirical data. I provide examples of different dynamical systems using simple circuits of two or three cells, emphasizing that any one connectivity pattern is compatible with multiple, diverse functions.

  14. Hybrid neural intelligent system to predict business failure in small-to-medium-size enterprises.

    Science.gov (United States)

    Borrajo, M Lourdes; Baruque, Bruno; Corchado, Emilio; Bajo, Javier; Corchado, Juan M

    2011-08-01

    During the last years there has been a growing need of developing innovative tools that can help small to medium sized enterprises to predict business failure as well as financial crisis. In this study we present a novel hybrid intelligent system aimed at monitoring the modus operandi of the companies and predicting possible failures. This system is implemented by means of a neural-based multi-agent system that models the different actors of the companies as agents. The core of the multi-agent system is a type of agent that incorporates a case-based reasoning system and automates the business control process and failure prediction. The stages of the case-based reasoning system are implemented by means of web services: the retrieval stage uses an innovative weighted voting summarization of self-organizing maps ensembles-based method and the reuse stage is implemented by means of a radial basis function neural network. An initial prototype was developed and the results obtained related to small and medium enterprises in a real scenario are presented.

  15. A hybrid fuzzy-neural system for computer-aided diagnosis of ultrasound kidney images using prominent features.

    Science.gov (United States)

    Bommanna Raja, K; Madheswaran, M; Thyagarajah, K

    2008-02-01

    The objective of this work is to develop and implement a computer-aided decision support system for an automated diagnosis and classification of ultrasound kidney images. The proposed method distinguishes three kidney categories namely normal, medical renal diseases and cortical cyst. For the each pre-processed ultrasound kidney image, 36 features are extracted. Two types of decision support systems, optimized multi-layer back propagation network and hybrid fuzzy-neural system have been developed with these features for classifying the kidney categories. The performance of the hybrid fuzzy-neural system is compared with the optimized multi-layer back propagation network in terms of classification efficiency, training and testing time. The results obtained show that fuzzy-neural system provides higher classification efficiency with minimum training and testing time. It has also been found that instead of using all 36 features, ranking the features enhance classification efficiency. The outputs of the decision support systems are validated with medical expert to measure the actual efficiency. The overall discriminating capability of the systems is accessed with performance evaluation measure, f-score. It has been observed that the performance of fuzzy-neural system is superior compared to optimized multi-layer back propagation network. Such hybrid fuzzy-neural system with feature extraction algorithms and pre-processing scheme helps in developing computer-aided diagnosis system for ultrasound kidney images and can be used as a secondary observer in clinical decision making.

  16. A MapReduce Based High Performance Neural Network in Enabling Fast Stability Assessment of Power Systems

    Directory of Open Access Journals (Sweden)

    Yang Liu

    2017-01-01

    Full Text Available Transient stability assessment is playing a vital role in modern power systems. For this purpose, machine learning techniques have been widely employed to find critical conditions and recognize transient behaviors based on massive data analysis. However, an ever increasing volume of data generated from power systems poses a number of challenges to traditional machine learning techniques, which are computationally intensive running on standalone computers. This paper presents a MapReduce based high performance neural network to enable fast stability assessment of power systems. Hadoop, which is an open-source implementation of the MapReduce model, is first employed to parallelize the neural network. The parallel neural network is further enhanced with HaLoop to reduce the computation overhead incurred in the iteration process of the neural network. In addition, ensemble techniques are employed to accommodate the accuracy loss of the parallelized neural network in classification. The parallelized neural network is evaluated with both the IEEE 68-node system and a real power system from the aspects of computation speedup and stability assessment.

  17. A Novel Robot System Integrating Biological and Mechanical Intelligence Based on Dissociated Neural Network-Controlled Closed-Loop Environment.

    Directory of Open Access Journals (Sweden)

    Yongcheng Li

    Full Text Available We propose the architecture of a novel robot system merging biological and artificial intelligence based on a neural controller connected to an external agent. We initially built a framework that connected the dissociated neural network to a mobile robot system to implement a realistic vehicle. The mobile robot system characterized by a camera and two-wheeled robot was designed to execute the target-searching task. We modified a software architecture and developed a home-made stimulation generator to build a bi-directional connection between the biological and the artificial components via simple binomial coding/decoding schemes. In this paper, we utilized a specific hierarchical dissociated neural network for the first time as the neural controller. Based on our work, neural cultures were successfully employed to control an artificial agent resulting in high performance. Surprisingly, under the tetanus stimulus training, the robot performed better and better with the increasement of training cycle because of the short-term plasticity of neural network (a kind of reinforced learning. Comparing to the work previously reported, we adopted an effective experimental proposal (i.e. increasing the training cycle to make sure of the occurrence of the short-term plasticity, and preliminarily demonstrated that the improvement of the robot's performance could be caused independently by the plasticity development of dissociated neural network. This new framework may provide some possible solutions for the learning abilities of intelligent robots by the engineering application of the plasticity processing of neural networks, also for the development of theoretical inspiration for the next generation neuro-prostheses on the basis of the bi-directional exchange of information within the hierarchical neural networks.

  18. A Novel Robot System Integrating Biological and Mechanical Intelligence Based on Dissociated Neural Network-Controlled Closed-Loop Environment

    Science.gov (United States)

    Wang, Yuechao; Li, Hongyi; Zheng, Xiongfei

    2016-01-01

    We propose the architecture of a novel robot system merging biological and artificial intelligence based on a neural controller connected to an external agent. We initially built a framework that connected the dissociated neural network to a mobile robot system to implement a realistic vehicle. The mobile robot system characterized by a camera and two-wheeled robot was designed to execute the target-searching task. We modified a software architecture and developed a home-made stimulation generator to build a bi-directional connection between the biological and the artificial components via simple binomial coding/decoding schemes. In this paper, we utilized a specific hierarchical dissociated neural network for the first time as the neural controller. Based on our work, neural cultures were successfully employed to control an artificial agent resulting in high performance. Surprisingly, under the tetanus stimulus training, the robot performed better and better with the increasement of training cycle because of the short-term plasticity of neural network (a kind of reinforced learning). Comparing to the work previously reported, we adopted an effective experimental proposal (i.e. increasing the training cycle) to make sure of the occurrence of the short-term plasticity, and preliminarily demonstrated that the improvement of the robot’s performance could be caused independently by the plasticity development of dissociated neural network. This new framework may provide some possible solutions for the learning abilities of intelligent robots by the engineering application of the plasticity processing of neural networks, also for the development of theoretical inspiration for the next generation neuro-prostheses on the basis of the bi-directional exchange of information within the hierarchical neural networks. PMID:27806074

  19. Using neural networks and Dyna algorithm for integrated planning, reacting and learning in systems

    Science.gov (United States)

    Lima, Pedro; Beard, Randal

    1992-01-01

    The traditional AI answer to the decision making problem for a robot is planning. However, planning is usually CPU-time consuming, depending on the availability and accuracy of a world model. The Dyna system generally described in earlier work, uses trial and error to learn a world model which is simultaneously used to plan reactions resulting in optimal action sequences. It is an attempt to integrate planning, reactive, and learning systems. The architecture of Dyna is presented. The different blocks are described. There are three main components of the system. The first is the world model used by the robot for internal world representation. The input of the world model is the current state and the action taken in the current state. The output is the corresponding reward and resulting state. The second module in the system is the policy. The policy observes the current state and outputs the action to be executed by the robot. At the beginning of program execution, the policy is stochastic and through learning progressively becomes deterministic. The policy decides upon an action according to the output of an evaluation function, which is the third module of the system. The evaluation function takes the following as input: the current state of the system, the action taken in that state, the resulting state, and a reward generated by the world which is proportional to the current distance from the goal state. Originally, the work proposed was as follows: (1) to implement a simple 2-D world where a 'robot' is navigating around obstacles, to learn the path to a goal, by using lookup tables; (2) to substitute the world model and Q estimate function Q by neural networks; and (3) to apply the algorithm to a more complex world where the use of a neural network would be fully justified. In this paper, the system design and achieved results will be described. First we implement the world model with a neural network and leave Q implemented as a look up table. Next, we use a

  20. Diagonal recurrent neural network based adaptive control of nonlinear dynamical systems using lyapunov stability criterion.

    Science.gov (United States)

    Kumar, Rajesh; Srivastava, Smriti; Gupta, J R P

    2017-03-01

    In this paper adaptive control of nonlinear dynamical systems using diagonal recurrent neural network (DRNN) is proposed. The structure of DRNN is a modification of fully connected recurrent neural network (FCRNN). Presence of self-recurrent neurons in the hidden layer of DRNN gives it an ability to capture the dynamic behaviour of the nonlinear plant under consideration (to be controlled). To ensure stability, update rules are developed using lyapunov stability criterion. These rules are then used for adjusting the various parameters of DRNN. The responses of plants obtained with DRNN are compared with those obtained when multi-layer feed forward neural network (MLFFNN) is used as a controller. Also, in example 4, FCRNN is also investigated and compared with DRNN and MLFFNN. Robustness of the proposed control scheme is also tested against parameter variations and disturbance signals. Four simulation examples including one-link robotic manipulator and inverted pendulum are considered on which the proposed controller is applied. The results so obtained show the superiority of DRNN over MLFFNN as a controller. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

  1. Prediction of Groundwater Arsenic Contamination using Geographic Information System and Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Md. Moqbul Hossain

    2013-01-01

    Full Text Available Ground water arsenic contamination is a well known health and environmental problem in Bangladesh. Sources of this heavy metal are known to be geogenic, however, the processes of its release into groundwater are poorly understood phenomena. In quest of mitigation of the problem it is necessary to predict probable contamination before it causes any damage to human health. Hence our research has been carried out to find the factor relations of arsenic contamination and develop an arsenic contamination prediction model. Researchers have generally agreed that the elevated concentration of arsenic is affected by several factors such as soil reaction (pH, organic matter content, geology, iron content, etc. However, the variability of concentration within short lateral and vertical intervals, and the inter-relationships of variables among themselves, make the statistical analyses highly non-linear and difficult to converge with a meaningful relationship. Artificial Neural Networks (ANN comes in handy for such a black box type problem. This research uses Back propagation Neural Networks (BPNN to train and validate the data derived from Geographic Information System (GIS spatial distribution grids. The neural network architecture with (6-20-1 pattern was able to predict the arsenic concentration with reasonable accuracy.

  2. Neural synchrony within the motor system: what have we learned so far?

    Directory of Open Access Journals (Sweden)

    Bernadette C. M. van Wijk

    2012-09-01

    Full Text Available Synchronization of neural activity is considered essential for information processing in the nervous system. Both local and inter-regional synchronization are omnipresent in different frequency regimes and relate to a variety of behavioral and cognitive functions. Over the years, many studies have sought to elucidate the question how alpha/mu, beta, and gamma synchronization contribute to motor control. Here, we review these studies with the purpose to delineate what they have added to our understanding of the neural control of movement. We highlight important findings regarding oscillations in primary motor cortex, synchronization between cortex and spinal cord, synchronization between cortical regions, as well as abnormal synchronization patterns in a selection of motor dysfunctions. The interpretation of synchronization patterns benefits from combining results of invasive and non-invasive recordings, different data analysis tools, and modeling work. Importantly, although synchronization is deemed to play a vital role, it is not the only mechanism for neural communication. Spike timing and rate coding act together during motor control and should therefore both be accounted for when interpreting movement-related activity.

  3. Toward a distributed free-floating wireless implantable neural recording system.

    Science.gov (United States)

    Pyungwoo Yeon; Xingyuan Tong; Byunghun Lee; Mirbozorgi, Abdollah; Ash, Bruce; Eckhardt, Helmut; Ghovanloo, Maysam

    2016-08-01

    To understand the complex correlations between neural networks across different regions in the brain and their functions at high spatiotemporal resolution, a tool is needed for obtaining long-term single unit activity (SUA) across the entire brain area. The concept and preliminary design of a distributed free-floating wireless implantable neural recording (FF-WINeR) system are presented, which can enabling SUA acquisition by dispersedly implanting tens to hundreds of untethered 1 mm3 neural recording probes, floating with the brain and operating wirelessly across the cortical surface. For powering FF-WINeR probes, a 3-coil link with an intermediate high-Q resonator provides a minimum S21 of -22.22 dB (in the body medium) and -21.23 dB (in air) at 2.8 cm coil separation, which translates to 0.76%/759 μW and 0.6%/604 μW of power transfer efficiency (PTE) / power delivered to a 9 kΩ load (PDL), in body and air, respectively. A mock-up FF-WINeR is implemented to explore microassembly method of the 1×1 mm2 micromachined silicon die with a bonding wire-wound coil and a tungsten micro-wire electrode. Circuit design methods to fit the active circuitry in only 0.96 mm2 of die area in a 130 nm standard CMOS process, and satisfy the strict power and performance requirements (in simulations) are discussed.

  4. Design a PID Controller for Suspension System by Back Propagation Neural Network

    Directory of Open Access Journals (Sweden)

    M. Heidari

    2013-01-01

    Full Text Available This paper presents a neural network for designing of a PID controller for suspension system. The suspension system, designed as a quarter model, is used to simplify the problem to one-dimensional spring-damper system. In this paper, back propagation neural network (BPN has been used for determining the gain parameters of a PID controller for suspension system of automotive. The BPN method is found to be the most accurate and quick. The best results were obtained by the BPN by Levenberg-Marquardt algorithm training with 10 neurons in the one hidden layer. Training was continued until the mean squared error is less than . Desired error value was achieved in the BPN, and the BPN was tested with both data used and not used for training. By training of this network, it is possible to estimate the gain parameters of PID controller at any condition. The inputs of network are automotive velocity, overshoot percentage, settling time, and steady state error of suspension system response. Also outputs of the net are the gain parameters of PID controller. Resultant low relative error value of the ANN model indicates the usability of the BPN in this area.

  5. The cerebellum: A neural system for the study of reinforcement learning

    Directory of Open Access Journals (Sweden)

    Rodney A. Swain

    2011-03-01

    Full Text Available In its strictest application, the term reinforcement learning refers to a computational approach to learning in which an agent (often a machine interacts with a mutable environment to maximize reward through trial and error. The approach borrows essentials from several fields, most notably Computer Science, Behavioral Neuroscience, and Psychology. At the most basic level, a neural system capable of mediating reinforcement learning must be able to acquire sensory information about the external environment and internal milieu (either directly or through connectivities with other brain regions, must be able to select a behavior to be executed, and must be capable of providing evaluative feedback about the success of that behavior. Given that Psychology informs us that reinforcers, both positive and negative, are stimuli or consequences that increase the probability that the immediately antecedent behavior will be repeated and that reinforcer strength or viability is modulated by the organism’s past experience with the reinforcer, its affect, and even the state of its muscles (e.g., eyes open or closed; it is the case that any neural system that supports reinforcement learning must also be sensitive to these same considerations. Once learning is established, such a neural system must finally be able to maintain continued response expression and prevent response drift. In this report, we examine both historical and recent evidence that the cerebellum satisfies all of these requirements. While we report evidence from a variety of learning paradigms, the majority of our discussion will focus on classical conditioning of the rabbit eye blink response as an ideal model system for the study of reinforcement and reinforcement learning.

  6. A new approach for sizing stand alone photovoltaic systems based in neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Hontoria, L.; Aguilera, J. [Universidad de Jaen, Dept. de Electronica, Jaen (Spain); Zufiria, P. [UPM Ciudad Universitaria, Dept. de Matematica Aplicada a las Tecnologias de la Informacion, Madrid (Spain)

    2005-02-01

    Several methods for sizing stand alone photovoltaic (pv) systems has been developed. The more simplistic are called intuitive methods. They are a useful tool for a first approach in sizing stand alone photovoltaic systems. Nevertheless they are very inaccurate. Analytical methods use equations to describe the pv system size as a function of reliability. These ones are more accurate than the previous ones but they are also not accurate enough for sizing of high reliability. In a third group there are methods which use system simulations. These ones are called numerical methods. Many of the analytical methods employ the concept of reliability of the system or the complementary term: loss of load probability (LOLP). In this paper an improvement for obtaining LOLP curves based on the neural network called Multilayer Perceptron (MLP) is presented. A unique MLP for many locations of Spain has been trained and after the training, the MLP is able to generate LOLP curves for any value and location. (Author)

  7. Neural basis of attachment-caregiving systems interaction: insights from neuroimaging studies

    Science.gov (United States)

    Lenzi, Delia; Trentini, Cristina; Tambelli, Renata; Pantano, Patrizia

    2015-01-01

    The attachment and the caregiving system are complementary systems which are active simultaneously in infant and mother interactions. This ensures the infant survival and optimal social, emotional, and cognitive development. In this brief review we first define the characteristics of these two behavioral systems and the theory that links them, according to what Bowlby called the “attachment-caregiving social bond” (Bowlby, 1969). We then follow with those neuroimaging studies that have focused on this particular issue, i.e., those which have studied the activation of the careging system in women (using infant stimuli) and have explored how the individual attachment model (through the Adult Attachment Interview) modulates its activity. Studies report altered activation in limbic and prefrontal areas and in basal ganglia and hypothalamus/pituitary regions. These altered activations are thought to be the neural substrate of the attachment-caregiving systems interaction. PMID:26379578

  8. Neural-network-observer-based optimal control for unknown nonlinear systems using adaptive dynamic programming

    Science.gov (United States)

    Liu, Derong; Huang, Yuzhu; Wang, Ding; Wei, Qinglai

    2013-09-01

    In this paper, an observer-based optimal control scheme is developed for unknown nonlinear systems using adaptive dynamic programming (ADP) algorithm. First, a neural-network (NN) observer is designed to estimate system states. Then, based on the observed states, a neuro-controller is constructed via ADP method to obtain the optimal control. In this design, two NN structures are used: a three-layer NN is used to construct the observer which can be applied to systems with higher degrees of nonlinearity and without a priori knowledge of system dynamics, and a critic NN is employed to approximate the value function. The optimal control law is computed using the critic NN and the observer NN. Uniform ultimate boundedness of the closed-loop system is guaranteed. The actor, critic, and observer structures are all implemented in real-time, continuously and simultaneously. Finally, simulation results are presented to demonstrate the effectiveness of the proposed control scheme.

  9. Neural basis of attachment-caregiving systems interaction:insights from neuroimaging

    Directory of Open Access Journals (Sweden)

    Delia eLenzi

    2015-08-01

    Full Text Available The attachment and the caregiving system are complementary systems which are active simultaneously in infant and mother interactions. This ensures the infant survival and optimal social, emotional and cognitive development. In this brief review we first define the characteristics of these two behavioral systems and the theory that links them, according to what Bowlby called the attachment-caregiving social bond (Bowlby, 1969. We then follow with those neuroimaging studies that have focused on this particular issue, i.e. those which have studied the activation of the careging system in women (using infant stimuli and have explored how the individual attachment model (through the Adult Attachment Interview modulates its activity. Studies report altered activation in limbic and prefrontal areas and in basal ganglia and hypothalamus/pituitary regions. These altered activations are thought to be the neural substrate of the attachment-caregiving systems interaction.

  10. Reactive Power based Model Reference Neural Learning Adaptive System for Speed Estimation in Sensor-less Induction Motor Drives

    Directory of Open Access Journals (Sweden)

    K Sedhuraman

    2012-12-01

    Full Text Available In this paper, a novel reactive power based model reference neural learning adaptive system (RP-MRNLAS is proposed. The model reference adaptive system (MRAS based speed estimation is one of the most popular methods used for sensor-less controlled induction motor drives. In conventional MRAS, the error adaptation is done using a Proportional-integral-(PI. The non-linear mapping capability of a neural network (NN and the powerful learning algorithms have increased the applications of NN in power electronics and drives. Thus, a neural learning algorithm is used for the adaptation mechanism in MRAS and is often referred to as a model reference neural learning adaptive system (MRNLAS. In MRNLAS, the error between the reference and neural learning adaptive models is back propagated to adjust the weights of the neural network for rotor speed estimation. The two different methods of MRNLAS are flux based (RF-MRNLAS and reactive power based (RP-MRNLAS. The reactive power- based methods are simple and free from integral equations as compared to flux based methods. The advantage of the reactive power based method and the NN learning algorithms are exploited in this work to yield a RPMRNLAS. The performance of the proposed RP-MRNLAS is analyzed extensively. The proposed RP-MRNLAS is compared in terms of accuracy and integrator drift problems with popular rotor flux-based MRNLAS for the same system and validated through Matlab/Simulink. The superiority of the RP- MRNLAS technique is demonstrated

  11. A VLSI field-programmable mixed-signal array to perform neural signal processing and neural modeling in a prosthetic system.

    Science.gov (United States)

    Bamford, Simeon A; Hogri, Roni; Giovannucci, Andrea; Taub, Aryeh H; Herreros, Ivan; Verschure, Paul F M J; Mintz, Matti; Del Giudice, Paolo

    2012-07-01

    A very-large-scale integration field-programmable mixed-signal array specialized for neural signal processing and neural modeling has been designed. This has been fabricated as a core on a chip prototype intended for use in an implantable closed-loop prosthetic system aimed at rehabilitation of the learning of a discrete motor response. The chosen experimental context is cerebellar classical conditioning of the eye-blink response. The programmable system is based on the intimate mixing of switched capacitor analog techniques with low speed digital computation; power saving innovations within this framework are presented. The utility of the system is demonstrated by the implementation of a motor classical conditioning model applied to eye-blink conditioning in real time with associated neural signal processing. Paired conditioned and unconditioned stimuli were repeatedly presented to an anesthetized rat and recordings were taken simultaneously from two precerebellar nuclei. These paired stimuli were detected in real time from this multichannel data. This resulted in the acquisition of a trigger for a well-timed conditioned eye-blink response, and repetition of unpaired trials constructed from the same data led to the extinction of the conditioned response trigger, compatible with natural cerebellar learning in awake animals.

  12. System Identification of a Nonlinear Multivariable Steam Generator Power Plant Using Time Delay and Wavelet Neural Networks

    Directory of Open Access Journals (Sweden)

    Laila Khalilzadeh Ganjali-khani

    2013-01-01

    Full Text Available One of the most effective strategies for steam generator efficiency enhancement is to improve the control system. For such an improvement, it is essential to have an accurate model for the steam generator of power plant. In this paper, an industrial steam generator is considered as a nonlinear multivariable system for identification. An important step in nonlinear system identification is the development of a nonlinear model. In recent years, artificial neural networks have been successfully used for identification of nonlinear systems in many researches. Wavelet neural networks (WNNs also are used as a powerful tool for nonlinear system identification. In this paper we present a time delay neural network model and a WNN model in order to identify an industrial steam generator. Simulation results show the effectiveness of the proposed models in the system identification and demonstrate that the WNN model is more precise to estimate the plant outputs.

  13. Preterm Birth

    Science.gov (United States)

    ... 3286 After hours (404) 639-2888 Contact Media Preterm Birth Recommend on Facebook Tweet Share Compartir Preterm ... Their Families What are the warning signs of preterm labor? In most cases, preterm labor (labor that ...

  14. Birthing Classes

    Science.gov (United States)

    ... class setting. Some hospitals are beginning to offer online courses, too.Expect to be required to pay ... called The Mongan Method, HypnoBirthing helps women learn self-hypnosis techniques to deliver their babies in a ...

  15. Birth Plans

    Science.gov (United States)

    ... and planning to shop for baby clothes. The reality of labor and birth may seem extremely far ... all women in labor, but many now show increased flexibility in how they handle their patients. Some ...

  16. Adaptive Neural-Sliding Mode Control of Active Suspension System for Camera Stabilization

    Directory of Open Access Journals (Sweden)

    Feng Zhao

    2015-01-01

    Full Text Available The camera always suffers from image instability on the moving vehicle due to the unintentional vibrations caused by road roughness. This paper presents a novel adaptive neural network based on sliding mode control strategy to stabilize the image captured area of the camera. The purpose is to suppress vertical displacement of sprung mass with the application of active suspension system. Since the active suspension system has nonlinear and time varying characteristics, adaptive neural network (ANN is proposed to make the controller robustness against systematic uncertainties, which release the model-based requirement of the sliding model control, and the weighting matrix is adjusted online according to Lyapunov function. The control system consists of two loops. The outer loop is a position controller designed with sliding mode strategy, while the PID controller in the inner loop is to track the desired force. The closed loop stability and asymptotic convergence performance can be guaranteed on the basis of the Lyapunov stability theory. Finally, the simulation results show that the employed controller effectively suppresses the vibration of the camera and enhances the stabilization of the entire camera, where different excitations are considered to validate the system performance.

  17. Surface Casting Defects Inspection Using Vision System and Neural Network Techniques

    Directory of Open Access Journals (Sweden)

    Świłło S.J.

    2013-12-01

    Full Text Available The paper presents a vision based approach and neural network techniques in surface defects inspection and categorization. Depending on part design and processing techniques, castings may develop surface discontinuities such as cracks and pores that greatly influence the material’s properties Since the human visual inspection for the surface is slow and expensive, a computer vision system is an alternative solution for the online inspection. The authors present the developed vision system uses an advanced image processing algorithm based on modified Laplacian of Gaussian edge detection method and advanced lighting system. The defect inspection algorithm consists of several parameters that allow the user to specify the sensitivity level at which he can accept the defects in the casting. In addition to the developed image processing algorithm and vision system apparatus, an advanced learning process has been developed, based on neural network techniques. Finally, as an example three groups of defects were investigated demonstrates automatic selection and categorization of the measured defects, such as blowholes, shrinkage porosity and shrinkage cavity.

  18. Neural reflex regulation of systemic inflammation: potential new targets for sepsis therapy.

    Directory of Open Access Journals (Sweden)

    Ricardo eFernandez

    2014-12-01

    Full Text Available Sepsis progresses to multiple organ dysfunction due to the uncontrolled release of inflammatory mediators, and a growing body of evidence shows that neural signals play a significant role in modulating the immune response. Thus, similar toall other physiological systems, the immune system is both connected to and regulated by the central nervous system. The efferent arc consists of the activation of the hypothalamic–pituitary–adrenal axis, sympathetic activation, the cholinergic anti-inflammatory reflex, and the local release of physiological neuromodulators. Immunosensory activity is centered on the production of pro-inflammatory cytokines, signals that are conveyed to the brain through different pathways. The activation of peripheral sensory nerves, i.e., vagal paraganglia by the vagus nerve, and carotid body (CB chemoreceptors by the carotid/sinus nerve are broadly discussed here. Despite cytokine receptor expression in vagal afferent fibers, pro-inflammatory cytokines have no significant effect on vagus nerve activity. Thus, the CB may be the source of immunosensory inputs and incoming neural signals and, in fact, sense inflammatory mediators, playing a protective role during sepsis. Considering that CB stimulation increases sympathetic activity and adrenal glucocorticoids release, the electrical stimulation of arterial chemoreceptors may be suitable therapeutic approach for regulating systemic inflammation.

  19. Neural systems supporting linguistic structure, linguistic experience, and symbolic communication in sign language and gesture.

    Science.gov (United States)

    Newman, Aaron J; Supalla, Ted; Fernandez, Nina; Newport, Elissa L; Bavelier, Daphne

    2015-09-15

    Sign languages used by deaf communities around the world possess the same structural and organizational properties as spoken languages: In particular, they are richly expressive and also tightly grammatically constrained. They therefore offer the opportunity to investigate the extent to which the neural organization for language is modality independent, as well as to identify ways in which modality influences this organization. The fact that sign languages share the visual-manual modality with a nonlinguistic symbolic communicative system-gesture-further allows us to investigate where the boundaries lie between language and symbolic communication more generally. In the present study, we had three goals: to investigate the neural processing of linguistic structure in American Sign Language (using verbs of motion classifier constructions, which may lie at the boundary between language and gesture); to determine whether we could dissociate the brain systems involved in deriving meaning from symbolic communication (including both language and gesture) from those specifically engaged by linguistically structured content (sign language); and to assess whether sign language experience influences the neural systems used for understanding nonlinguistic gesture. The results demonstrated that even sign language constructions that appear on the surface to be similar to gesture are processed within the left-lateralized frontal-temporal network used for spoken languages-supporting claims that these constructions are linguistically structured. Moreover, although nonsigners engage regions involved in human action perception to process communicative, symbolic gestures, signers instead engage parts of the language-processing network-demonstrating an influence of experience on the perception of nonlinguistic stimuli.

  20. An mHealth monitoring system for traditional birth attendant-led antenatal risk assessment in rural Guatemala.

    Science.gov (United States)

    Stroux, Lisa; Martinez, Boris; Coyote Ixen, Enma; King, Nora; Hall-Clifford, Rachel; Rohloff, Peter; Clifford, Gari D

    Limited funding for medical technology, low levels of education and poor infrastructure for delivering and maintaining technology severely limit medical decision support in low- and middle-income countries. Perinatal and maternal mortality is of particular concern with millions dying every year from potentially treatable conditions. Guatemala has one of the worst maternal mortality ratios, the highest incidence of intra-uterine growth restriction (IUGR), and one of the lowest gross national incomes per capita within Latin America. To address the lack of decision support in rural Guatemala, a smartphone-based system is proposed including peripheral sensors, such as a handheld Doppler for the identification of foetal compromise. Designed for use by illiterate birth attendants, the system uses pictograms, audio guidance, local and cloud processing, SMS alerts and voice calling. The initial prototype was evaluated on 22 women in highland Guatemala. Results were fed back into the refinement of the system, currently undergoing RCT evaluation.

  1. Identification of Nonlinear Dynamic Systems Using Hammerstein-Type Neural Network

    Directory of Open Access Journals (Sweden)

    Hongshan Yu

    2014-01-01

    Full Text Available Hammerstein model has been popularly applied to identify the nonlinear systems. In this paper, a Hammerstein-type neural network (HTNN is derived to formulate the well-known Hammerstein model. The HTNN consists of a nonlinear static gain in cascade with a linear dynamic part. First, the Lipschitz criterion for order determination is derived. Second, the backpropagation algorithm for updating the network weights is presented, and the stability analysis is also drawn. Finally, simulation results show that HTNN identification approach demonstrated identification performances.

  2. Automated system for load flow prediction in power substations using artificial neural networks

    Directory of Open Access Journals (Sweden)

    Arlys Michel Lastre Aleaga

    2015-09-01

    Full Text Available The load flow is of great importance in assisting the process of decision making and planning of generation, distribution and transmission of electricity. Ignorance of the values in this indicator, as well as their inappropriate prediction, difficult decision making and efficiency of the electricity service, and can cause undesirable situations such as; the on demand, overheating of the components that make up a substation, and incorrect planning processes electricity generation and distribution. Given the need for prediction of flow of electric charge of the substations in Ecuador this research proposes the concept for the development of an automated prediction system employing the use of Artificial Neural Networks.

  3. Gapped sequence alignment using artificial neural networks: application to the MHC class I system

    DEFF Research Database (Denmark)

    Andreatta, Massimo; Nielsen, Morten

    2016-01-01

    . On this relatively simple system, we developed a sequence alignment method based on artificial neural networks that allows insertions and deletions in the alignment. Results: We show that prediction methods based on alignments that include insertions and deletions have significantly higher performance than methods...... the length profile of different MHC molecules, and quantified the reduction of the experimental effort required to identify potential epitopes using our prediction algorithm. Availability and implementation: The NetMHC-4.0 method for the prediction of peptide-MHC class I binding affinity using gapped...... sequence alignment is publicly available at: http://www.cbs.dtu.dk/services/NetMHC-4.0....

  4. PENGGUNAAN MOVING AVERAGE DENGAN METODE HYBRID ARTIFICIAL NEURAL NETWORK DAN FUZZY INFERENCE SYSTEM UNTUK PREDIKSI CUACA

    Directory of Open Access Journals (Sweden)

    Fahrur Rozi

    2016-12-01

    Full Text Available Kebutuhan akan prediksi sangat diperlukan diberbagai sektor kehidupan, salah satunya adalah mengenai prediksi cuaca. Prediksi mengenai cuaca dapat dilakukan dalam rentang waktu tertentu, sehingga untuk dapat memprediksi keadaan cuaca dalam rentang waktu tertentu penelitian ini akan menggunakan moving average dengan metode hybrid artificial neural network dan fuzzy inference system. Data yang digunakan berasal dari BMKG Karangploso, Malang dengan menggunakan empat buah parameter yang mempengaruhi kondisi cuaca, yaitu suhu, tekanan udara, kelembapan udara, dan kecepatan angin. Performa model menghasilkan tingkat akurasi mencapai 73.91 %.

  5. Alternative Sensor System and MLP Neural Network for Vehicle Pedal Activity Estimation

    Directory of Open Access Journals (Sweden)

    Ahmed M. Wefky

    2010-04-01

    Full Text Available It is accepted that the activity of the vehicle pedals (i.e., throttle, brake, clutch reflects the driver’s behavior, which is at least partially related to the fuel consumption and vehicle pollutant emissions. This paper presents a solution to estimate the driver activity regardless of the type, model, and year of fabrication of the vehicle. The solution is based on an alternative sensor system (regime engine, vehicle speed, frontal inclination and linear acceleration that reflects the activity of the pedals in an indirect way, to estimate that activity by means of a multilayer perceptron neural network with a single hidden layer.

  6. Visualization of suspicious lesions in breast MRI based on intelligent neural systems

    Science.gov (United States)

    Twellmann, Thorsten; Lange, Oliver; Nattkemper, Tim Wilhelm; Meyer-Bäse, Anke

    2006-05-01

    Intelligent medical systems based on supervised and unsupervised artificial neural networks are applied to the automatic visualization and classification of suspicious lesions in breast MRI. These systems represent an important component of future sophisticated computer-aided diagnosis systems and enable the extraction of spatial and temporal features of dynamic MRI data stemming from patients with confirmed lesion diagnosis. By taking into account the heterogenity of the cancerous tissue, these techniques reveal the malignant, benign and normal kinetic signals and and provide a regional subclassification of pathological breast tissue. Intelligent medical systems are expected to have substantial implications in healthcare politics by contributing to the diagnosis of indeterminate breast lesions by non-invasive imaging.

  7. Optimal system size for complex dynamics in random neural networks near criticality

    Energy Technology Data Exchange (ETDEWEB)

    Wainrib, Gilles, E-mail: wainrib@math.univ-paris13.fr [Laboratoire Analyse Géométrie et Applications, Université Paris XIII, Villetaneuse (France); García del Molino, Luis Carlos, E-mail: garciadelmolino@ijm.univ-paris-diderot.fr [Institute Jacques Monod, Université Paris VII, Paris (France)

    2013-12-15

    In this article, we consider a model of dynamical agents coupled through a random connectivity matrix, as introduced by Sompolinsky et al. [Phys. Rev. Lett. 61(3), 259–262 (1988)] in the context of random neural networks. When system size is infinite, it is known that increasing the disorder parameter induces a phase transition leading to chaotic dynamics. We observe and investigate here a novel phenomenon in the sub-critical regime for finite size systems: the probability of observing complex dynamics is maximal for an intermediate system size when the disorder is close enough to criticality. We give a more general explanation of this type of system size resonance in the framework of extreme values theory for eigenvalues of random matrices.

  8. Intra-day signal instabilities affect decoding performance in an intracortical neural interface system

    Science.gov (United States)

    Perge, János A.; Homer, Mark L.; Malik, Wasim Q.; Cash, Sydney; Eskandar, Emad; Friehs, Gerhard; Donoghue, John P.; Hochberg, Leigh R.

    2013-06-01

    Objective. Motor neural interface systems (NIS) aim to convert neural signals into motor prosthetic or assistive device control, allowing people with paralysis to regain movement or control over their immediate environment. Effector or prosthetic control can degrade if the relationship between recorded neural signals and intended motor behavior changes. Therefore, characterizing both biological and technological sources of signal variability is important for a reliable NIS. Approach. To address the frequency and causes of neural signal variability in a spike-based NIS, we analyzed within-day fluctuations in spiking activity and action potential amplitude recorded with silicon microelectrode arrays implanted in the motor cortex of three people with tetraplegia (BrainGate pilot clinical trial, IDE). Main results. 84% of the recorded units showed a statistically significant change in apparent firing rate (3.8 ± 8.71 Hz or 49% of the mean rate) across several-minute epochs of tasks performed on a single session, and 74% of the units showed a significant change in spike amplitude (3.7 ± 6.5 µV or 5.5% of mean spike amplitude). 40% of the recording sessions showed a significant correlation in the occurrence of amplitude changes across electrodes, suggesting array micro-movement. Despite the relatively frequent amplitude changes, only 15% of the observed within-day rate changes originated from recording artifacts such as spike amplitude change or electrical noise, while 85% of the rate changes most likely emerged from physiological mechanisms. Computer simulations confirmed that systematic rate changes of individual neurons could produce a directional ‘bias’ in the decoded neural cursor movements. Instability in apparent neuronal spike rates indeed yielded a directional bias in 56% of all performance assessments in participant cursor control (n = 2 participants, 108 and 20 assessments over two years), resulting in suboptimal performance in these sessions

  9. Periodontal disease and preterm birth.

    Science.gov (United States)

    Horton, Amanda L; Boggess, Kim A

    2012-03-01

    Preterm birth (delivery at fewer than 37 weeks’ gestation) is the most common cause of infant morbidity and mortality among nonanomalous infants in the United States. Increasing evidence has focused on associations between clinical infection, inflammation, and preterm birth. Maternal periodontal disease, which is associated with systemic inflammation, has been associated with preterm birth. Intervention trails for treatment of periodontal disease during pregnancy, however have not consistently shown a reduction in preterm birth rates. Despite the lack of reduction in preterm birth, oral health maintenance is an important part of preventive care and should be supported during pregnancy.

  10. An artificial neural network to assess the impact of neighbouring photovoltaic systems in power forecasting in Utrecht, the Netherlands

    NARCIS (Netherlands)

    Vaz, A. G R; Elsinga, B.|info:eu-repo/dai/nl/372629601; van Sark, W. G J H M|info:eu-repo/dai/nl/074628526; Brito, M. C.

    2016-01-01

    In order to perform predictions of a photovoltaic (PV) system power production, a neural network architecture system using the Nonlinear Autoregressive with eXogenous inputs (NARX) model is implemented using not only local meteorological data but also measurements of neighbouring PV systems as

  11. Nascimentos pré-termo no Brasil entre 1994 e 2005 conforme o Sistema de Informações sobre Nascidos Vivos (SINASC Preterm births in Brazil from 1994 to 2005 according to the Information System on Live Births (SINASC

    Directory of Open Access Journals (Sweden)

    Mariângela F. Silveira

    2009-06-01

    Full Text Available A monitorização da prematuridade é muito importante, considerando seu impacto na morbidade e mortalidade infantis e seus custos econômicos e sociais. Este estudo utilizou dados do Sistema de Informações sobre Nascidos Vivos (SINASC, implantado em 1990 e expandido de forma gradativa até cobrir cerca de 90% de todos os nascimentos no país, para descrever a evolução da prematuridade no Brasil, regiões e capitais, entre os anos de 1994 e 2005. Observou-se um aumento na proporção de partos prematuros no país como um todo e uma diminuição no número de nascimentos sem informação da idade gestacional. As regiões Sudeste, Sul e Centro-Oeste acompanharam a tendência do país, ao contrário das regiões Norte e Nordeste. São comparados os achados do SINASC com os de estudos de base populacional. O SINASC vem apresentando progressiva melhora na cobertura e qualidade dos dados, mas ainda existem problemas com a acurácia da idade gestacional, levando a uma subestimação da prevalência de prematuridade. Por causa de sua importância, torna-se necessário aprofundar os esforços para o ganho de acurácia do sistema.Monitoring preterm births is essential given their impact on infant morbidity and mortality and their economic and social costs. This article is based on data from the Information System on Live Births (SINASC, implemented in 1990 and expanded gradually to cover 90% of all births in the country. Preterm birth time trends are presented for Brazil, regions, and capitals from 1994 to 2005. At the national level, there was an increase in the preterm birth rate, accompanied by a reduction in the proportion of missing information on gestational age. The Southeast, South, and Central-West regions followed the national trend, while the preterm birth rate fell in the North and Northeast regions. We compared the findings from SINASC with those from population-based studies. The coverage and quality of SINASC has increased over time

  12. Biological channel modeling and implantable UWB antenna design for neural recording systems.

    Science.gov (United States)

    Bahrami, Hadi; Mirbozorgi, S Abdollah; Rusch, Leslie A; Gosselin, Benoit

    2015-01-01

    Ultrawideband (UWB) short-range communication systems have proved to be valuable in medical technology, particularly for implanted devices, due to their low-power consumption, low cost, small size, and high data rates. Neural activity monitoring in the brain requires high data rate (800 kb/s per neural sensor), and we target a system supporting a large number of sensors, in particular, aggregate transmission above 430 Mb/s (∼512 sensors). Knowledge of channel behavior is required to determine the maximum allowable power to 1) respect ANSI guidelines for avoiding tissue damage, and 2) respect FCC guidelines on unlicensed transmissions. We utilize a realistic model of the biological channel to inform the design of antennas for the implanted transmitter and the external receiver under these requirements. Antennas placement is examined under two scenarios having contrasting power constraints. Performance of the system within the biological tissues is examined via simulation and experiment. Our miniaturized antennas, 12 mm ×12 mm, need worst-case receiver sensitivities of -38 and -30.5 dBm for the first and second scenarios, respectively. These sensitivities allow us to successfully detect signals transmitted through tissues in the 3.1-10.6-GHz UWB band.

  13. Rapid-onset antidepressant efficacy of glutamatergic system modulators: the neural plasticity hypothesis of depression.

    Science.gov (United States)

    Wang, Jing; Jing, Liang; Toledo-Salas, Juan-Carlos; Xu, Lin

    2015-02-01

    Depression is a devastating psychiatric disorder widely attributed to deficient monoaminergic signaling in the central nervous system. However, most clinical antidepressants enhance monoaminergic neurotransmission with little delay but require 4-8 weeks to reach therapeutic efficacy, a paradox suggesting that the monoaminergic hypothesis of depression is an oversimplification. In contrast to the antidepressants targeting the monoaminergic system, a single dose of the N-methyl-D-aspartate receptor (NMDAR) antagonist ketamine produces rapid (within 2 h) and sustained (over 7 days) antidepressant efficacy in treatment-resistant patients. Glutamatergic transmission mediated by NMDARs is critical for experience-dependent synaptic plasticity and learning, processes that can be modified indirectly by the monoaminergic system. To better understand the mechanisms of action of the new antidepressants like ketamine, we review and compare the monoaminergic and glutamatergic antidepressants, with emphasis on neural plasticity. The pathogenesis of depression may involve maladaptive neural plasticity in glutamatergic circuits that may serve as a new class of targets to produce rapid antidepressant effects.

  14. Optimization of steel casting feeding system based on BP neural network and genetic algorithm

    Directory of Open Access Journals (Sweden)

    Xue-dan Gong

    2016-05-01

    Full Text Available The trial-and-error method is widely used for the current optimization of the steel casting feeding system, which is highly random, subjective and thus inefficient. In the present work, both the theoretical and the experimental research on the modeling and optimization methods of the process are studied. An approximate alternative model is established based on the Back Propagation (BP neural network and experimental design. The process parameters of the feeding system are taken as the input, the volumes of shrinkage cavities and porosities calculated by simulation are simultaneously taken as the output. Thus, a mathematical model is established by the BP neural network to combine the input variables with the output response. Then, this model is optimized by the nonlinear optimization function of the genetic algorithm. Finally, a feeding system optimization of a steel traveling wheel is conducted. No shrinkage cavities and porosities are induced through the optimization. Compared to the initial design scheme, the process yield is increased by 4.1% and the volume of the riser is decreased by 5.48×106 mm3.

  15. Artificial neural networks: Principle and application to model based control of drying systems -- A review

    Energy Technology Data Exchange (ETDEWEB)

    Thyagarajan, T.; Ponnavaikko, M. [Crescent Engineering Coll., Madras (India); Shanmugam, J. [Madras Inst. of Tech. (India); Panda, R.C.; Rao, P.G. [Central Leather Research Inst., Madras (India)

    1998-07-01

    This paper reviews the developments in the model based control of drying systems using Artificial Neural Networks (ANNs). Survey of current research works reveals the growing interest in the application of ANN in modeling and control of non-linear, dynamic and time-variant systems. Over 115 articles published in this area are reviewed. All landmark papers are systematically classified in chronological order, in three distinct categories; namely, conventional feedback controllers, model based controllers using conventional methods and model based controllers using ANN for drying process. The principles of ANN are presented in detail. The problems and issues of the drying system and the features of various ANN models are dealt with up-to-date. ANN based controllers lead to smoother controller outputs, which would increase actuator life. The paper concludes with suggestions for improving the existing modeling techniques as applied to predicting the performance characteristics of dryers. The hybridization techniques, namely, neural with fuzzy logic and genetic algorithms, presented, provide, directions for pursuing further research for the implementation of appropriate control strategies. The authors opine that the information presented here would be highly beneficial for pursuing research in modeling and control of drying process using ANN. 118 refs.

  16. Developing an Intelligent System for Diagnosis of Asthma Based on Artificial Neural Network.

    Science.gov (United States)

    Alizadeh, Behrouz; Safdari, Reza; Zolnoori, Maryam; Bashiri, Azadeh

    2015-08-01

    Lack of proper diagnosis and inadequate treatment of asthma, leads to physical and financial complications. This study aimed to use data mining techniques and creating a neural network intelligent system for diagnosis of asthma. The study population is the patients who had visited one of the Lung Clinics in Tehran. Data were analyzed using the SPSS statistical tool and the chi-square Pearson's coefficient was the basis of decision making for data ranking. The considered neural network is trained using back propagation learning technique. According to the analysis performed by means of SPSS to select the top factors, 13 effective factors were selected, in different performances, data was mixed in various forms, so the different modes was made for training the data and testing networks and in all different modes, the network was able to predict correctly 100% of all cases. Using data mining methods before the design structure of system, aimed to reduce the data dimension and the optimum choice of the data, will lead to a more accurate system. So considering the data mining approaches due to the nature of medical data is necessary.

  17. Fuzzy Wavelet Neural Network Using a Correntropy Criterion for Nonlinear System Identification

    Directory of Open Access Journals (Sweden)

    Leandro L. S. Linhares

    2015-01-01

    Full Text Available Recent researches have demonstrated that the Fuzzy Wavelet Neural Networks (FWNNs are an efficient tool to identify nonlinear systems. In these structures, features related to fuzzy logic, wavelet functions, and neural networks are combined in an architecture similar to the Adaptive Neurofuzzy Inference Systems (ANFIS. In practical applications, the experimental data set used in the identification task often contains unknown noise and outliers, which decrease the FWNN model reliability. In order to reduce the negative effects of these erroneous measurements, this work proposes the direct use of a similarity measure based on information theory in the FWNN learning procedure. The Mean Squared Error (MSE cost function is replaced by the Maximum Correntropy Criterion (MCC in the traditional error backpropagation (BP algorithm. The input-output maps of a real nonlinear system studied in this work are identified from an experimental data set corrupted by different outliers rates and additive white Gaussian noise. The results demonstrate the advantages of the proposed cost function using the MCC as compared to the MSE. This work also investigates the influence of the kernel size on the performance of the MCC in the BP algorithm, since it is the only free parameter of correntropy.

  18. Travel Time Estimation Using Freeway Point Detector Data Based on Evolving Fuzzy Neural Inference System.

    Directory of Open Access Journals (Sweden)

    Jinjun Tang

    Full Text Available Travel time is an important measurement used to evaluate the extent of congestion within road networks. This paper presents a new method to estimate the travel time based on an evolving fuzzy neural inference system. The input variables in the system are traffic flow data (volume, occupancy, and speed collected from loop detectors located at points both upstream and downstream of a given link, and the output variable is the link travel time. A first order Takagi-Sugeno fuzzy rule set is used to complete the inference. For training the evolving fuzzy neural network (EFNN, two learning processes are proposed: (1 a K-means method is employed to partition input samples into different clusters, and a Gaussian fuzzy membership function is designed for each cluster to measure the membership degree of samples to the cluster centers. As the number of input samples increases, the cluster centers are modified and membership functions are also updated; (2 a weighted recursive least squares estimator is used to optimize the parameters of the linear functions in the Takagi-Sugeno type fuzzy rules. Testing datasets consisting of actual and simulated data are used to test the proposed method. Three common criteria including mean absolute error (MAE, root mean square error (RMSE, and mean absolute relative error (MARE are utilized to evaluate the estimation performance. Estimation results demonstrate the accuracy and effectiveness of the EFNN method through comparison with existing methods including: multiple linear regression (MLR, instantaneous model (IM, linear model (LM, neural network (NN, and cumulative plots (CP.

  19. Neural systems underlying aversive conditioning in humans with primary and secondary reinforcers

    Directory of Open Access Journals (Sweden)

    Mauricio R Delgado

    2011-05-01

    Full Text Available Money is a secondary reinforcer commonly used across a range of disciplines in experimental paradigms investigating reward learning and decision-making. The effectiveness of monetary reinforcers during aversive learning and its neural basis, however, remains a topic of debate. Specifically, it is unclear if the initial acquisition of aversive representations of monetary losses depends on similar neural systems as more traditional aversive conditioning that involves primary reinforcers. This study contrasts the efficacy of a biologically defined primary reinforcer (shock and a socially defined secondary reinforcer (money during aversive learning and its associated neural circuitry. During a two-part experiment, participants first played a gambling game where wins and losses were based on performance to gain an experimental bank. Participants were then exposed to two separate aversive conditioning sessions. In one session, a primary reinforcer (mild shock served as an unconditioned stimulus (US and was paired with one of two colored squares, the conditioned stimuli (CS+ and CS-, respectively. In another session, a secondary reinforcer (loss of money served as the US and was paired with one of two different CS. Skin conductance responses were greater for CS+ compared to CS- trials irrespective of type of reinforcer. Neuroimaging results revealed that the striatum, a region typically linked with reward-related processing, was found to be involved in the acquisition of aversive conditioned response irrespective of reinforcer type. In contrast, the amygdala was involved during aversive conditioning with primary reinforcers, as suggested by both an exploratory fMRI analysis and a follow-up case study with a patient with bilateral amygdala damage. Taken together, these results suggest that learning about potential monetary losses may depend on reinforcement learning related systems, rather than on typical structures involved in more biologically based

  20. Mixed Mode Oscillations and Synchronous Activity in Noise Induced Modified Morris-Lecar Neural System

    Science.gov (United States)

    Upadhyay, Ranjit Kumar; Mondal, Argha; Teka, Wondimu W.

    The modified three-dimensional (3D) Morris-Lecar (M-L) model is very useful to understand the spiking activities of neurons. The present article addresses the random dynamical behavior of a modified M-L model driven by a white Gaussian noise with mean zero and unit spectral density. The applied stimulus can be expressed as a random term. Such random perturbations are represented by a white Gaussian noise current added through the electrical potential of membrane of the excitatory principal cells. The properties of the stochastic system (perturbed one) and noise induced mixed mode oscillation are analyzed. The Lyapunov spectrum is computed to present the nature of the system dynamics. The noise intensity is varied while keeping fixed the predominant parameters of the model in their ranges and also observed the changes in the dynamical behavior of the system. The dynamical synchronization is studied in the coupled M-L systems interconnected by excitatory and inhibitory neurons with noisy electrical coupling and verified with similarity functions. This result suggests the potential benefits of noise and noise induced oscillations which have been observed in real neurons and how that affects the dynamics of the neural model as well as the coupled systems. The analysis reports that the modified M-L system which has the limit cycle behavior can show a type of phase locking behavior which follows either period adding (i.e. 1:1, 2:1, 3:1, 4:1) sequences or Farey sequences. For the coupled neural systems, complete synchronization is shown for sufficient noisy coupling strength.

  1. A Neural Network Controller for Variable-Speed Variable-Pitch Wind Energy Conversion Systems Using Generalized Minimum Entropy Criterion

    Directory of Open Access Journals (Sweden)

    Mifeng Ren

    2014-01-01

    Full Text Available This paper considers the neural network controller design problem for variable pitch wind energy conversion systems (WECS with non-Gaussian wind speed disturbances in the stochastic distribution control framework. The approach here is used to directly model the unknown control law based on a fixed neural network (the number of layers and nodes in a neural network is fixed without the need to construct a separate model for the WECS. In order to characterize the randomness of the WECS, a generalized minimum entropy criterion is established to train connection weights of the neural network. For the train purpose, both kernel density estimation method and sliding window technique are adopted to estimate the PDF of tracking error and entropies. Due to the unknown process dynamics, the gradient of the objective function in a gradient-descent-type algorithm is estimated using an incremental perturbation method. The proposed approach is illustrated on a simulated WECS with non-Gaussian wind speed.

  2. Scaling up a chemically-defined aggregate-based suspension culture system for neural commitment of human pluripotent stem cells.

    Science.gov (United States)

    Miranda, Cláudia C; Fernandes, Tiago G; Diogo, M Margarida; Cabral, Joaquim M S

    2016-12-01

    The demand of high cell numbers for applications in cellular therapies and drug screening requires the development of scalable platforms capable to generating highly pure populations of tissue-specific cells from human pluripotent stem cells. In this work, we describe the scaling-up of an aggregate-based culture system for neural induction of human induced pluripotent stem cells (hiPSCs) under chemically-defined conditions. A combination of non-enzymatic dissociation and rotary agitation was successfully used to produce homogeneous populations of hiPSC aggregates with an optimal (140 μm) and narrow distribution of diameters (coefficient of variation of 21.6%). Scalable neural commitment of hiPSCs as 3D aggregates was performed in 50 mL spinner flasks, and the process was optimized using a factorial design approach, involving parameters such as agitation rate and seeding density. We were able to produce neural progenitor cell cultures, that at the end of a 6-day neural induction process contained less than 3% of Oct4-positive cells and that, after replating, retained more than 60% of Pax6-positive neural cells. The results here presented should set the stage for the future generation of a clinically relevant number of human neural progenitors for transplantation and other biomedical applications using controlled, automated and reproducible large-scale bioreactor culture systems. Copyright © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  3. A Robust Single Primate Neuroepithelial Cell Clonal Expansion System for Neural Tube Development and Disease Studies

    Directory of Open Access Journals (Sweden)

    Xiaoqing Zhu

    2016-02-01

    Full Text Available Developing a model of primate neural tube (NT development is important to promote many NT disorder studies in model organisms. Here, we report a robust and stable system to allow for clonal expansion of single monkey neuroepithelial stem cells (NESCs to develop into miniature NT-like structures. Single NESCs can produce functional neurons in vitro, survive, and extensively regenerate neuron axons in monkey brain. NT formation and NESC maintenance depend on high metabolism activity and Wnt signaling. NESCs are regionally restricted to a telencephalic fate. Moreover, single NESCs can turn into radial glial progenitors (RGPCs. The transition is accurately regulated by Wnt signaling through regulation of Notch signaling and adhesion molecules. Finally, using the “NESC-TO-NTs” system, we model the functions of folic acid (FA on NT closure and demonstrate that FA can regulate multiple mechanisms to prevent NT defects. Our system is ideal for studying NT development and diseases.

  4. Using Weightless Neural Networks for Vergence Control in an Artificial Vision System

    Directory of Open Access Journals (Sweden)

    Karin S. Komati

    2003-01-01

    Full Text Available This paper presents a methodology we have developed and used to implement an artificial binocular vision system capable of emulating the vergence of eye movements. This methodology involves using weightless neural networks (WNNs as building blocks of artificial vision systems. Using the proposed methodology, we have designed several architectures of WNN-based artificial vision systems, in which images captured by virtual cameras are used for controlling the position of the ‘foveae’ of these cameras (high-resolution region of the images captured. Our best architecture is able to control the foveae vergence movements with average error of only 3.58 image pixels, which is equivalent to an angular error of approximately 0.629°.

  5. A probablistic neural network classification system for signal and image processing

    Energy Technology Data Exchange (ETDEWEB)

    Bowman, B. [Lawrence Livermore National Lab., CA (United States)

    1994-11-15

    The Acoustical Heart Valve Analysis Package is a system for signal and image processing and classification. It is being developed in both Matlab and C, to provide an interactive, interpreted environment, and has been optimized for large scale matrix operations. It has been used successfully to classify acoustic signals from implanted prosthetic heart valves in human patients, and will be integrated into a commercial Heart Valve Screening Center. The system uses several standard signal processing algorithms, as well as supervised learning techniques using the probabilistic neural network (PNN). Although currently used for the acoustic heart valve application, the algorithms and modular design allow it to be used for other applications, as well. We will describe the signal classification system, and show results from a set of test valves.

  6. Neural Network-Based Receiver in Band-Limited Communication System with MPPSK Modulation

    Directory of Open Access Journals (Sweden)

    Wang Zixin

    2018-01-01

    Full Text Available As a type of the spectrally efficient modulation, the m-ary phase position shift keying (MPPSK has been considered to meet the increasing spectrum requirement in the future wireless system. To limit the signal bandwidth and cancel the out-band interference the band-pass filters are used, which introduce the waveform distortion and inter-symbol interference (ISI. Therefore, a single hidden-layer neural network (NN-based receiver is proposed to jointly equalize and demodulate the received signal. The impulse response of the system is static and the network parameters can be obtained after off-line training. The number of the hidden nodes is also determined through simulations. Simulation results show that the NN-based receiver works well in the communication system with different allocated bandwidths. By observing the modified confusion matrix, the false symbol decision is relevant to modulation index, waveform distortions and the ISI.

  7. Investigations on Incipient Fault Diagnosis of Power Transformer Using Neural Networks and Adaptive Neurofuzzy Inference System

    Directory of Open Access Journals (Sweden)

    Nandkumar Wagh

    2014-01-01

    Full Text Available Continuity of power supply is of utmost importance to the consumers and is only possible by coordination and reliable operation of power system components. Power transformer is such a prime equipment of the transmission and distribution system and needs to be continuously monitored for its well-being. Since ratio methods cannot provide correct diagnosis due to the borderline problems and the probability of existence of multiple faults, artificial intelligence could be the best approach. Dissolved gas analysis (DGA interpretation may provide an insight into the developing incipient faults and is adopted as the preliminary diagnosis tool. In the proposed work, a comparison of the diagnosis ability of backpropagation (BP, radial basis function (RBF neural network, and adaptive neurofuzzy inference system (ANFIS has been investigated and the diagnosis results in terms of error measure, accuracy, network training time, and number of iterations are presented.

  8. ARTIFICIAL NEURAL NETWORK BASED ULTRASONIC SENSOR SYSTEM FOR DETECTION OF ADULTERATION IN EDIBLE OIL

    Directory of Open Access Journals (Sweden)

    TONY GEORGE

    2017-06-01

    Full Text Available This paper presents the design, development and experimental validation of an ultrasonic sensor system for the detection of adulteration in edible oil. Variation of ultrasonic wave propagation characteristics like attenuation coefficient, reflection coefficient and velocity of propagation in pure and adulterated oil were used for developing the algorithm to detect the adulteration. Measurement cell was designed for operating ultrasonic transducer at 1 MHz using COMSOL 4.4. Artificial Neural Network (ANN based algorithm was also developed for improving the efficiency of the sensor system. It is found that this system can detect adulteration with an accuracy of 99.53% for sunflower oil added in pure coconut oil, whereas 98.82% for palm oil added in pure coconut oil.

  9. Data systems and computer science: Neural networks base R/T program overview

    Science.gov (United States)

    Gulati, Sandeep

    1991-01-01

    The research base, in the U.S. and abroad, for the development of neural network technology is discussed. The technical objectives are to develop and demonstrate adaptive, neural information processing concepts. The leveraging of external funding is also discussed.

  10. Extrapolating a hierarchy of building block systems towards future neural network organisms.

    Science.gov (United States)

    Jagers op Akkerhuis, G

    2001-01-01

    Is it possible to predict future life forms? In this paper it is argued that the answer to this question may well be positive. As a basis for predictions a rationale is used that is derived from historical data, e.g. from a hierarchical classification that ranks all building block systems, that have evolved so far. This classification is based on specific emergent properties that allow stepwise transitions, from low level building blocks to higher level ones. This paper shows how this hierarchy can be used for predicting future life forms. The extrapolations suggest several future neural network organisms. Major aspects of the structures of these organisms are predicted. The results can be considered of fundamental importance for several reasons. Firstly, assuming that the operator hierarchy is a proper basis for predictions, the result yields insight into the structure of future organisms. Secondly, the predictions are not extrapolations of presently observed trends, but are fully integrated with all historical system transitions in evolution. Thirdly, the extrapolations suggest the structures of intelligences that, one day, will possess more powerful brains than human beings. This study ends with a discussion of possibilities for falsification of the present theory, the implications of the present predictions in relation to recent developments in artificial intelligence and the philosophical implications of the role of humanity in evolution with regard to the creation of future neural network organisms.

  11. Autism as a neural systems disorder: A theory of frontal-posterior underconnectivity

    Science.gov (United States)

    Just, Marcel Adam; Keller, Timothy A.; Malave, Vicente L.; Kana, Rajesh K.; Varma, Sashank

    2012-01-01

    The underconnectivity theory of autism attributes the disorder to lower anatomical and functional systems connectivity between frontal and more posterior cortical processing. Here we review evidence for the theory and present a computational model of an executive functioning task (Tower of London) implementing the assumptions of underconnectivity. We make two modifications to a previous computational account of performance and brain activity in typical individuals in the Tower of London task (Newman et al., 2003): (1) the communication bandwidth between frontal and parietal areas was decreased and (2) the posterior centers were endowed with more executive capability (i.e., more autonomy, an adaptation is proposed to arise in response to the lowered frontal-posterior bandwidth). The autism model succeeds in matching the lower frontal-posterior functional connectivity (lower synchronization of activation) seen in fMRI data, as well as providing insight into behavioral response time results. The theory provides a unified account of how a neural dysfunction can produce a neural systems disorder and a psychological disorder with the widespread and diverse symptoms of autism. PMID:22353426

  12. An artificial neural network system to identify alleles in reference electropherograms.

    Science.gov (United States)

    Taylor, Duncan; Harrison, Ash; Powers, David

    2017-09-01

    Electropherograms are produced in great numbers in forensic DNA laboratories as part of everyday criminal casework. Before the results of these electropherograms can be used they must be scrutinised by analysts to determine what the identified data tells them about the underlying DNA sequences and what is purely an artefact of the DNA profiling process. This process of interpreting the electropherograms can be time consuming and is prone to subjective differences between analysts. Recently it was demonstrated that artificial neural networks could be used to classify information within an electropherogram as allelic (i.e. representative of a DNA fragment present in the DNA extract) or as one of several different categories of artefactual fluorescence that arise as a result of generating an electropherogram. We extend that work here to demonstrate a series of algorithms and artificial neural networks that can be used to identify peaks on an electropherogram and classify them. We demonstrate the functioning of the system on several profiles and compare the results to a leading commercial DNA profile reading system. Copyright © 2017 Elsevier B.V. All rights reserved.

  13. A biologically based neural system coordinates the joints and legs of a tetrapod.

    Science.gov (United States)

    Hunt, Alexander; Schmidt, Manuela; Fischer, Martin; Quinn, Roger

    2015-09-09

    A biologically inspired neural control system has been developed that coordinates a tetrapod trotting gait in the sagittal plane. The developed neuromechanical system is used to explore properties of connections in inter-leg and intra-leg coordination. The neural controller is built with biologically based neurons and synapses, and connections are based on data from literature where available. It is applied to a planar biomechanical model of a rat with 14 joints, each actuated by a pair of antagonistic Hill muscle models. The controller generates tension in the muscles through activation of simulated motoneurons. The hind leg and inter-leg control networks are based on pathways discovered in cat research tuned to the kinematic motions of a rat. The foreleg network was developed by extrapolating analogous pathways from the hind legs. The formulated intra-leg and inter-leg networks properly coordinate the joints and produce motions similar to those of a walking rat. Changing the strength of a single inter-leg connection is sufficient to account for differences in phase timing in different trotting rats.

  14. Use of artificial neural networks for analysis of complex physical systems

    Energy Technology Data Exchange (ETDEWEB)

    Benjamin, A.; Altman, B.; O`Gorman, C.; Rodeman, R.; Paez, T.L.

    1996-12-31

    Mathematical models of physical systems are used, among other purposes, to improve our understanding of the behavior of physical systems, predict physical system response, and control the responses of systems. Phenomenological models are frequently used to simulate system behavior, but an alternative is available - the artificial neural network (ANN). The ANN is an inductive, or data-based model for the simulation of input/output mappings. The ANN can be used in numerous frameworks to simulate physical system behavior. ANNs require training data to learn patterns of input/output behavior, and once trained, they can be used to simulate system behavior within the space where they were trained.They do this by interpolating specified inputs among the training inputs to yield outputs that are interpolations of =Ming outputs. The reason for using ANNs for the simulation of system response is that they provide accurate approximations of system behavior and are typically much more efficient than phenomenological models. This efficiency is very important in situations where multiple response computations are required, as in, for example, Monte Carlo analysis of probabilistic system response. This paper describes two frameworks in which we have used ANNs to good advantage in the approximate simulation of the behavior of physical system response. These frameworks are the non-recurrent and recurrent frameworks. It is assumed in these applications that physical experiments have been performed to obtain data characterizing the behavior of a system, or that an accurate finite element model has been run to establish system response. The paper provides brief discussions on the operation of ANNs, the operation of two different types of mechanical systems, and approaches to the solution of some special problems that occur in connection with ANN simulation of physical system response. Numerical examples are presented to demonstrate system simulation with ANNs.

  15. Precipitous Birth

    Directory of Open Access Journals (Sweden)

    Jennifer Yee

    2017-09-01

    Full Text Available Audience: This scenario was developed to educate emergency medicine residents on the management of a precipitous birth in the emergency department (ED. The case is also appropriate for teaching of medical students and advanced practice providers, as well as reviewing the principles of crisis resource management, teamwork, and communication. Introduction: Patients with precipitous birth require providers to manage two patients simultaneously with limited time and resources. Crisis resource management skills will be tested once baby is delivered, and the neonate will require assessment for potential neonatal resuscitation. Objectives: At the conclusion of the simulation session, learners will be able to manage women who have precipitous deliveries, as well as perform neonatal assessment and management. Method: This session was conducted using high-fidelity simulation, followed by a debriefing session and lecture on precipitous birth management and neonatal evaluation.

  16. Emergence of gamma motor activity in an artificial neural network model of the corticospinal system.

    Science.gov (United States)

    Grandjean, Bernard; Maier, Marc A

    2017-02-01

    Muscle spindle discharge during active movement is a function of mechanical and neural parameters. Muscle length changes (and their derivatives) represent its primary mechanical, fusimotor drive its neural component. However, neither the action nor the function of fusimotor and in particular of γ-drive, have been clearly established, since γ-motor activity during voluntary, non-locomotor movements remains largely unknown. Here, using a computational approach, we explored whether γ-drive emerges in an artificial neural network model of the corticospinal system linked to a biomechanical antagonist wrist simulator. The wrist simulator included length-sensitive and γ-drive-dependent type Ia and type II muscle spindle activity. Network activity and connectivity were derived by a gradient descent algorithm to generate reciprocal, known target α-motor unit activity during wrist flexion-extension (F/E) movements. Two tasks were simulated: an alternating F/E task and a slow F/E tracking task. Emergence of γ-motor activity in the alternating F/E network was a function of α-motor unit drive: if muscle afferent (together with supraspinal) input was required for driving α-motor units, then γ-drive emerged in the form of α-γ coactivation, as predicted by empirical studies. In the slow F/E tracking network, γ-drive emerged in the form of α-γ dissociation and provided critical, bidirectional muscle afferent activity to the cortical network, containing known bidirectional target units. The model thus demonstrates the complementary aspects of spindle output and hence γ-drive: i) muscle spindle activity as a driving force of α-motor unit activity, and ii) afferent activity providing continuous sensory information, both of which crucially depend on γ-drive.

  17. GPM Ground Validation Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks Cloud Classification System (PERSIANN-CCS) IFloodS V1

    Data.gov (United States)

    National Aeronautics and Space Administration — The GPM Ground Validation Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks Cloud Classification System (PERSIANN-CCS)...

  18. Birth of an oceanic spreading center at a magma-poor rift system.

    Science.gov (United States)

    Gillard, Morgane; Sauter, Daniel; Tugend, Julie; Tomasi, Simon; Epin, Marie-Eva; Manatschal, Gianreto

    2017-11-08

    Oceanic crust is continuously created at mid-oceanic ridges and seafloor spreading represents one of the main processes of plate tectonics. However, if oceanic crust architecture, composition and formation at present-day oceanic ridges are largely described, the processes governing the birth of a spreading center remain enigmatic. Understanding the transition between inherited continental and new oceanic domains is a prerequisite to constrain one of the last major unsolved problems of plate tectonics, namely the formation of a stable divergent plate boundary. In this paper, we present newly released high-resolution seismic reflection profiles that image the complete transition from unambiguous continental to oceanic crusts in the Gulf of Guinea. Based on these high-resolution seismic sections we show that onset of oceanic seafloor spreading is associated with the formation of a hybrid crust in which thinned continental crust and/or exhumed mantle is sandwiched between magmatic intrusive and extrusive bodies. This crust results from a polyphase evolution showing a gradual transition from tectonic-driven to magmatic-driven processes. The results presented in this paper provide a characterization of the domain in which lithospheric breakup occurs and enable to define the processes controlling formation of a new plate boundary.

  19. Vibration control of uncertain multiple launch rocket system using radial basis function neural network

    Science.gov (United States)

    Li, Bo; Rui, Xiaoting

    2018-01-01

    Poor dispersion characteristics of rockets due to the vibration of Multiple Launch Rocket System (MLRS) have always restricted the MLRS development for several decades. Vibration control is a key technique to improve the dispersion characteristics of rockets. For a mechanical system such as MLRS, the major difficulty in designing an appropriate control strategy that can achieve the desired vibration control performance is to guarantee the robustness and stability of the control system under the occurrence of uncertainties and nonlinearities. To approach this problem, a computed torque controller integrated with a radial basis function neural network is proposed to achieve the high-precision vibration control for MLRS. In this paper, the vibration response of a computed torque controlled MLRS is described. The azimuth and elevation mechanisms of the MLRS are driven by permanent magnet synchronous motors and supposed to be rigid. First, the dynamic model of motor-mechanism coupling system is established using Lagrange method and field-oriented control theory. Then, in order to deal with the nonlinearities, a computed torque controller is designed to control the vibration of the MLRS when it is firing a salvo of rockets. Furthermore, to compensate for the lumped uncertainty due to parametric variations and un-modeled dynamics in the design of the computed torque controller, a radial basis function neural network estimator is developed to adapt the uncertainty based on Lyapunov stability theory. Finally, the simulated results demonstrate the effectiveness of the proposed control system and show that the proposed controller is robust with regard to the uncertainty.

  20. A Neural Network Architecture For Rapid Model Indexing In Computer Vision Systems

    Science.gov (United States)

    Pawlicki, Ted

    1988-03-01

    Models of objects stored in memory have been shown to be useful for guiding the processing of computer vision systems. A major consideration in such systems, however, is how stored models are initially accessed and indexed by the system. As the number of stored models increases, the time required to search memory for the correct model becomes high. Parallel distributed, connectionist, neural networks' have been shown to have appealing content addressable memory properties. This paper discusses an architecture for efficient storage and reference of model memories stored as stable patterns of activity in a parallel, distributed, connectionist, neural network. The emergent properties of content addressability and resistance to noise are exploited to perform indexing of the appropriate object centered model from image centered primitives. The system consists of three network modules each of which represent information relative to a different frame of reference. The model memory network is a large state space vector where fields in the vector correspond to ordered component objects and relative, object based spatial relationships between the component objects. The component assertion network represents evidence about the existence of object primitives in the input image. It establishes local frames of reference for object primitives relative to the image based frame of reference. The spatial relationship constraint network is an intermediate representation which enables the association between the object based and the image based frames of reference. This intermediate level represents information about possible object orderings and establishes relative spatial relationships from the image based information in the component assertion network below. It is also constrained by the lawful object orderings in the model memory network above. The system design is consistent with current psychological theories of recognition by component. It also seems to support Marr's notions

  1. ANALYSIS OF THE HARMONIC LOSSES WITH ARTIFICIAL NEURAL NETWORKS IN UNBALANCED SYSTEM LOSSES USING BALANCED ELECTRIC POWER SYSTEM DATA

    Directory of Open Access Journals (Sweden)

    Aslan İNAN

    2005-01-01

    Full Text Available The losses in the power systems should be low as possible as. Saving energy instead of loses (kWh in power utilities can supply much more energy to the consumers. The lower losses the more energy is saved and thus the power system becomes more economical. In recent years, the increasing number of applications and power ratings of the devices which have nonlinear voltage-current characteristics cause voltage waveform distortion and additional losses. While evaluating losses considering harmonics will provide more contribution to obtain more accurate results. In this study, Artificial Neural Networks (ANN method has been presented to predict the harmonic losses in unbalanced power systems by using the data from balanced power system with nonlinear loads.

  2. Measurement of neural signals from inexpensive, wireless and dry EEG systems.

    Science.gov (United States)

    Grummett, T S; Leibbrandt, R E; Lewis, T W; DeLosAngeles, D; Powers, D M W; Willoughby, J O; Pope, K J; Fitzgibbon, S P

    2015-07-01

    Electroencephalography (EEG) is challenged by high cost, immobility of equipment and the use of inconvenient conductive gels. We compared EEG recordings obtained from three systems that are inexpensive, wireless, and/or dry (no gel), against recordings made with a traditional, research-grade EEG system, in order to investigate the ability of these 'non-traditional' systems to produce recordings of comparable quality to a research-grade system. The systems compared were: Emotiv EPOC (inexpensive and wireless), B-Alert (wireless), g.Sahara (dry) and g.HIamp (research-grade). We compared the ability of the systems to demonstrate five well-studied neural phenomena: (1) enhanced alpha activity with eyes closed versus open; (2) visual steady-state response (VSSR); (3) mismatch negativity; (4) P300; and (5) event-related desynchronization/synchronization. All systems measured significant alpha augmentation with eye closure, and were able to measure VSSRs (although these were smaller with g.Sahara). The B-Alert and g.Sahara were able to measure the three time-locked phenomena equivalently to the g.HIamp. The Emotiv EPOC did not have suitably located electrodes for two of the tasks and synchronization considerations meant that data from the time-locked tasks were not assessed. The results show that inexpensive, wireless, or dry systems may be suitable for experimental studies using EEG, depending on the research paradigm, and within the constraints imposed by their limited electrode placement and number.

  3. Closed-loop optical neural stimulation based on a 32-channel low-noise recording system with online spike sorting

    Science.gov (United States)

    Nguyen, T. K. T.; Navratilova, Z.; Cabral, H.; Wang, L.; Gielen, G.; Battaglia, F. P.; Bartic, C.

    2014-08-01

    Objective. Closed-loop operation of neuro-electronic systems is desirable for both scientific and clinical (neuroprosthesis) applications. Integrating optical stimulation with recording capability further enhances the selectivity of neural stimulation. We have developed a system enabling the local delivery of optical stimuli and the simultaneous electrical measuring of the neural activities in a closed-loop approach. Approach. The signal analysis is performed online through the implementation of a template matching algorithm. The system performance is demonstrated with the recorded data and in awake rats. Main results. Specifically, the neural activities are simultaneously recorded, detected, classified online (through spike sorting) from 32 channels, and used to trigger a light emitting diode light source using generated TTL signals. Significance. A total processing time of 8 ms is achieved, suitable for optogenetic studies of brain mechanisms online.

  4. A Neural Systems-Based Neurobiology and Neuropsychiatry Course: Integrating Biology, Psychodynamics, and Psychology in the Psychiatric Curriculum

    Science.gov (United States)

    Lacy, Timothy; Hughes, John D.

    2006-01-01

    Objective: Psychotherapy and biological psychiatry remain divided in psychiatry residency curricula. Behavioral neurobiology and neuropsychiatry provide a systems-level framework that allows teachers to integrate biology, psychodynamics, and psychology. Method: The authors detail the underlying assumptions and outline of a neural systems-based…

  5. VERIFICATION OF GRAPHEMES USING NEURAL NETWORKS IN AN HMM­BASED ON­LINE KOREAN HANDWRITING RECOGNITION SYSTEM

    NARCIS (Netherlands)

    So, S.J.; Kim, J.; Kim, J.H.

    2004-01-01

    This paper presents a neural network based verification method in an HMM­based on­line Korean handwriting recognition system. It penalizes unreasonable grapheme hypotheses and complements global and structural information to the HMM­based recognition system, which is intrinsically based on local

  6. DECISION WITH ARTIFICIAL NEURAL NETWORKS IN DISCRETE EVENT SIMULATION MODELS ON A TRAFFIC SYSTEM

    Directory of Open Access Journals (Sweden)

    Marília Gonçalves Dutra da Silva

    2016-04-01

    Full Text Available ABSTRACT This work aims to demonstrate the use of a mechanism to be applied in the development of the discrete-event simulation models that perform decision operations through the implementation of an artificial neural network. Actions that involve complex operations performed by a human agent in a process, for example, are often modeled in simplified form with the usual mechanisms of simulation software. Therefore, it was chosen a traffic system controlled by a traffic officer with a flow of vehicles and pedestrians to demonstrate the proposed solution. From a module built in simulation software itself, it was possible to connect the algorithm for intelligent decision to the simulation model. The results showed that the model elaborated responded as expected when it was submitted to actions, which required different decisions to maintain the operation of the system with changes in the flow of people and vehicles.

  7. FPGA IMPLEMENTATION OF ADAPTIVE INTEGRATED SPIKING NEURAL NETWORK FOR EFFICIENT IMAGE RECOGNITION SYSTEM

    Directory of Open Access Journals (Sweden)

    T. Pasupathi

    2014-05-01

    Full Text Available Image recognition is a technology which can be used in various applications such as medical image recognition systems, security, defense video tracking, and factory automation. In this paper we present a novel pipelined architecture of an adaptive integrated Artificial Neural Network for image recognition. In our proposed work we have combined the feature of spiking neuron concept with ANN to achieve the efficient architecture for image recognition. The set of training images are trained by ANN and target output has been identified. Real time videos are captured and then converted into frames for testing purpose and the image were recognized. The machine can operate at up to 40 frames/sec using images acquired from the camera. The system has been implemented on XC3S400 SPARTAN-3 Field Programmable Gate Arrays.

  8. Adaptive Neural Control of Nonaffine Nonlinear Systems without Differential Condition for Nonaffine Function

    Directory of Open Access Journals (Sweden)

    Chaojiao Sun

    2016-01-01

    Full Text Available An adaptive neural control scheme is proposed for nonaffine nonlinear system without using the implicit function theorem or mean value theorem. The differential conditions on nonaffine nonlinear functions are removed. The control-gain function is modeled with the nonaffine function probably being indifferentiable. Furthermore, only a semibounded condition for nonaffine nonlinear function is required in the proposed method, and the basic idea of invariant set theory is then constructively introduced to cope with the difficulty in the control design for nonaffine nonlinear systems. It is rigorously proved that all the closed-loop signals are bounded and the tracking error converges to a small residual set asymptotically. Finally, simulation examples are provided to demonstrate the effectiveness of the designed method.

  9. Computer vision system for egg volume prediction using backpropagation neural network

    Science.gov (United States)

    Siswantoro, J.; Hilman, M. Y.; Widiasri, M.

    2017-11-01

    Volume is one of considered aspects in egg sorting process. A rapid and accurate volume measurement method is needed to develop an egg sorting system. Computer vision system (CVS) provides a promising solution for volume measurement problem. Artificial neural network (ANN) has been used to predict the volume of egg in several CVSs. However, volume prediction from ANN could have less accuracy due to inappropriate input features or inappropriate ANN structure. This paper proposes a CVS for predicting the volume of egg using ANN. The CVS acquired an image of egg from top view and then processed the image to extract its 1D and 2 D size features. The features were used as input for ANN in predicting the volume of egg. The experiment results show that the proposed CSV can predict the volume of egg with a good accuracy and less computation time.

  10. A hybrid neural network system for prediction and recognition of promoter regions in human genome.

    Science.gov (United States)

    Chen, Chuan-Bo; Li, Tao

    2005-05-01

    This paper proposes a high specificity and sensitivity algorithm called PromPredictor for recognizing promoter regions in the human genome. PromPredictor extracts compositional features and CpG islands information from genomic sequence, feeding these features as input for a hybrid neural network system (HNN) and then applies the HNN for prediction. It combines a novel promoter recognition model, coding theory, feature selection and dimensionality reduction with machine learning algorithm. Evaluation on Human chromosome 22 was approximately 66% in sensitivity and approximately 48% in specificity. Comparison with two other systems revealed that our method had superior sensitivity and specificity in predicting promoter regions. PromPredictor is written in MATLAB and requires Matlab to run. PromPredictor is freely available at http://www.whtelecom.com/Prompredictor.htm.

  11. Disease and degeneration of aging neural systems that integrate sleep drive and circadian oscillations

    Directory of Open Access Journals (Sweden)

    Kris eSingletary

    2011-10-01

    Full Text Available Sleep and circadian activity rhythms become irregular with age which are characterized by fragmented sleep during the night and increased daytime sleepiness. These changes lead to a reduction in the quality of life due to cognitive impairments and emotional stress. More importantly, severely disrupted sleep and circadian rhythms have been associated with an increase in disease susceptibility. Many of the same brain areas affected by neurodegenerative diseases include the sleep and wake promoting systems. Any advances in our knowledge of these sleep/wake networks are necessary to target neural areas or connections for therapy. This review will discuss research that uses molecular, behavioral, genetic and anatomical methods to further our understanding of the interaction of these systems.

  12. First Principles Neural Network Potentials for Reactive Simulations of Large Molecular and Condensed Systems.

    Science.gov (United States)

    Behler, Jörg

    2017-10-09

    Modern simulation techniques have reached a level of maturity which allows a wide range of problems in chemistry and materials science to be addressed. Unfortunately, the application of first principles methods with predictive power is still limited to rather small systems, and despite the rapid evolution of computer hardware no fundamental change in this situation can be expected. Consequently, the development of more efficient but equally reliable atomistic potentials to reach an atomic level understanding of complex systems has received considerable attention in recent years. A promising new development has been the introduction of machine learning (ML) methods to describe the atomic interactions. Once trained with electronic structure data, ML potentials can accelerate computer simulations by several orders of magnitude, while preserving quantum mechanical accuracy. This Review considers the methodology of an important class of ML potentials that employs artificial neural networks. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

  13. Prospects of application of artificial neural networks for forecasting of cargo transportation volume in transport systems

    Directory of Open Access Journals (Sweden)

    D. T. Yakupov

    2017-01-01

    Full Text Available The purpose of research – to identify the prospects for the use of neural network approach in relation to the tasks of economic forecasting of logistics performance, in particular of volume freight traffic in the transport system promiscuous regional freight traffic, as well as to substantiate the effectiveness of the use of artificial neural networks (ANN, as compared with the efficiency of traditional extrapolative methods of forecasting. The authors consider the possibility of forecasting to use ANN for these economic indicators not as an alternative to the traditional methods of statistical forecasting, but as one of the available simple means for solving complex problems.Materials and methods. When predicting the ANN, three methods of learning were used: 1 the Levenberg-Marquardt algorithm-network training stops when the generalization ceases to improve, which is shown by the increase in the mean square error of the output value; 2 Bayes regularization method - network training is stopped in accordance with the minimization of adaptive weights; 3 the method of scaled conjugate gradients, which is used to find the local extremum of a function on the basis of information about its values and gradient. The Neural Network Toolbox package is used for forecasting. The neural network model consists of a hidden layer of neurons with a sigmoidal activation function and an output neuron with a linear activation function, the input values of the dynamic time series, and the predicted value is removed from the output. For a more objective assessment of the prospects of the ANN application, the results of the forecast are presented in comparison with the results obtained in predicting the method of exponential smoothing.Results. When predicting the volumes of freight transportation by rail, satisfactory indicators of the verification of forecasting by both the method of exponential smoothing and ANN had been obtained, although the neural network

  14. From neural plate to cortical arousal-a neuronal network theory of sleep derived from in vitro "model" systems for primordial patterns of spontaneous bioelectric activity in the vertebrate central nervous system.

    Science.gov (United States)

    Corner, Michael A

    2013-05-22

    In the early 1960s intrinsically generated widespread neuronal discharges were discovered to be the basis for the earliest motor behavior throughout the animal kingdom. The pattern generating system is in fact programmed into the developing nervous system, in a regionally specific manner, already at the early neural plate stage. Such rhythmically modulated phasic bursts were next discovered to be a general feature of developing neural networks and, largely on the basis of experimental interventions in cultured neural tissues, to contribute significantly to their morpho-physiological maturation. In particular, the level of spontaneous synchronized bursting is homeostatically regulated, and has the effect of constraining the development of excessive network excitability. After birth or hatching, this "slow-wave" activity pattern becomes sporadically suppressed in favor of sensory oriented "waking" behaviors better adapted to dealing with environmental contingencies. It nevertheless reappears periodically as "sleep" at several species-specific points in the diurnal/nocturnal cycle. Although this "default" behavior pattern evolves with development, its essential features are preserved throughout the life cycle, and are based upon a few simple mechanisms which can be both experimentally demonstrated and simulated by computer modeling. In contrast, a late onto- and phylogenetic aspect of sleep, viz., the intermittent "paradoxical" activation of the forebrain so as to mimic waking activity, is much less well understood as regards its contribution to brain development. Some recent findings dealing with this question by means of cholinergically induced "aroused" firing patterns in developing neocortical cell cultures, followed by quantitative electrophysiological assays of immediate and longterm sequelae, will be discussed in connection with their putative implications for sleep ontogeny.

  15. System Error Compensation Methodology Based on a Neural Network for a Micromachined Inertial Measurement Unit

    Directory of Open Access Journals (Sweden)

    Shi Qiang Liu

    2016-01-01

    Full Text Available Errors compensation of micromachined-inertial-measurement-units (MIMU is essential in practical applications. This paper presents a new compensation method using a neural-network-based identification for MIMU, which capably solves the universal problems of cross-coupling, misalignment, eccentricity, and other deterministic errors existing in a three-dimensional integrated system. Using a neural network to model a complex multivariate and nonlinear coupling system, the errors could be readily compensated through a comprehensive calibration. In this paper, we also present a thermal-gas MIMU based on thermal expansion, which measures three-axis angular rates and three-axis accelerations using only three thermal-gas inertial sensors, each of which capably measures one-axis angular rate and one-axis acceleration simultaneously in one chip. The developed MIMU (100 × 100 × 100 mm3 possesses the advantages of simple structure, high shock resistance, and large measuring ranges (three-axes angular rates of ±4000°/s and three-axes accelerations of ±10 g compared with conventional MIMU, due to using gas medium instead of mechanical proof mass as the key moving and sensing elements. However, the gas MIMU suffers from cross-coupling effects, which corrupt the system accuracy. The proposed compensation method is, therefore, applied to compensate the system errors of the MIMU. Experiments validate the effectiveness of the compensation, and the measurement errors of three-axis angular rates and three-axis accelerations are reduced to less than 1% and 3% of uncompensated errors in the rotation range of ±600°/s and the acceleration range of ±1 g, respectively.

  16. Goal-Directed Behavior and Instrumental Devaluation: A Neural System-Level Computational Model.

    Science.gov (United States)

    Mannella, Francesco; Mirolli, Marco; Baldassarre, Gianluca

    2016-01-01

    Devaluation is the key experimental paradigm used to demonstrate the presence of instrumental behaviors guided by goals in mammals. We propose a neural system-level computational model to address the question of which brain mechanisms allow the current value of rewards to control instrumental actions. The model pivots on and shows the computational soundness of the hypothesis for which the internal representation of instrumental manipulanda (e.g., levers) activate the representation of rewards (or "action-outcomes", e.g., foods) while attributing to them a value which depends on the current internal state of the animal (e.g., satiation for some but not all foods). The model also proposes an initial hypothesis of the integrated system of key brain components supporting this process and allowing the recalled outcomes to bias action selection: (a) the sub-system formed by the basolateral amygdala and insular cortex acquiring the manipulanda-outcomes associations and attributing the current value to the outcomes; (b) three basal ganglia-cortical loops selecting respectively goals, associative sensory representations, and actions; (c) the cortico-cortical and striato-nigro-striatal neural pathways supporting the selection, and selection learning, of actions based on habits and goals. The model reproduces and explains the results of several devaluation experiments carried out with control rats and rats with pre- and post-training lesions of the basolateral amygdala, the nucleus accumbens core, the prelimbic cortex, and the dorso-medial striatum. The results support the soundness of the hypotheses of the model and show its capacity to integrate, at the system-level, the operations of the key brain structures underlying devaluation. Based on its hypotheses and predictions, the model also represents an operational framework to support the design and analysis of new experiments on the motivational aspects of goal-directed behavior.

  17. Noise-tolerant inverse analysis models for nondestructive evaluation of transportation infrastructure systems using neural networks

    Science.gov (United States)

    Ceylan, Halil; Gopalakrishnan, Kasthurirangan; Birkan Bayrak, Mustafa; Guclu, Alper

    2013-09-01

    The need to rapidly and cost-effectively evaluate the present condition of pavement infrastructure is a critical issue concerning the deterioration of ageing transportation infrastructure all around the world. Nondestructive testing (NDT) and evaluation methods are well-suited for characterising materials and determining structural integrity of pavement systems. The falling weight deflectometer (FWD) is a NDT equipment used to assess the structural condition of highway and airfield pavement systems and to determine the moduli of pavement layers. This involves static or dynamic inverse analysis (referred to as backcalculation) of FWD deflection profiles in the pavement surface under a simulated truck load. The main objective of this study was to employ biologically inspired computational systems to develop robust pavement layer moduli backcalculation algorithms that can tolerate noise or inaccuracies in the FWD deflection data collected in the field. Artificial neural systems, also known as artificial neural networks (ANNs), are valuable computational intelligence tools that are increasingly being used to solve resource-intensive complex engineering problems. Unlike the linear elastic layered theory commonly used in pavement layer backcalculation, non-linear unbound aggregate base and subgrade soil response models were used in an axisymmetric finite element structural analysis programme to generate synthetic database for training and testing the ANN models. In order to develop more robust networks that can tolerate the noisy or inaccurate pavement deflection patterns in the NDT data, several network architectures were trained with varying levels of noise in them. The trained ANN models were capable of rapidly predicting the pavement layer moduli and critical pavement responses (tensile strains at the bottom of the asphalt concrete layer, compressive strains on top of the subgrade layer and the deviator stresses on top of the subgrade layer), and also pavement

  18. Viewing pictures of a romantic partner reduces experimental pain: involvement of neural reward systems.

    Directory of Open Access Journals (Sweden)

    Jarred Younger

    2010-10-01

    Full Text Available The early stages of a new romantic relationship are characterized by intense feelings of euphoria, well-being, and preoccupation with the romantic partner. Neuroimaging research has linked those feelings to activation of reward systems in the human brain. The results of those studies may be relevant to pain management in humans, as basic animal research has shown that pharmacologic activation of reward systems can substantially reduce pain. Indeed, viewing pictures of a romantic partner was recently demonstrated to reduce experimental thermal pain. We hypothesized that pain relief evoked by viewing pictures of a romantic partner would be associated with neural activations in reward-processing centers. In this functional magnetic resonance imaging (fMRI study, we examined fifteen individuals in the first nine months of a new, romantic relationship. Participants completed three tasks under periods of moderate and high thermal pain: 1 viewing pictures of their romantic partner, 2 viewing pictures of an equally attractive and familiar acquaintance, and 3 a word-association distraction task previously demonstrated to reduce pain. The partner and distraction tasks both significantly reduced self-reported pain, although only the partner task was associated with activation of reward systems. Greater analgesia while viewing pictures of a romantic partner was associated with increased activity in several reward-processing regions, including the caudate head, nucleus accumbens, lateral orbitofrontal cortex, amygdala, and dorsolateral prefrontal cortex--regions not associated with distraction-induced analgesia. The results suggest that the activation of neural reward systems via non-pharmacologic means can reduce the experience of pain.

  19. System Error Compensation Methodology Based on a Neural Network for a Micromachined Inertial Measurement Unit.

    Science.gov (United States)

    Liu, Shi Qiang; Zhu, Rong

    2016-01-29

    Errors compensation of micromachined-inertial-measurement-units (MIMU) is essential in practical applications. This paper presents a new compensation method using a neural-network-based identification for MIMU, which capably solves the universal problems of cross-coupling, misalignment, eccentricity, and other deterministic errors existing in a three-dimensional integrated system. Using a neural network to model a complex multivariate and nonlinear coupling system, the errors could be readily compensated through a comprehensive calibration. In this paper, we also present a thermal-gas MIMU based on thermal expansion, which measures three-axis angular rates and three-axis accelerations using only three thermal-gas inertial sensors, each of which capably measures one-axis angular rate and one-axis acceleration simultaneously in one chip. The developed MIMU (100 × 100 × 100 mm³) possesses the advantages of simple structure, high shock resistance, and large measuring ranges (three-axes angular rates of ±4000°/s and three-axes accelerations of ± 10 g) compared with conventional MIMU, due to using gas medium instead of mechanical proof mass as the key moving and sensing elements. However, the gas MIMU suffers from cross-coupling effects, which corrupt the system accuracy. The proposed compensation method is, therefore, applied to compensate the system errors of the MIMU. Experiments validate the effectiveness of the compensation, and the measurement errors of three-axis angular rates and three-axis accelerations are reduced to less than 1% and 3% of uncompensated errors in the rotation range of ±600°/s and the acceleration range of ± 1 g, respectively.

  20. Design of FPGA Based Neural Network Controller for Earth Station Power System

    Directory of Open Access Journals (Sweden)

    Hassen T. Dorrah

    2012-06-01

    Full Text Available Automation of generating hardware description language code from neural networks models can highly decrease time of implementation those networks into a digital devices, thus significant money savings. To implement the neural network into hardware designer, it is required to translate generated model into device structure. VHDL language is used to describe those networks into hardware. VHDL code has been proposed to implement ANNs as well as to present simulation results with floating point arithmetic of the earth station and the satellite power systems using ModelSim PE 6.6 simulator tool. Integration between MATLAB and VHDL is used to save execution time of computation. The results shows that a good agreement between MATLAB and VHDL and a fast/flexible feed forward NN which is capable of dealing with floating point arithmetic operations; minimum number of CLB slices; and good speed of performance. FPGA synthesis results are obtained with view RTL schematic and technology schematic from Xilinix tool. Minimum number of utilized resources is obtained by using Xilinix VERTIX5.

  1. DeepFruits: A Fruit Detection System Using Deep Neural Networks.

    Science.gov (United States)

    Sa, Inkyu; Ge, Zongyuan; Dayoub, Feras; Upcroft, Ben; Perez, Tristan; McCool, Chris

    2016-08-03

    This paper presents a novel approach to fruit detection using deep convolutional neural networks. The aim is to build an accurate, fast and reliable fruit detection system, which is a vital element of an autonomous agricultural robotic platform; it is a key element for fruit yield estimation and automated harvesting. Recent work in deep neural networks has led to the development of a state-of-the-art object detector termed Faster Region-based CNN (Faster R-CNN). We adapt this model, through transfer learning, for the task of fruit detection using imagery obtained from two modalities: colour (RGB) and Near-Infrared (NIR). Early and late fusion methods are explored for combining the multi-modal (RGB and NIR) information. This leads to a novel multi-modal Faster R-CNN model, which achieves state-of-the-art results compared to prior work with the F1 score, which takes into account both precision and recall performances improving from 0 . 807 to 0 . 838 for the detection of sweet pepper. In addition to improved accuracy, this approach is also much quicker to deploy for new fruits, as it requires bounding box annotation rather than pixel-level annotation (annotating bounding boxes is approximately an order of magnitude quicker to perform). The model is retrained to perform the detection of seven fruits, with the entire process taking four hours to annotate and train the new model per fruit.

  2. DeepFruits: A Fruit Detection System Using Deep Neural Networks

    Directory of Open Access Journals (Sweden)

    Inkyu Sa

    2016-08-01

    Full Text Available This paper presents a novel approach to fruit detection using deep convolutional neural networks. The aim is to build an accurate, fast and reliable fruit detection system, which is a vital element of an autonomous agricultural robotic platform; it is a key element for fruit yield estimation and automated harvesting. Recent work in deep neural networks has led to the development of a state-of-the-art object detector termed Faster Region-based CNN (Faster R-CNN. We adapt this model, through transfer learning, for the task of fruit detection using imagery obtained from two modalities: colour (RGB and Near-Infrared (NIR. Early and late fusion methods are explored for combining the multi-modal (RGB and NIR information. This leads to a novel multi-modal Faster R-CNN model, which achieves state-of-the-art results compared to prior work with the F1 score, which takes into account both precision and recall performances improving from 0 . 807 to 0 . 838 for the detection of sweet pepper. In addition to improved accuracy, this approach is also much quicker to deploy for new fruits, as it requires bounding box annotation rather than pixel-level annotation (annotating bounding boxes is approximately an order of magnitude quicker to perform. The model is retrained to perform the detection of seven fruits, with the entire process taking four hours to annotate and train the new model per fruit.

  3. A 3D human neural cell culture system for modeling Alzheimer’s disease

    Science.gov (United States)

    Kim, Young Hye; Choi, Se Hoon; D’Avanzo, Carla; Hebisch, Matthias; Sliwinski, Christopher; Bylykbashi, Enjana; Washicosky, Kevin J.; Klee, Justin B.; Brüstle, Oliver; Tanzi, Rudolph E.; Kim, Doo Yeon

    2015-01-01

    Stem cell technologies have facilitated the development of human cellular disease models that can be used to study pathogenesis and test therapeutic candidates. These models hold promise for complex neurological diseases such as Alzheimer’s disease (AD) because existing animal models have been unable to fully recapitulate all aspects of pathology. We recently reported the characterization of a novel three-dimensional (3D) culture system that exhibits key events in AD pathogenesis, including extracellular aggregation of β-amyloid and accumulation of hyperphosphorylated tau. Here we provide instructions for the generation and analysis of 3D human neural cell cultures, including the production of genetically modified human neural progenitor cells (hNPCs) with familial AD mutations, the differentiation of the hNPCs in a 3D matrix, and the analysis of AD pathogenesis. The 3D culture generation takes 1–2 days. The aggregation of β-amyloid is observed after 6-weeks of differentiation followed by robust tau pathology after 10–14 weeks. PMID:26068894

  4. An Artificial Neural Network for Analyzing Overall Uniformity in Outdoor Lighting Systems

    Directory of Open Access Journals (Sweden)

    Antonio del Corte-Valiente

    2017-02-01

    Full Text Available Street lighting installations are an essential service for modern life due to their capability of creating a welcoming feeling at nighttime. Nevertheless, several studies have highlighted that it is possible to improve the quality of the light significantly improving the uniformity of the illuminance. The main difficulty arises when trying to improve some of the installation’s characteristics based only on statistical analysis of the light distribution. This paper presents a new algorithm that is able to obtain the overall illuminance uniformity in order to improve this sort of installations. To develop this algorithm it was necessary to perform a detailed study of all the elements which are part of street lighting installations. Because classification is one of the most important tasks in the application areas of artificial neural networks, we compared the performances of six types of training algorithms in a feed forward neural network for analyzing the overall uniformity in outdoor lighting systems. We found that the best algorithm that minimizes the error is “Levenberg-Marquardt back-propagation”, which approximates the desired output of the training pattern. By means of this kind of algorithm, it is possible to help to lighting professionals optimize the quality of street lighting installations.

  5. Insular neural system controls decision-making in healthy and methamphetamine-treated rats

    Science.gov (United States)

    Mizoguchi, Hiroyuki; Katahira, Kentaro; Inutsuka, Ayumu; Fukumoto, Kazuya; Nakamura, Akihiro; Wang, Tian; Nagai, Taku; Sato, Jun; Sawada, Makoto; Ohira, Hideki; Yamanaka, Akihiro; Yamada, Kiyofumi

    2015-01-01

    Patients suffering from neuropsychiatric disorders such as substance-related and addictive disorders exhibit altered decision-making patterns, which may be associated with their behavioral abnormalities. However, the neuronal mechanisms underlying such impairments are largely unknown. Using a gambling test, we demonstrated that methamphetamine (METH)-treated rats chose a high-risk/high-reward option more frequently and assigned higher value to high returns than control rats, suggestive of changes in decision-making choice strategy. Immunohistochemical analysis following the gambling test revealed aberrant activation of the insular cortex (INS) and nucleus accumbens in METH-treated animals. Pharmacological studies, together with in vivo microdialysis, showed that the insular neural system played a crucial role in decision-making. Moreover, manipulation of INS activation using designer receptor exclusively activated by designer drug technology resulted in alterations to decision-making. Our findings suggest that the INS is a critical region involved in decision-making and that insular neural dysfunction results in risk-taking behaviors associated with altered decision-making. PMID:26150496

  6. Efficient Approach for RLS Type Learning in TSK Neural Fuzzy Systems.

    Science.gov (United States)

    Yeh, Jen-Wei; Su, Shun-Feng

    2017-09-01

    This paper presents an efficient approach for the use of recursive least square (RLS) learning algorithm in Takagi-Sugeno-Kang neural fuzzy systems. In the use of RLS, reduced covariance matrix, of which the off-diagonal blocks defining the correlation between rules are set to zeros, may be employed to reduce computational burden. However, as reported in the literature, the performance of such an approach is slightly worse than that of using the full covariance matrix. In this paper, we proposed a so-called enhanced local learning concept in which a threshold is considered to stop learning for those less fired rules. It can be found from our experiments that the proposed approach can have better performances than that of using the full covariance matrix. Enhanced local learning method can be more active on the structure learning phase. Thus, the method not only can stop the update for insufficiently fired rules to reduce disturbances in self-constructing neural fuzzy inference network but also raises the learning speed on structure learning phase by using a large backpropagation learning constant.

  7. A Hypergraph and Arithmetic Residue-based Probabilistic Neural Network for classification in Intrusion Detection Systems.

    Science.gov (United States)

    Raman, M R Gauthama; Somu, Nivethitha; Kirthivasan, Kannan; Sriram, V S Shankar

    2017-08-01

    Over the past few decades, the design of an intelligent Intrusion Detection System (IDS) remains an open challenge to the research community. Continuous efforts by the researchers have resulted in the development of several learning models based on Artificial Neural Network (ANN) to improve the performance of the IDSs. However, there exists a tradeoff with respect to the stability of ANN architecture and the detection rate for less frequent attacks. This paper presents a novel approach based on Helly property of Hypergraph and Arithmetic Residue-based Probabilistic Neural Network (HG AR-PNN) to address the classification problem in IDS. The Helly property of Hypergraph was exploited for the identification of the optimal feature subset and the arithmetic residue of the optimal feature subset was used to train the PNN. The performance of HG AR-PNN was evaluated using KDD CUP 1999 intrusion dataset. Experimental results prove the dominance of HG AR-PNN classifier over the existing classifiers with respect to the stability and improved detection rate for less frequent attacks. Copyright © 2017 Elsevier Ltd. All rights reserved.

  8. Implementasi Neural Fuzzy Inference System dan Algoritma Pelatihan Levenberg-Marquardt untuk Prediksi Curah Hujan

    Directory of Open Access Journals (Sweden)

    Nola Ritha

    2016-07-01

    Full Text Available Rainfall prediction can be used for various purposes and the accuracy in predicting is important in many ways.  In this research, data of rainfall prediction use daily rainfall data from 2013-2014 years at rainfall station in Putussibau, West Kalimantan. Rainfall prediction using four parameters: mean temperature, average humidity, wind speed and mean sea level pressure. This research to determine how performance Neural Fuzzy Inference System with Levenberg-Marquardt training algorithm for rainfall prediction. Fuzzy logic can be used to resolve the linguistic variables used in rule of rainfall. While neural networks have ability to adapt and learning process, due to recognize patterns of data from input need training to prediction. And Levenberg-Marquardt algorithm is used for training because of effectiveness and convergence acceleration. The results showed five models NFIS-LM developed using a variety of membership functions as input obtained that model NFIS-LM with twelve of membership functions and use four inputs, such as mean temperature, average humidity, wind speed and mean sea level pressure gives best results to predict rainfall with values Mean Square Error (MSE of 0.0262050. When compared with model NN-Backpropagation, NFIS-LM models showed lower accuracy. It is shown from MSE generated where model NN-Backpropagation generate MSE of 0.0167990.

  9. Intelligent Monitoring System on Prediction of Building Damage Index using Artificial Neural Network

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    Reni Suryanita

    2012-03-01

    Full Text Available An earthquake potentially destroys a tall building. The building damage can be indexed by FEMA into three categories namely Immediate Occupancy (IO, Life Safety (LS, and Collapse Prevention (CP. To determine the damage index, the building model has been simulated into structure analysis software. Acceleration data has been analyzed using non linear method in structure analysis program. The earthquake load is time history at surface, PGA=0105g. This work proposes an intelligent monitoring system utilizing Artificial Neural Network to predict the building damage index. The system also provides an alert system and notification to inform the status of the damage. Data learning is trained on ANN utilizing feed forward and back propagation algorithm. The alert system is designed to be able to activate the alarm sound, view the alert bar or text, and send notification via email to the security or management. The system is tested using sample data represented in three conditions involving IO, LS, and CP. The results show that the proposed intelligent monitoring system could provide prediction of up to 92% rate of accuracy and activate the alert. Implementation of the system in building monitoring would allow for rapid, intelligent and accurate prediction of the building damage index due to earthquake.

  10. Raised Frequency of Central Nervous System Malformations Related to Zika Virus Infection in Two Birth Defects Surveillance Systems in Bogotá and Cali, Colombia.

    Science.gov (United States)

    Hurtado-Villa, Paula; Puerto, Angie K; Victoria, Salomé; Gracia, Gloria; Guasmayán, Lesly; Arce, Patricia; Álvarez, Gilberto; Blandón, Esperanza; Rengifo, Nubia; Holguín, Jorge A; Durán, Alexander; Zarante, Ignacio

    2017-07-01

    Zika virus infection during pregnancy is now known to cause congenital microcephaly and severe brain defects. Colombia has been experiencing an epidemic wave of Zika infection, starting approximately in October 2015. Here we document the trends of microcephaly and severe CNS malformations in two major cities in Colombia from 2012 through 2016, tracking the epidemiologic curve from before through the major Zika epidemic so far. The study included neural tube defects (anencephaly, spina bifida, encephalocele), holoprosencephaly, and hydrocephaly. In 2016, rates of microcephaly appeared to start increasing around May (8 months after the reported start of the Zika virus epidemic), peaking in July, and declining through December 2016 to levels close to those reported in May 2016. If these estimates are generalizable to the 2016 birth cohort in the cities of Cali and Bogota, then 121 cases of severe microcephaly associated with Zika virus epidemic would be expected in both cities and 588 cases in Colombia for 2016. The occurrence of microcephaly appears to have increased nearly four-fold in two large cities in Colombia, concurrently with the reported Zika virus epidemic in the country. For other major CNS anomalies the evidence is less clear, and further studies are indicated.

  11. Neural Network Based Finite-Time Stabilization for Discrete-Time Markov Jump Nonlinear Systems with Time Delays

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    Fei Chen

    2013-01-01

    Full Text Available This paper deals with the finite-time stabilization problem for discrete-time Markov jump nonlinear systems with time delays and norm-bounded exogenous disturbance. The nonlinearities in different jump modes are parameterized by neural networks. Subsequently, a linear difference inclusion state space representation for a class of neural networks is established. Based on this, sufficient conditions are derived in terms of linear matrix inequalities to guarantee stochastic finite-time boundedness and stochastic finite-time stabilization of the closed-loop system. A numerical example is illustrated to verify the efficiency of the proposed technique.

  12. Prescribed performance synchronization controller design of fractional-order chaotic systems: An adaptive neural network control approach

    Directory of Open Access Journals (Sweden)

    Yuan Li

    2017-03-01

    Full Text Available In this study, an adaptive neural network synchronization (NNS approach, capable of guaranteeing prescribed performance (PP, is designed for non-identical fractional-order chaotic systems (FOCSs. For PP synchronization, we mean that the synchronization error converges to an arbitrary small region of the origin with convergence rate greater than some function given in advance. Neural networks are utilized to estimate unknown nonlinear functions in the closed-loop system. Based on the integer-order Lyapunov stability theorem, a fractional-order adaptive NNS controller is designed, and the PP can be guaranteed. Finally, simulation results are presented to confirm our results.

  13. Prescribed performance synchronization controller design of fractional-order chaotic systems: An adaptive neural network control approach

    Science.gov (United States)

    Li, Yuan; Lv, Hui; Jiao, Dongxiu

    2017-03-01

    In this study, an adaptive neural network synchronization (NNS) approach, capable of guaranteeing prescribed performance (PP), is designed for non-identical fractional-order chaotic systems (FOCSs). For PP synchronization, we mean that the synchronization error converges to an arbitrary small region of the origin with convergence rate greater than some function given in advance. Neural networks are utilized to estimate unknown nonlinear functions in the closed-loop system. Based on the integer-order Lyapunov stability theorem, a fractional-order adaptive NNS controller is designed, and the PP can be guaranteed. Finally, simulation results are presented to confirm our results.

  14. Magnitude of Birth Defects in Central and Northwest Ethiopia from 2010-2014: A Descriptive Retrospective Study.

    Directory of Open Access Journals (Sweden)

    Molla Taye

    Full Text Available Birth defects are defined as structural and functional defects that develop during the organogenesis period and present at birth or detected later in life. They are one of the leading causes of infant and child mortality, morbidity, and long term disability. The magnitude of birth defects varies from country to country and from race/ethnicity to race/ethnicity, and about 40-60% of their causes are unknown. The known causes of birth defects are genetic and environmental factors which may be prevented. For various reasons, there is lack of data and research on birth defects in Ethiopia.The major objective of this study is to estimate the magnitude of birth defects in Ethiopia.A hospital based, retrospective, cross sectional, descriptive study was conducted. The subjects were babies/children aged 0-17years who visited selected hospitals between 2010 and 2014. Fourteen hospitals (8 in Addis Ababa, 6 in Amhara Region were selected purposively based on case load. A data retrieving form was developed to extract relevant information from record books.In the hospitals mentioned, 319,776 various medical records of children aged 0-17years were found. Of these, 6,076 (1.9% with 95% CI: 1.85%-1.95% children were diagnosed as having birth defects. The majority (58.5% of the children were male and 41.5% female. A slightly more than half (51.1% of the children were urban dwellers, while 48.9% were from rural areas. Among the participants of the study the proportion of birth defects ranged as follows: orofacial (34.2%, neural tube (30.8%, upper and lower limb (12.8%, cardiovascular system (10.3%, digestive system and abdominal wall (4.8%, unspecified congenital malformations (2.5%, Down syndrome (2%, genitourinary system (2%, head, face, and neck defects (0.4%, and others (0.3%. The trend of birth defects increased linearly over time [Extended Mantel-Haenszel chi square for linear trend = 356.7 (P<0.0001]. About 275 (4.5% of the cases had multiple (associated

  15. Minimally-Invasive Neural Interface for Distributed Wireless Electrocorticogram Recording Systems

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    Sun-Il Chang

    2018-01-01

    Full Text Available This paper presents a minimally-invasive neural interface for distributed wireless electrocorticogram (ECoG recording systems. The proposed interface equips all necessary components for ECoG recording, such as the high performance front-end integrated circuits, a fabricated flexible microelectrode array, and wireless communication inside a miniaturized custom-made platform. The multiple units of the interface systems can be deployed to cover a broad range of the target brain region and transmit signals via a built-in intra-skin communication (ISCOM module. The core integrated circuit (IC consists of 16-channel, low-power push-pull double-gated preamplifiers, in-channel successive approximation register analog-to-digital converters (SAR ADC with a single-clocked bootstrapping switch and a time-delayed control unit, an ISCOM module for wireless data transfer through the skin instead of a power-hungry RF wireless transmitter, and a monolithic voltage/current reference generator to support the aforementioned analog and mixed-signal circuit blocks. The IC was fabricated using 250 nm CMOS processes in an area of 3.2 × 0.9 mm2 and achieved the low-power operation of 2.5 µW per channel. Input-referred noise was measured as 5.62 µVrms for 10 Hz to 10 kHz and ENOB of 7.21 at 31.25 kS/s. The implemented system successfully recorded multi-channel neural activities in vivo from a primate and demonstrated modular expandability using the ISCOM with power consumption of 160 µW.

  16. Minimally-Invasive Neural Interface for Distributed Wireless Electrocorticogram Recording Systems.

    Science.gov (United States)

    Chang, Sun-Il; Park, Sung-Yun; Yoon, Euisik

    2018-01-17

    This paper presents a minimally-invasive neural interface for distributed wireless electrocorticogram (ECoG) recording systems. The proposed interface equips all necessary components for ECoG recording, such as the high performance front-end integrated circuits, a fabricated flexible microelectrode array, and wireless communication inside a miniaturized custom-made platform. The multiple units of the interface systems can be deployed to cover a broad range of the target brain region and transmit signals via a built-in intra-skin communication (ISCOM) module. The core integrated circuit (IC) consists of 16-channel, low-power push-pull double-gated preamplifiers, in-channel successive approximation register analog-to-digital converters (SAR ADC) with a single-clocked bootstrapping switch and a time-delayed control unit, an ISCOM module for wireless data transfer through the skin instead of a power-hungry RF wireless transmitter, and a monolithic voltage/current reference generator to support the aforementioned analog and mixed-signal circuit blocks. The IC was fabricated using 250 nm CMOS processes in an area of 3.2 × 0.9 mm² and achieved the low-power operation of 2.5 µW per channel. Input-referred noise was measured as 5.62 µVrms for 10 Hz to 10 kHz and ENOB of 7.21 at 31.25 kS/s. The implemented system successfully recorded multi-channel neural activities in vivo from a primate and demonstrated modular expandability using the ISCOM with power consumption of 160 µW.

  17. GAUSSIAN MIXTURE MODELS FOR ADAPTATION OF DEEP NEURAL NETWORK ACOUSTIC MODELS IN AUTOMATIC SPEECH RECOGNITION SYSTEMS

    Directory of Open Access Journals (Sweden)

    Natalia A. Tomashenko

    2016-11-01

    Full Text Available Subject of Research. We study speaker adaptation of deep neural network (DNN acoustic models in automatic speech recognition systems. The aim of speaker adaptation techniques is to improve the accuracy of the speech recognition system for a particular speaker. Method. A novel method for training and adaptation of deep neural network acoustic models has been developed. It is based on using an auxiliary GMM (Gaussian Mixture Models model and GMMD (GMM-derived features. The principle advantage of the proposed GMMD features is the possibility of performing the adaptation of a DNN through the adaptation of the auxiliary GMM. In the proposed approach any methods for the adaptation of the auxiliary GMM can be used, hence, it provides a universal method for transferring adaptation algorithms developed for GMMs to DNN adaptation.Main Results. The effectiveness of the proposed approach was shown by means of one of the most common adaptation algorithms for GMM models – MAP (Maximum A Posteriori adaptation. Different ways of integration of the proposed approach into state-of-the-art DNN architecture have been proposed and explored. Analysis of choosing the type of the auxiliary GMM model is given. Experimental results on the TED-LIUM corpus demonstrate that, in an unsupervised adaptation mode, the proposed adaptation technique can provide, approximately, a 11–18% relative word error reduction (WER on different adaptation sets, compared to the speaker-independent DNN system built on conventional features, and a 3–6% relative WER reduction compared to the SAT-DNN trained on fMLLR adapted features.

  18. Artificial neural network-aided image analysis system for cell counting.

    Science.gov (United States)

    Sjöström, P J; Frydel, B R; Wahlberg, L U

    1999-05-01

    In histological preparations containing debris and synthetic materials, it is difficult to automate cell counting using standard image analysis tools, i.e., systems that rely on boundary contours, histogram thresholding, etc. In an attempt to mimic manual cell recognition, an automated cell counter was constructed using a combination of artificial intelligence and standard image analysis methods. Artificial neural network (ANN) methods were applied on digitized microscopy fields without pre-ANN feature extraction. A three-layer feed-forward network with extensive weight sharing in the first hidden layer was employed and trained on 1,830 examples using the error back-propagation algorithm on a Power Macintosh 7300/180 desktop computer. The optimal number of hidden neurons was determined and the trained system was validated by comparison with blinded human counts. System performance at 50x and lO0x magnification was evaluated. The correlation index at 100x magnification neared person-to-person variability, while 50x magnification was not useful. The system was approximately six times faster than an experienced human. ANN-based automated cell counting in noisy histological preparations is feasible. Consistent histology and computer power are crucial for system performance. The system provides several benefits, such as speed of analysis and consistency, and frees up personnel for other tasks.

  19. Artificial neural networks and adaptive neuro-fuzzy assessments for ground-coupled heat pump system

    Energy Technology Data Exchange (ETDEWEB)

    Esen, Hikmet; Esen, Mehmet [Department of Mechanical Education, Faculty of Technical Education, Firat University, 23119 Elazig (Turkey); Inalli, Mustafa [Department of Mechanical Engineering, Faculty of Engineering, Firat University, 23279 Elazig (Turkey); Sengur, Abdulkadir [Department of Electronic and Computer Science, Faculty of Technical Education, Firat University, 23119 Elazig (Turkey)

    2008-07-01

    This article present a comparison of artificial neural network (ANN) and adaptive neuro-fuzzy inference systems (ANFIS) applied for modelling a ground-coupled heat pump system (GCHP). The aim of this study is predicting system performance related to ground and air (condenser inlet and outlet) temperatures by using desired models. Performance forecasting is the precondition for the optimal design and energy-saving operation of air-conditioning systems. So obtained models will help the system designer to realize this precondition. The most suitable algorithm and neuron number in the hidden layer are found as Levenberg-Marquardt (LM) with seven neurons for ANN model whereas the most suitable membership function and number of membership functions are found as Gauss and two, respectively, for ANFIS model. The root-mean squared (RMS) value and the coefficient of variation in percent (cov) value are 0.0047 and 0.1363, respectively. The absolute fraction of variance (R{sup 2}) is 0.9999 which can be considered as very promising. This paper shows the appropriateness of ANFIS for the quantitative modeling of GCHP systems. (author)

  20. RBFNDOB-based neural network inverse control for non-minimum phase MIMO system with disturbances.

    Science.gov (United States)

    Li, Juan; Li, Shihua; Chen, Xisong; Yang, Jun

    2014-07-01

    An adaptive control strategy combining neural network inverse controller (NNIC) with RBFN disturbance observer (RBFNDOB) is developed for a multi-input-multi-output (MIMO) system with non-minimum phase, internal and external disturbances in this paper. Since the inverse model of system is unstable due to the non-minimum phase, a pseudo-plant is constructed, then the RBFN is used to identify the inverse model of pseudo-plant, which can track the parameter variations of system. By copying the structure and parameters of the identifier, the NNIC is obtained. Cascading the NNIC with the original plant, the MIMO system can be decoupled and linearized into independent SISO systems. For the independent decoupled system, the RBFNDOB employs a RBFN to observe the external disturbances and this estimate value is used as a feed-forward compensation term in controller. The case study on ball mill grinding circuit is presented. The effectiveness of the proposed method is demonstrated by simulation results and comparisons. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

  1. Quantum inspired PSO for the optimization of simultaneous recurrent neural networks as MIMO learning systems.

    Science.gov (United States)

    Luitel, Bipul; Venayagamoorthy, Ganesh Kumar

    2010-06-01

    Training a single simultaneous recurrent neural network (SRN) to learn all outputs of a multiple-input-multiple-output (MIMO) system is a difficult problem. A new training algorithm developed from combined concepts of swarm intelligence and quantum principles is presented. The training algorithm is called particle swarm optimization with quantum infusion (PSO-QI). To improve the effectiveness of learning, a two-step learning approach is introduced in the training. The objective of the learning in the first step is to find the optimal set of weights in the SRN considering all output errors. In the second step, the objective is to maximize the learning of each output dynamics by fine tuning the respective SRN output weights. To demonstrate the effectiveness of the PSO-QI training algorithm and the two-step learning approach, two examples of an SRN learning MIMO systems are presented. The first example is learning a benchmark MIMO system and the second one is the design of a wide area monitoring system for a multimachine power system. From the results, it is observed that SRNs can effectively learn MIMO systems when trained using the PSO-QI algorithm and the two-step learning approach. Copyright 2009 Elsevier Ltd. All rights reserved.

  2. Development of a highly accurate OCR system by neural network approach

    Science.gov (United States)

    Poon, Joe C. H.; Man, Gary M. T.; Hung, Yan C.

    1993-04-01

    We have developed an application software system for font and size invariant optical character recognition (OCR). A preliminary front-end process, which handles gray scale normalization, noise elimination, line finding, and character block segmentation, has been included in our system. But the main characteristic of our system is that we adopt a Fourier descriptors (FDs) based feature extraction approach and a multicategory back-propagation neural network classifier for the recognition. Instead of using one set of FDs to represent the image object as in conventional FDs approach, we use three sets of FDs to represent different portions of the object. We find this approach can solve the intrinsic problem of FDs caused by their rotational and reflectional invariance properties. Thus, our existing method can correctly classify ambiguous characters like 5, 2, p, q, etc. Our system will become a primitive building block for later more complex OCR systems. In general, the back propagation provides a gradient descent optimization in training. Without prior knowledge of the mathematical relation between the input and output, the network is highly efficient to map a large variety set of input patterns to an arbitrary set of output patterns after successful training. Thus, the system is not only capable of understanding printed English text and isolated cursive scripts, but also can be extended to read other symbols at the expense of additional training time. At present, a 99.8 percent rate of recognition accuracy has already been achieved for trained English samples in our experiment.

  3. Controlling selective stimulations below a spinal cord hemisection using brain recordings with a neural interface system approach

    Science.gov (United States)

    Panetsos, Fivos; Sanchez-Jimenez, Abel; Torets, Carlos; Largo, Carla; Micera, Silvestro

    2011-08-01

    In this work we address the use of realtime cortical recordings for the generation of coherent, reliable and robust motor activity in spinal-lesioned animals through selective intraspinal microstimulation (ISMS). The spinal cord of adult rats was hemisectioned and groups of multielectrodes were implanted in both the central nervous system (CNS) and the spinal cord below the lesion level to establish a neural system interface (NSI). To test the reliability of this new NSI connection, highly repeatable neural responses recorded from the CNS were used as a pattern generator of an open-loop control strategy for selective ISMS of the spinal motoneurons. Our experimental procedure avoided the spontaneous non-controlled and non-repeatable neural activity that could have generated spurious ISMS and the consequent undesired muscle contractions. Combinations of complex CNS patterns generated precisely coordinated, reliable and robust motor actions.

  4. Robust Decentralized Adaptive Neural Control for a Class of Nonaffine Nonlinear Large-Scale Systems with Unknown Dead Zones

    Directory of Open Access Journals (Sweden)

    Huanqing Wang

    2014-01-01

    Full Text Available The problem of robust decentralized adaptive neural stabilization control is investigated for a class of nonaffine nonlinear interconnected large-scale systems with unknown dead zones. In the controller design procedure, radical basis function (RBF neural networks are applied to approximate packaged unknown nonlinearities and then an adaptive neural decentralized controller is systematically derived without requiring any information on the boundedness of dead zone parameters (slopes and break points. It is proven that the developed control scheme can ensure that all the signals in the closed-loop system are semiglobally uniformly ultimately bounded in the sense of mean square. Simulation study is provided to further demonstrate the effectiveness of the developed control scheme.

  5. A neural-network-based model for the dynamic simulation of the tire/suspension system while traversing road irregularities.

    Science.gov (United States)

    Guarneri, Paolo; Rocca, Gianpiero; Gobbi, Massimiliano

    2008-09-01

    This paper deals with the simulation of the tire/suspension dynamics by using recurrent neural networks (RNNs). RNNs are derived from the multilayer feedforward neural networks, by adding feedback connections between output and input layers. The optimal network architecture derives from a parametric analysis based on the optimal tradeoff between network accuracy and size. The neural network can be trained with experimental data obtained in the laboratory from simulated road profiles (cleats). The results obtained from the neural network demonstrate good agreement with the experimental results over a wide range of operation conditions. The NN model can be effectively applied as a part of vehicle system model to accurately predict elastic bushings and tire dynamics behavior. Although the neural network model, as a black-box model, does not provide a good insight of the physical behavior of the tire/suspension system, it is a useful tool for assessing vehicle ride and noise, vibration, harshness (NVH) performance due to its good computational efficiency and accuracy.

  6. Learning ensembles of neural networks by means of a Bayesian artificial immune system.

    Science.gov (United States)

    Castro, Pablo A Dalbem; Von Zuben, Fernando José

    2011-02-01

    In this paper, we apply an immune-inspired approach to design ensembles of heterogeneous neural networks for classification problems. Our proposal, called Bayesian artificial immune system, is an estimation of distribution algorithm that replaces the traditional mutation and cloning operators with a probabilistic model, more specifically a Bayesian network, representing the joint distribution of promising solutions. Among the additional attributes provided by the Bayesian framework inserted into an immune-inspired search algorithm are the automatic control of the population size along the search and the inherent ability to promote and preserve diversity among the candidate solutions. Both are attributes generally absent from alternative estimation of distribution algorithms, and both were shown to be useful attributes when implementing the generation and selection of components of the ensemble, thus leading to high-performance classifiers. Several aspects of the design are illustrated in practical applications, including a comparative analysis with other attempts to synthesize ensembles.

  7. Safety Assessment for Electrical Motor Drive System Based on SOM Neural Network

    Directory of Open Access Journals (Sweden)

    Linghui Meng

    2016-01-01

    Full Text Available With the development of the urban rail train, safety and reliability have become more and more important. In this paper, the fault degree and health degree of the system are put forward based on the analysis of electric motor drive system’s control principle. With the self-organizing neural network’s advantage of competitive learning and unsupervised clustering, the system’s health clustering and safety identification are worked out. With the switch devices’ faults data obtained from the dSPACE simulation platform, the health assessment algorithm is verified. And the results show that the algorithm can achieve the system’s fault diagnosis and health assessment, which has a point in the health assessment and maintenance for the train.

  8. Using System Dynamic Model and Neural Network Model to Analyse Water Scarcity in Sudan

    Science.gov (United States)

    Li, Y.; Tang, C.; Xu, L.; Ye, S.

    2017-07-01

    Many parts of the world are facing the problem of Water Scarcity. Analysing Water Scarcity quantitatively is an important step to solve the problem. Water scarcity in a region is gauged by WSI (water scarcity index), which incorporate water supply and water demand. To get the WSI, Neural Network Model and SDM (System Dynamic Model) that depict how environmental and social factors affect water supply and demand are developed to depict how environmental and social factors affect water supply and demand. The uneven distribution of water resource and water demand across a region leads to an uneven distribution of WSI within this region. To predict WSI for the future, logistic model, Grey Prediction, and statistics are applied in predicting variables. Sudan suffers from severe water scarcity problem with WSI of 1 in 2014, water resource unevenly distributed. According to the result of modified model, after the intervention, Sudan’s water situation will become better.

  9. Visual evoked potential estimation by adaptive noise cancellation with neural-network-based fuzzy inference system.

    Science.gov (United States)

    Zeng, Y; Zhang, J; Yin, H; Pan, Y

    2007-01-01

    Visual evoked potentials (VEPs) are time-varying signals typically buried in relatively large background noise known as the electroencephalogram (EEG). In this paper, an adaptive noise cancellation with neural network-based fuzzy inference system (NNFIS) was used and the NNFIS was carefully designed to model the VEP signal. It is assumed that VEP responses can be modelled by NNFIS with the centres of its membership functions evenly distributed over time. The weights of NNFIS are adaptively determined by minimizing the variance of the error signal using the least mean squares (LMS) algorithm. As the NNFIS is dynamic to any change of VEP, the non-stationary characteristics of VEP can be tracked. Thus, this method should be able to track the VEP. Four sets of simulated data indicate that the proposed method is appropriate to estimate VEP. A total of 150 trials are processed to demonstrate the superior performance of the proposed method.

  10. First Steps Toward Incorporating Image Based Diagnostics Into Particle Accelerator Control Systems Using Convolutional Neural Networks

    Energy Technology Data Exchange (ETDEWEB)

    Edelen, A. L.; Biedron, S. G.; Milton, S. V.; Edelen, J. P.

    2016-12-16

    At present, a variety of image-based diagnostics are used in particle accelerator systems. Often times, these are viewed by a human operator who then makes appropriate adjustments to the machine. Given recent advances in using convolutional neural networks (CNNs) for image processing, it should be possible to use image diagnostics directly in control routines (NN-based or otherwise). This is especially appealing for non-intercepting diagnostics that could run continuously during beam operation. Here, we show results of a first step toward implementing such a controller: our trained CNN can predict multiple simulated downstream beam parameters at the Fermilab Accelerator Science and Technology (FAST) facility's low energy beamline using simulated virtual cathode laser images, gun phases, and solenoid strengths.

  11. Artificial neural network-based predictive emission monitoring system for NOx emissions

    Energy Technology Data Exchange (ETDEWEB)

    Ciccone, A.; Cinnamon, C.; Niejadlik, P.R. [TransCanada Energy Ltd., Toronto, ON (Canada)]|[Golder Associates, Toronto, ON (Canada)

    2005-07-01

    Considering the nature of long term power supply contracts that do not include mechanisms for cost recovery, developing cost-effective ways to handle changes in legislation impacting on facilities already in operation is extremely important. Also of importance is the age of the facilities, since continuous emissions monitoring (CEM) systems were not required when they were originally put into operation, but they are not yet old enough for capital stock turnover to allow for equipment changes or transition to new operations. An alternative monitoring method that is less expensive and as accurate as traditional (CEM) systems is discussed. TransCanada Energy Ltd., developed a predictive emission monitoring (PEM) system that achieved the required accuracy of the regulatory authorities using four of its gas turbine power plant facilities. Using the power plant operation variables to predict the nitric oxide (NO) portion of the exhaust emissions, the systems are founded on an artificial neural network (ANN). This paper provides a summary of the PEM system architecture and provides background information on the facilities used in the development of this approach. It was concluded that the PEM system provides a cost effective method to monitor emissions accurately and reliably at low emitting natural gas fired facilities. As well, there is a great potential for the system to be used by other industries to monitor and report emissions. The PEM system is an ideal system for the low emitting natural gas fired generating plants however the system could be adapted for other types of industries. 5 refs., 5 tabs., 2 figs.

  12. Evolution of the Galaxy and the Birth of the Solar System: The Short ...

    Indian Academy of Sciences (India)

    Sahijpal & Gupta 2013). Apart from understanding the entire temporal evolution of the galaxy, one of the major emphasis in developing the GCE models is to under- stand the origin of stable isotopic abundance of the solar system (Anders & Grevesse. 1989; Asplund et al. 2009). The solar system formed around 4.56 Gyr ...

  13. Deep Recurrent Neural Networks for seizure detection and early seizure detection systems

    Energy Technology Data Exchange (ETDEWEB)

    Talathi, S. S. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

    2017-06-05

    Epilepsy is common neurological diseases, affecting about 0.6-0.8 % of world population. Epileptic patients suffer from chronic unprovoked seizures, which can result in broad spectrum of debilitating medical and social consequences. Since seizures, in general, occur infrequently and are unpredictable, automated seizure detection systems are recommended to screen for seizures during long-term electroencephalogram (EEG) recordings. In addition, systems for early seizure detection can lead to the development of new types of intervention systems that are designed to control or shorten the duration of seizure events. In this article, we investigate the utility of recurrent neural networks (RNNs) in designing seizure detection and early seizure detection systems. We propose a deep learning framework via the use of Gated Recurrent Unit (GRU) RNNs for seizure detection. We use publicly available data in order to evaluate our method and demonstrate very promising evaluation results with overall accuracy close to 100 %. We also systematically investigate the application of our method for early seizure warning systems. Our method can detect about 98% of seizure events within the first 5 seconds of the overall epileptic seizure duration.

  14. Towards Artificial Speech Therapy: A Neural System for Impaired Speech Segmentation.

    Science.gov (United States)

    Iliya, Sunday; Neri, Ferrante

    2016-09-01

    This paper presents a neural system-based technique for segmenting short impaired speech utterances into silent, unvoiced, and voiced sections. Moreover, the proposed technique identifies those points of the (voiced) speech where the spectrum becomes steady. The resulting technique thus aims at detecting that limited section of the speech which contains the information about the potential impairment of the speech. This section is of interest to the speech therapist as it corresponds to the possibly incorrect movements of speech organs (lower lip and tongue with respect to the vocal tract). Two segmentation models to detect and identify the various sections of the disordered (impaired) speech signals have been developed and compared. The first makes use of a combination of four artificial neural networks. The second is based on a support vector machine (SVM). The SVM has been trained by means of an ad hoc nested algorithm whose outer layer is a metaheuristic while the inner layer is a convex optimization algorithm. Several metaheuristics have been tested and compared leading to the conclusion that some variants of the compact differential evolution (CDE) algorithm appears to be well-suited to address this problem. Numerical results show that the SVM model with a radial basis function is capable of effective detection of the portion of speech that is of interest to a therapist. The best performance has been achieved when the system is trained by the nested algorithm whose outer layer is hybrid-population-based/CDE. A population-based approach displays the best performance for the isolation of silence/noise sections, and the detection of unvoiced sections. On the other hand, a compact approach appears to be clearly well-suited to detect the beginning of the steady state of the voiced signal. Both the proposed segmentation models display outperformed two modern segmentation techniques based on Gaussian mixture model and deep learning.

  15. Proliferation of reticuloendothelial system (RES) in rats with altered vision or smell (to the hypothetic neural regulation of the RES).

    Science.gov (United States)

    Jansa, P; Urbánek, K; Riegrová, D

    1993-12-01

    A long-term administration of an azo-dye, trypan blue, induced a reactive proliferation of RES in rats. Reaction of the RES was followed in rats whose optical analyzer was eliminated by enucleation of bulbs after the birth, in rats whose smell analyzer was altered by a repeated long-term exposition to ammoniac and in normal rats. Blind rats showed a striking proliferation of histiocytes of the RES in the liver, while the reaction was weak in the spleen and lymph nodes. In contrary to that, rats with altered smell and also the normal rats exhibited standard reactive changes in the spleen and lymph nodes and distinctly weaker reaction in the liver. The obtained results support an idea that vegetative neural mechanisms play a role in control and coordination of RES reactions.

  16. Design of a Closed-Loop, Bidirectional Brain Machine Interface System With Energy Efficient Neural Feature Extraction and PID Control.

    Science.gov (United States)

    Liu, Xilin; Zhang, Milin; Richardson, Andrew G; Lucas, Timothy H; Van der Spiegel, Jan

    2017-08-01

    This paper presents a bidirectional brain machine interface (BMI) microsystem designed for closed-loop neuroscience research, especially experiments in freely behaving animals. The system-on-chip (SoC) consists of 16-channel neural recording front-ends, neural feature extraction units, 16-channel programmable neural stimulator back-ends, in-channel programmable closed-loop controllers, global analog-digital converters (ADC), and peripheral circuits. The proposed neural feature extraction units includes 1) an ultra low-power neural energy extraction unit enabling a 64-step natural logarithmic domain frequency tuning, and 2) a current-mode action potential (AP) detection unit with time-amplitude window discriminator. A programmable proportional-integral-derivative (PID) controller has been integrated in each channel enabling a various of closed-loop operations. The implemented ADCs include a 10-bit voltage-mode successive approximation register (SAR) ADC for the digitization of the neural feature outputs and/or local field potential (LFP) outputs, and an 8-bit current-mode SAR ADC for the digitization of the action potential outputs. The multi-mode stimulator can be programmed to perform monopolar or bipolar, symmetrical or asymmetrical charge balanced stimulation with a maximum current of 4 mA in an arbitrary channel configuration. The chip has been fabricated in 0.18 μ m CMOS technology, occupying a silicon area of 3.7 mm 2 . The chip dissipates 56 μW/ch on average. General purpose low-power microcontroller with Bluetooth module are integrated in the system to provide wireless link and SoC configuration. Methods, circuit techniques and system topology proposed in this work can be used in a wide range of relevant neurophysiology research, especially closed-loop BMI experiments.

  17. Birth and Death Recording System Reviewed from Aspects of Public Policy (A Study on Implementation of Sector Policy Population Administration in Bandung Regency and Bandung West Java

    Directory of Open Access Journals (Sweden)

    Hasoloan Nadeak

    2014-03-01

    Full Text Available The purpose of this research is to know about factualization and validation on Registration System of Birth and Death in the Region of Regency of Bandung and City of Bandung Province of West Java. Based on the said approach and purpose it is known that Registration System of Birth and Death in the Office of Dukcapil Regency of Bandung and City of Bandung as the implementing agency is not yet conducted in a good and smooth system because the population as part of registration system of birth and death as the beneficiaries of Registration System still have low awareness to report important events they experience such as the events of birth and death. In addition, the form of death report that is currently used, must be reformatted because the report received form districts of study location is not by name, by address and nor by SIAK data but based on the total or the number of died people in the district areas that making it difficult to the Office of Dukcapil Cifty of Bandung and Regency of Bandung to abolish their names from SIAK data, that has the impact on the accuracy of the total population and the existence of died people contained in List of Temporary Voters (DPS.

  18. A simulation study for the application of two different neural network control algorithms on an electrohydraulic system

    Science.gov (United States)

    İstif, İlyas

    2005-11-01

    This paper studies a servo-valve controlled hydraulic cylinder system which is mostly used in industrial applications such as robotics, computer numerical control (CNC) machines and transportations. The system model consists of combination of two models: The first model involves nonlinear flow equations of the servo-valve, which are widely available in the literature. The second model employed in the system is a tailored asymmetric cylinder model. A fourth order nonlinear system model is then obtained by combining these two models. Two different neural network control algorithms are applied to the system. The first algorithm is "Neural Network Predictive Control (NNPC)," which employs identified neural network model to predict the future output of the system. The second algorithm is "Nonlinear Autoregressive Moving Average (NARMA-L2)" control, which transforms nonlinear system dynamics into linear system dynamics by eliminating the nonlinearities. On the simulation, NNPC and NARMA-L2 control are applied to the system model by using Matlab's Simulik simulation package and position control of the system is realized. A discussion regarding the advantages and disadvantages of the two control algorithms are also provided in the paper.

  19. The association of birth weight and infant growth with childhood autonomic nervous system activity and its mediating effects on energy-balance-related behaviours-the ABCD study.

    Science.gov (United States)

    van Deutekom, Arend W; Chinapaw, Mai Jm; Gademan, Maaike Gj; Twisk, Jos Wr; Gemke, Reinoud Jbj; Vrijkotte, Tanja Gm

    2016-08-01

    The purpose of this study was to examine the association of birth weight and infant growth with childhood autonomic nervous system (ANS) activity and to assess whether ANS activity mediates the associations of birth weight and infant growth with energy-balance-related behaviours, including energy intake, satiety response, physical activity and screen time. In 2089 children, we prospectively collected birth weight, infant growth defined as conditional weight and height gain between birth and 12 months and-at 5 years-indices of cardiac ANS activity and parent-reported energy-balance-related behaviours. A mediation analysis was conducted, based on MacKinnon's multivariate extension of the product-of-coefficients strategy. Birth weight and infant height gain were inversely associated with sympathetic, but not parasympathetic, activity at age 5. Infant weight gain was not associated with childhood ANS activity. Infant weight gain was predictive of increased childhood screen time and infant height gain of diminished childhood energy intake, but sympathetic activity did not mediate these associations. Low-birth-weight children have higher sympathetic activity, which is considered a risk factor for cardiovascular disease. Height gain in infancy seems to be beneficial for childhood sympathetic activity. However, sympathetic activity was no mediator of the associations of infant growth with childhood energy-balance-related behaviours. As individual differences in ANS activity predict increased risk of cardiovascular disease, these differences may offer insight into the early-life origins of chronic diseases and provide further basis for public health strategies to optimize birth weight and infant growth. © The Author 2016; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association.

  20. An Intelligent Ensemble Neural Network Model for Wind Speed Prediction in Renewable Energy Systems

    Science.gov (United States)

    Ranganayaki, V.; Deepa, S. N.

    2016-01-01

    Various criteria are proposed to select the number of hidden neurons in artificial neural network (ANN) models and based on the criterion evolved an intelligent ensemble neural network model is proposed to predict wind speed in renewable energy applications. The intelligent ensemble neural model based wind speed forecasting is designed by averaging the forecasted values from multiple neural network models which includes multilayer perceptron (MLP), multilayer adaptive linear neuron (Madaline), back propagation neural network (BPN), and probabilistic neural network (PNN) so as to obtain better accuracy in wind speed prediction with minimum error. The random selection of hidden neurons numbers in artificial neural network results in overfitting or underfitting problem. This paper aims to avoid the occurrence of overfitting and underfitting problems. The selection of number of hidden neurons is done in this paper employing 102 criteria; these evolved criteria are verified by the computed various error values. The proposed criteria for fixing hidden neurons are validated employing the convergence theorem. The proposed intelligent ensemble neural model is applied for wind speed prediction application considering the real time wind data collected from the nearby locations. The obtained simulation results substantiate that the proposed ensemble model reduces the error value to minimum and enhances the accuracy. The computed results prove the effectiveness of the proposed ensemble neural network (ENN) model with respect to the considered error factors in comparison with that of the earlier models available in the literature. PMID:27034973

  1. An Intelligent Ensemble Neural Network Model for Wind Speed Prediction in Renewable Energy Systems.

    Science.gov (United States)

    Ranganayaki, V; Deepa, S N

    2016-01-01

    Various criteria are proposed to select the number of hidden neurons in artificial neural network (ANN) models and based on the criterion evolved an intelligent ensemble neural network model is proposed to predict wind speed in renewable energy applications. The intelligent ensemble neural model based wind speed forecasting is designed by averaging the forecasted values from multiple neural network models which includes multilayer perceptron (MLP), multilayer adaptive linear neuron (Madaline), back propagation neural network (BPN), and probabilistic neural network (PNN) so as to obtain better accuracy in wind speed prediction with minimum error. The random selection of hidden neurons numbers in artificial neural network results in overfitting or underfitting problem. This paper aims to avoid the occurrence of overfitting and underfitting problems. The selection of number of hidden neurons is done in this paper employing 102 criteria; these evolved criteria are verified by the computed various error values. The proposed criteria for fixing hidden neurons are validated employing the convergence theorem. The proposed intelligent ensemble neural model is applied for wind speed prediction application considering the real time wind data collected from the nearby locations. The obtained simulation results substantiate that the proposed ensemble model reduces the error value to minimum and enhances the accuracy. The computed results prove the effectiveness of the proposed ensemble neural network (ENN) model with respect to the considered error factors in comparison with that of the earlier models available in the literature.

  2. AN AUTOMATED IMPLEMENTATION OF INDIAN UNIVERSITY ADMISSION SYSTEM USING ARTIFICIAL NEURAL NETWORKS

    Directory of Open Access Journals (Sweden)

    B. Sankarasubramanian

    2014-04-01

    Full Text Available Indian University Admission is a complex phenomenon encompassing various factors both tangible and intangible. Apart from Merit - Caste, Community and Religion play a crucial role in getting admission to various courses offered by Universities. The Single Window Admission System followed by almost all Government Universities (for example: Anna University, Chennai is, so far, the best practice to offer Admission sought by student community. But, still, in Private Universities, the Admission process is done manually since number of students seeking a course is smaller in size. This manual process is fraught with some drawbacks: such as slower in time, cumbersome and costlier, bias by the Admission Officer, manual errors while processing, due to influence exercised by powerful people qualified students not getting their course of choice and unqualified students getting into their course of willingness and etc. This paper addresses these problems via neural network architecture based Admission system which will eliminate all the pitfalls and drawbacks inherent in the current system and offers a smooth, clearer, easier and cost effective way of student admission system implementation in Indian Universities.

  3. Fuzzy Logic, Neural Networks, Genetic Algorithms: Views of Three Artificial Intelligence Concepts Used in Modeling Scientific Systems

    Science.gov (United States)

    Sunal, Cynthia Szymanski; Karr, Charles L.; Sunal, Dennis W.

    2003-01-01

    Students' conceptions of three major artificial intelligence concepts used in the modeling of systems in science, fuzzy logic, neural networks, and genetic algorithms were investigated before and after a higher education science course. Students initially explored their prior ideas related to the three concepts through active tasks. Then,…

  4. White blood cells identification system based on convolutional deep neural learning networks.

    Science.gov (United States)

    Shahin, A I; Guo, Yanhui; Amin, K M; Sharawi, Amr A

    2017-11-16

    White blood cells (WBCs) differential counting yields valued information about human health and disease. The current developed automated cell morphology equipments perform differential count which is based on blood smear image analysis. Previous identification systems for WBCs consist of successive dependent stages; pre-processing, segmentation, feature extraction, feature selection, and classification. There is a real need to employ deep learning methodologies so that the performance of previous WBCs identification systems can be increased. Classifying small limited datasets through deep learning systems is a major challenge and should be investigated. In this paper, we propose a novel identification system for WBCs based on deep convolutional neural networks. Two methodologies based on transfer learning are followed: transfer learning based on deep activation features and fine-tuning of existed deep networks. Deep acrivation featues are extracted from several pre-trained networks and employed in a traditional identification system. Moreover, a novel end-to-end convolutional deep architecture called "WBCsNet" is proposed and built from scratch. Finally, a limited balanced WBCs dataset classification is performed through the WBCsNet as a pre-trained network. During our experiments, three different public WBCs datasets (2551 images) have been used which contain 5 healthy WBCs types. The overall system accuracy achieved by the proposed WBCsNet is (96.1%) which is more than different transfer learning approaches or even the previous traditional identification system. We also present features visualization for the WBCsNet activation which reflects higher response than the pre-trained activated one. a novel WBCs identification system based on deep learning theory is proposed and a high performance WBCsNet can be employed as a pre-trained network. Copyright © 2017. Published by Elsevier B.V.

  5. Fabrication of micropatterned hydrogels for neural culture systems using dynamic mask projection photolithography.

    Science.gov (United States)

    Curley, J Lowry; Jennings, Scott R; Moore, Michael J

    2011-02-11

    photocrosslinkable or self polymerizing hydrogel, or selectively adhered to the permeable support membrane using cell-restrictive photopatterning. Using the DMD, we created hydrogel constructs up to ~1mm thick, but thin film (3D hydrogel systems for production of microfabricated neural cell and tissue cultures. The dual hydrogel constructs demonstrated herein represent versatile in vitro models that may prove useful for studies in neuroscience involving cell survival, migration, and/or neurite growth and guidance. Moreover, as the protocol can work for many types of hydrogels and cells, the potential applications are both varied and vast.

  6. A Fully Integrated Wireless Compressed Sensing Neural Signal Acquisition System for Chronic Recording and Brain Machine Interface.

    Science.gov (United States)

    Liu, Xilin; Zhang, Milin; Xiong, Tao; Richardson, Andrew G; Lucas, Timothy H; Chin, Peter S; Etienne-Cummings, Ralph; Tran, Trac D; Van der Spiegel, Jan

    2016-07-18

    Reliable, multi-channel neural recording is critical to the neuroscience research and clinical treatment. However, most hardware development of fully integrated, multi-channel wireless neural recorders to-date, is still in the proof-of-concept stage. To be ready for practical use, the trade-offs between performance, power consumption, device size, robustness, and compatibility need to be carefully taken into account. This paper presents an optimized wireless compressed sensing neural signal recording system. The system takes advantages of both custom integrated circuits and universal compatible wireless solutions. The proposed system includes an implantable wireless system-on-chip (SoC) and an external wireless relay. The SoC integrates 16-channel low-noise neural amplifiers, programmable filters and gain stages, a SAR ADC, a real-time compressed sensing module, and a near field wireless power and data transmission link. The external relay integrates a 32 bit low-power microcontroller with Bluetooth 4.0 wireless module, a programming interface, and an inductive charging unit. The SoC achieves high signal recording quality with minimized power consumption, while reducing the risk of infection from through-skin connectors. The external relay maximizes the compatibility and programmability. The proposed compressed sensing module is highly configurable, featuring a SNDR of 9.78 dB with a compression ratio of 8×. The SoC has been fabricated in a 180 nm standard CMOS technology, occupying 2.1 mm × 0.6 mm silicon area. A pre-implantable system has been assembled to demonstrate the proposed paradigm. The developed system has been successfully used for long-term wireless neural recording in freely behaving rhesus monkey.

  7. LOAD FORECASTING FOR POWER SYSTEM PLANNING AND OPERATION USING ARTIFICIAL NEURAL NETWORK AT AL BATINAH REGION OMAN

    Directory of Open Access Journals (Sweden)

    HUSSEIN A. ABDULQADER

    2012-08-01

    Full Text Available Load forecasting is essential part for the power system planning and operation. In this paper the modeling and design of artificial neural network for load forecasting is carried out in a particular region of Oman. Neural network approach helps to reduce the problem associated with conventional method and has the advantage of learning directly from the historical data. The neural network here uses data such as past load; weather information like humidity and temperatures. Once the neural network is trained for the past set of data it can give a prediction of future load. This reduces the capital investment reducing the equipments to be installed. The actual data are taken from the Mazoon Electrical Company, Oman. The data of load for the year 2007, 2008 and 2009 are collected for a particular region called Al Batinah in Oman and trained using neural networks to forecast the future. The main objective is to forecast the amount of electricity needed for better load distribution in the areas of this region in Oman. The load forecasting is done for the year 2010 and is validated for the accuracy.

  8. Clara's birth.

    Science.gov (United States)

    Thorens, S; Richer, D; Bel, A; Bel, B

    1999-01-01

    Advocacy for homebirth is based on the strong assumption that birthing is a physiological process and does not require medical interventions unless things turn "wrong." Let us assume that something might always go wrong, for instance during Clara's birth when the placenta was still retained after three hours. What needs to be done? The moment the midwife entered the house she was endowed with a responsibility for any problem caused by her failure to give proper guidance. With this weight on her shoulder, and according to her training and experience, there was no other way for her than to suggest an intervention regarding the placenta. The two midwives, B, and C., might not agree on risk estimations, the nature of the intervention, whether it should be performed at home or in a hospital. The estimation of abnormalities, evaluation of risks and the procedures with which to handle them are the main practical difference between classic obstetrics and non-interventionist midwifery--by analogy, between allopathy and naturopathy. The rest (positive thinking) is basically literature. A delivery will not remain normal just because we decide it "must" be physiological. Dr. Barua, a professor of obstetrics in Pondicherry, pointed out that normal deliveries are rare--fewer than 10 percent in South India. What we have instead is either pathological or "natural" deliveries in which regenerative processes take care of abnormal situations. Unless she has developed sensitive hands, a birth assistant or midwife must rely on monitoring procedures to evaluate deviations from the normal process. Even with the greatest care, these procedures are intrusive in that they disconnect the parturient from her own sensations. While successful unattended homebirth stories emphasise the extraordinary power and sensitivity of a birthing woman, the whole dream seems to collapse in abnormal or pathological cases. It would have collapsed for Sonia as well, had she not discarded negative suggestions

  9. Fuzzy and neural control

    Science.gov (United States)

    Berenji, Hamid R.

    1992-01-01

    Fuzzy logic and neural networks provide new methods for designing control systems. Fuzzy logic controllers do not require a complete analytical model of a dynamic system and can provide knowledge-based heuristic controllers for ill-defined and complex systems. Neural networks can be used for learning control. In this chapter, we discuss hybrid methods using fuzzy logic and neural networks which can start with an approximate control knowledge base and refine it through reinforcement learning.

  10. Validation of birth outcomes from the Society for Assisted Reproductive Technology Clinic Outcome Reporting System (SART CORS): population-based analysis from the Massachusetts Outcome Study of Assisted Reproductive Technology (MOSART).

    Science.gov (United States)

    Stern, Judy E; Gopal, Daksha; Liberman, Rebecca F; Anderka, Marlene; Kotelchuck, Milton; Luke, Barbara

    2016-09-01

    To assess the validity of outcome data reported to the Society for Assisted Reproductive Technology Clinic Outcome Reporting System (SART CORS) compared with data from vital records and the birth defects registry in Massachusetts. Longitudinal cohort. Not applicable. A total of 342,035 live births and fetal deaths from Massachusetts mothers giving birth in the state from July 1, 2004, to December 31, 2008; 9,092 births and fetal deaths were from mothers who had conceived with the use of assisted reproductive technology (ART) and whose cycle data had been reported to the SART CORS. Not applicable. Percentage agreement between maternal race and ethnicity, delivery outcome (live birth or fetal death), plurality (singleton, twin, or triplet+), delivery date, and singleton birth weight reported in the SART CORS versus vital records; sensitivity and specificity for birth defects among singletons as reported in the SART CORS versus the Massachusetts Birth Defects Monitoring Program (BDMP). There was >95% agreement between the SART CORS and vital records for fields of maternal race/ethnicity, live birth/fetal death, and plurality; birth outcome date was within 1 day with 94.9% agreement and birth weight was within 100 g with 89.6% agreement. In contrast, sensitivity for report of any birth defect was 38.6%, with a range of 18.4%-50.0%, for specific birth defect categories. Although most SART CORS outcome fields are accurately reported, birth defect variables showed poor sensitivity compared with the gold standard data from the BDMP. We suggest that reporting of birth defects be discontinued. Copyright © 2016 American Society for Reproductive Medicine. Published by Elsevier Inc. All rights reserved.

  11. Sensorineural hearing loss amplifies neural coding of envelope information in the central auditory system of chinchillas.

    Science.gov (United States)

    Zhong, Ziwei; Henry, Kenneth S; Heinz, Michael G

    2014-03-01

    People with sensorineural hearing loss often have substantial difficulty understanding speech under challenging listening conditions. Behavioral studies suggest that reduced sensitivity to the temporal structure of sound may be responsible, but underlying neurophysiological pathologies are incompletely understood. Here, we investigate the effects of noise-induced hearing loss on coding of envelope (ENV) structure in the central auditory system of anesthetized chinchillas. ENV coding was evaluated noninvasively using auditory evoked potentials recorded from the scalp surface in response to sinusoidally amplitude modulated tones with carrier frequencies of 1, 2, 4, and 8 kHz and a modulation frequency of 140 Hz. Stimuli were presented in quiet and in three levels of white background noise. The latency of scalp-recorded ENV responses was consistent with generation in the auditory midbrain. Hearing loss amplified neural coding of ENV at carrier frequencies of 2 kHz and above. This result may reflect enhanced ENV coding from the periphery and/or an increase in the gain of central auditory neurons. In contrast to expectations, hearing loss was not associated with a stronger adverse effect of increasing masker intensity on ENV coding. The exaggerated neural representation of ENV information shown here at the level of the auditory midbrain helps to explain previous findings of enhanced sensitivity to amplitude modulation in people with hearing loss under some conditions. Furthermore, amplified ENV coding may potentially contribute to speech perception problems in people with cochlear hearing loss by acting as a distraction from more salient acoustic cues, particularly in fluctuating backgrounds. Copyright © 2013 Elsevier B.V. All rights reserved.

  12. Low-complexity object detection with deep convolutional neural network for embedded systems

    Science.gov (United States)

    Tripathi, Subarna; Kang, Byeongkeun; Dane, Gokce; Nguyen, Truong

    2017-09-01

    We investigate low-complexity convolutional neural networks (CNNs) for object detection for embedded vision applications. It is well-known that consolidation of an embedded system for CNN-based object detection is more challenging due to computation and memory requirement comparing with problems like image classification. To achieve these requirements, we design and develop an end-to-end TensorFlow (TF)-based fully-convolutional deep neural network for generic object detection task inspired by one of the fastest framework, YOLO.1 The proposed network predicts the localization of every object by regressing the coordinates of the corresponding bounding box as in YOLO. Hence, the network is able to detect any objects without any limitations in the size of the objects. However, unlike YOLO, all the layers in the proposed network is fully-convolutional. Thus, it is able to take input images of any size. We pick face detection as an use case. We evaluate the proposed model for face detection on FDDB dataset and Widerface dataset. As another use case of generic object detection, we evaluate its performance on PASCAL VOC dataset. The experimental results demonstrate that the proposed network can predict object instances of different sizes and poses in a single frame. Moreover, the results show that the proposed method achieves comparative accuracy comparing with the state-of-the-art CNN-based object detection methods while reducing the model size by 3× and memory-BW by 3 - 4× comparing with one of the best real-time CNN-based object detectors, YOLO. Our 8-bit fixed-point TF-model provides additional 4× memory reduction while keeping the accuracy nearly as good as the floating-point model. Moreover, the fixed- point model is capable of achieving 20× faster inference speed comparing with the floating-point model. Thus, the proposed method is promising for embedded implementations.

  13. Optimal Sizing of Energy Storage System in Solar Energy Electric Vehicle Using Genetic Algorithm and Neural Network

    Science.gov (United States)

    Zhou, Shiqiong; Kang, Longyun; Cheng, Miaomiao; Cao, Binggang

    Owing to sun's rays distributing randomly and discontinuously and load fluctuation, energy storage system is very important in Solar Energy Electric Vehicle (SEEV). The combinatorial optimization by genetic algorithm and neural network was used to optimize the energy storage system (including storage batteries and flywheel).In the optimization design, the operation strategy of the system was fixed and used to instruct the simulation about the system's operation. And the optimal objective was selected as minimizing the total capital cost of the energy storage system, subject to the main constraint of the Loss of Power Supply Probability (LPSP). Studies have proved that the combinatorial optimization by genetic algorithm and neural network converges well, lessen calculation time and it is feasible.

  14. Learning from adaptive neural dynamic surface control of strict-feedback systems.

    Science.gov (United States)

    Wang, Min; Wang, Cong

    2015-06-01

    Learning plays an essential role in autonomous control systems. However, how to achieve learning in the nonstationary environment for nonlinear systems is a challenging problem. In this paper, we present learning method for a class of n th-order strict-feedback systems by adaptive dynamic surface control (DSC) technology, which achieves the human-like ability of learning by doing and doing with learned knowledge. To achieve the learning, this paper first proposes stable adaptive DSC with auxiliary first-order filters, which ensures the boundedness of all the signals in the closed-loop system and the convergence of tracking errors in a finite time. With the help of DSC, the derivative of the filter output variable is used as the neural network (NN) input instead of traditional intermediate variables. As a result, the proposed adaptive DSC method reduces greatly the dimension of NN inputs, especially for high-order systems. After the stable DSC design, we decompose the stable closed-loop system into a series of linear time-varying perturbed subsystems. Using a recursive design, the recurrent property of NN input variables is easily verified since the complexity is overcome using DSC. Subsequently, the partial persistent excitation condition of the radial basis function NN is satisfied. By combining a state transformation, accurate approximations of the closed-loop system dynamics are recursively achieved in a local region along recurrent orbits. Then, the learning control method using the learned knowledge is proposed to achieve the closed-loop stability and the improved control performance. Simulation studies are performed to demonstrate the proposed scheme can not only reuse the learned knowledge to achieve the better control performance with the faster tracking convergence rate and the smaller tracking error but also greatly alleviate the computational burden because of reducing the number and complexity of NN input variables.

  15. Prediction of preterm birth.

    Science.gov (United States)

    Borg, F; Gravino, G; Schembri-Wismayer, P; Calleja-Agius, J

    2013-06-01

    Preterm delivery is birth occurring before 37 completed weeks of gestation. Preterm birth is the primary cause of morbidity and mortality in children especially if this occurs before 34 weeks of gestation. If preterm birth could be predicted and treated accordingly, this would greatly reduce mortality, morbidity and associated costs. There have been many attempts to develop an accurate and efficient method to predict preterm premature rupture of membranes (PPROM) and preterm labor that leads to spontaneous preterm birth (SPB). However, the initial signs and symptoms are most often mild and may even occur in normal pregnancies, making early detection rather difficult. The aim of this paper is to provide an overview of the current methods employed in predicting preterm birth occurring due to preterm labor and PPROM. Among these methods are risk scoring systems, cervical/vaginal screening for fetal fibronectin, cervical assessment by ultrasonography, uterine activity monitoring, biomarkers such as endocrine factors, cytokines and enzymes, fetal DNA and genetic polymorphism. SPB is multifactorial, and so it is highly unlikely that a single test can accurately predict SPB. A combination of biological markers is also reviewed in the estimation of the risk of preterm delivery.

  16. Combined expert system/neural networks method for process fault diagnosis

    Science.gov (United States)

    Reifman, Jaques; Wei, Thomas Y. C.

    1995-01-01

    A two-level hierarchical approach for process fault diagnosis is an operating system employs a function-oriented approach at a first level and a component characteristic-oriented approach at a second level, where the decision-making procedure is structured in order of decreasing intelligence with increasing precision. At the first level, the diagnostic method is general and has knowledge of the overall process including a wide variety of plant transients and the functional behavior of the process components. An expert system classifies malfunctions by function to narrow the diagnostic focus to a particular set of possible faulty components that could be responsible for the detected functional misbehavior of the operating system. At the second level, the diagnostic method limits its scope to component malfunctions, using more detailed knowledge of component characteristics. Trained artificial neural networks are used to further narrow the diagnosis and to uniquely identify the faulty component by classifying the abnormal condition data as a failure of one of the hypothesized components through component characteristics. Once an anomaly is detected, the hierarchical structure is used to successively narrow the diagnostic focus from a function misbehavior, i.e., a function oriented approach, until the fault can be determined, i.e., a component characteristic-oriented approach.

  17. Fuzzy Counter Propagation Neural Network Control for a Class of Nonlinear Dynamical Systems

    Directory of Open Access Journals (Sweden)

    Vandana Sakhre

    2015-01-01

    Full Text Available Fuzzy Counter Propagation Neural Network (FCPN controller design is developed, for a class of nonlinear dynamical systems. In this process, the weight connecting between the instar and outstar, that is, input-hidden and hidden-output layer, respectively, is adjusted by using Fuzzy Competitive Learning (FCL. FCL paradigm adopts the principle of learning, which is used to calculate Best Matched Node (BMN which is proposed. This strategy offers a robust control of nonlinear dynamical systems. FCPN is compared with the existing network like Dynamic Network (DN and Back Propagation Network (BPN on the basis of Mean Absolute Error (MAE, Mean Square Error (MSE, Best Fit Rate (BFR, and so forth. It envisages that the proposed FCPN gives better results than DN and BPN. The effectiveness of the proposed FCPN algorithms is demonstrated through simulations of four nonlinear dynamical systems and multiple input and single output (MISO and a single input and single output (SISO gas furnace Box-Jenkins time series data.

  18. An Improved Method for Sizing Standalone Photovoltaic Systems Using Generalized Regression Neural Network

    Directory of Open Access Journals (Sweden)

    Tamer Khatib

    2014-01-01

    Full Text Available In this research an improved approach for sizing standalone PV system (SAPV is presented. This work is an improved work developed previously by the authors. The previous work is based on the analytical method which faced some concerns regarding the difficulty of finding the model’s coefficients. Therefore, the proposed approach in this research is based on a combination of an analytical method and a machine learning approach for a generalized artificial neural network (GRNN. The GRNN assists to predict the optimal size of a PV system using the geographical coordinates of the targeted site instead of using mathematical formulas. Employing the GRNN facilitates the use of a previously developed method by the authors and avoids some of its drawbacks. The approach has been tested using data from five Malaysian sites. According to the results, the proposed method can be efficiently used for SAPV sizing whereas the proposed GRNN based model predicts the sizing curves of the PV system accurately with a prediction error of 0.6%. Moreover, hourly meteorological and load demand data are used in this research in order to consider the uncertainty of the solar energy and the load demand.

  19. Dispersion compensation of fiber optic communication system with direct detection using artificial neural networks (ANNs)

    Science.gov (United States)

    Maghrabi, Mahmoud M. T.; Kumar, Shiva; Bakr, Mohamed H.

    2018-02-01

    This work introduces a powerful digital nonlinear feed-forward equalizer (NFFE), exploiting multilayer artificial neural network (ANN). It mitigates impairments of optical communication systems arising due to the nonlinearity introduced by direct photo-detection. In a direct detection system, the detection process is nonlinear due to the fact that the photo-current is proportional to the absolute square of the electric field intensity. The proposed equalizer provides the most efficient computational cost with high equalization performance. Its performance is comparable to the benchmark compensation performance achieved by maximum-likelihood sequence estimator. The equalizer trains an ANN to act as a nonlinear filter whose impulse response removes the intersymbol interference (ISI) distortions of the optical channel. Owing to the proposed extensive training of the equalizer, it achieves the ultimate performance limit of any feed-forward equalizer (FFE). The performance and efficiency of the equalizer is investigated by applying it to various practical short-reach fiber optic communication system scenarios. These scenarios are extracted from practical metro/media access networks and data center applications. The obtained results show that the ANN-NFFE compensates for the received BER degradation and significantly increases the tolerance to the chromatic dispersion distortion.

  20. Combined expert system/neural networks method for process fault diagnosis

    Science.gov (United States)

    Reifman, J.; Wei, T.Y.C.

    1995-08-15

    A two-level hierarchical approach for process fault diagnosis of an operating system employs a function-oriented approach at a first level and a component characteristic-oriented approach at a second level, where the decision-making procedure is structured in order of decreasing intelligence with increasing precision. At the first level, the diagnostic method is general and has knowledge of the overall process including a wide variety of plant transients and the functional behavior of the process components. An expert system classifies malfunctions by function to narrow the diagnostic focus to a particular set of possible faulty components that could be responsible for the detected functional misbehavior of the operating system. At the second level, the diagnostic method limits its scope to component malfunctions, using more detailed knowledge of component characteristics. Trained artificial neural networks are used to further narrow the diagnosis and to uniquely identify the faulty component by classifying the abnormal condition data as a failure of one of the hypothesized components through component characteristics. Once an anomaly is detected, the hierarchical structure is used to successively narrow the diagnostic focus from a function misbehavior, i.e., a function oriented approach, until the fault can be determined, i.e., a component characteristic-oriented approach. 9 figs.