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Sample records for artificial canal network

  1. Development of an artificial neural network-based software for prediction of power plant canal water discharge temperature

    Energy Technology Data Exchange (ETDEWEB)

    Romero, C.E.; Shan, J.F. [Lehigh University, Bethlehem, PA (United States). Energy Research Center

    2005-11-01

    Power plant cooling water systems that interact with nearby effluents are complex non-linear, large-time-delay systems. A neural network-based software tool was developed for prediction of the canal water discharge temperature at a coal-fired power plant as a function of plant operating parameters and local weather conditions, including tide information. The plant has four units totaling an installed capacity of 1550 MW and its water thermal discharge is environmentally regulated. In the summer months, when the price of electricity is very profitable and the risk of exceeding the canal temperature limit is greater, the tradeoff between maximum generation and environmental compliance violations is financially significant. The software is a predictive tool to assist in scheduling load generation among the plant's four units without exceeding a thermal discharge limit of 95{sup o}F. Back propagation neural network architectures were trained using plant operating data with an 'off-set' component. The artificial intelligence models produced reasonable trends for year-round prediction and different operational scenarios. Comparison of measured and predicted canal temperatures indicated an accuracy of less than 0.3{sup o}F over the range between 90 and 95{sup o}F. The software tool was developed as an Object Linking and Embedding (OLE) for Process Control (OPC) client, with real-time communication and interface with the plant Distributed Control System (DCS).

  2. Nested Canalizing Functions and Their Networks

    CERN Document Server

    Kadelka, Claus; Adeyeye, John O; Laubenbacher, Reinhard

    2014-01-01

    The concept of a nested canalizing Boolean function has been studied over the last decade in the context of understanding the regulatory logic of molecular interaction networks, such as gene regulatory networks. Such networks are predominantly governed by nested canalizing functions. Derrida values are frequently used to analyze the robustness of a Boolean network to perturbations. This paper introduces closed formulas for the calculation of Derrida values of networks governed by Boolean nested canalizing functions, which previously required extensive simulations. Recently, the concept of nested canalizing functions has been generalized to include multistate functions, and a recursive formula has been derived for their number, as a function of the number of variables. This paper contains a detailed analysis of the class of nested canalizing functions over an arbitrary finite field. In addition, the concept of nested canalization is further generalized and closed formulas for the number of such generalized fun...

  3. Artificial neural network modelling

    CERN Document Server

    Samarasinghe, Sandhya

    2016-01-01

    This book covers theoretical aspects as well as recent innovative applications of Artificial Neural networks (ANNs) in natural, environmental, biological, social, industrial and automated systems. It presents recent results of ANNs in modelling small, large and complex systems under three categories, namely, 1) Networks, Structure Optimisation, Robustness and Stochasticity 2) Advances in Modelling Biological and Environmental Systems and 3) Advances in Modelling Social and Economic Systems. The book aims at serving undergraduates, postgraduates and researchers in ANN computational modelling. .

  4. Introduction to Artificial Neural Networks

    DEFF Research Database (Denmark)

    Larsen, Jan

    1999-01-01

    The note addresses introduction to signal analysis and classification based on artificial feed-forward neural networks.......The note addresses introduction to signal analysis and classification based on artificial feed-forward neural networks....

  5. Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Kapil Nahar

    2012-12-01

    Full Text Available An artificial neural network is an information-processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information. The key element of this paradigm is the novel structure of the information processing system. It is composed of a large number of highly interconnected processing elements (neurons working in unison to solve specific problems. Ann’s, like people, learn by example.

  6. Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Kapil Nahar

    2012-12-01

    Full Text Available An artificial neural network is an information-processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information.The key element of this paradigm is the novel structure of the information processing system. It is composed of a large number of highly interconnected processing elements (neurons working in unison to solve specific problems.Ann’s, like people, learn by example.

  7. The Influence of Canalization on the Robustness of Boolean Networks

    CERN Document Server

    Kadelka, Claus; Laubenbacher, Reinhard

    2016-01-01

    Time- and state-discrete dynamical systems are frequently used to model molecular networks. This paper provides a collection of mathematical and computational tools for the study of robustness in Boolean network models. The focus is on networks governed by $k$-canalizing functions, a recently introduced class of Boolean functions that contains the well-studied class of nested canalizing functions. The activities and sensitivity of a function quantify the impact of input changes on the function output. This paper generalizes the latter concept to $c$-sensitivity and provides formulas for the activities and $c$-sensitivity of general $k$-canalizing functions as well as canalizing functions with more precisely defined structure. A popular measure for the robustness of a network, the Derrida value, can be expressed as a weighted sum of the $c$-sensitivities of the governing canalizing functions, and can also be calculated for a stochastic extension of Boolean networks. These findings provide a computationally eff...

  8. What are artificial neural networks?

    DEFF Research Database (Denmark)

    Krogh, Anders

    2008-01-01

    Artificial neural networks have been applied to problems ranging from speech recognition to prediction of protein secondary structure, classification of cancers and gene prediction. How do they work and what might they be good for? Udgivelsesdato: 2008-Feb......Artificial neural networks have been applied to problems ranging from speech recognition to prediction of protein secondary structure, classification of cancers and gene prediction. How do they work and what might they be good for? Udgivelsesdato: 2008-Feb...

  9. Artificial neural networks in medicine

    Energy Technology Data Exchange (ETDEWEB)

    Keller, P.E.

    1994-07-01

    This Technology Brief provides an overview of artificial neural networks (ANN). A definition and explanation of an ANN is given and situations in which an ANN is used are described. ANN applications to medicine specifically are then explored and the areas in which it is currently being used are discussed. Included are medical diagnostic aides, biochemical analysis, medical image analysis and drug development.

  10. Artificial organic networks artificial intelligence based on carbon networks

    CERN Document Server

    Ponce-Espinosa, Hiram; Molina, Arturo

    2014-01-01

    This monograph describes the synthesis and use of biologically-inspired artificial hydrocarbon networks (AHNs) for approximation models associated with machine learning and a novel computational algorithm with which to exploit them. The reader is first introduced to various kinds of algorithms designed to deal with approximation problems and then, via some conventional ideas of organic chemistry, to the creation and characterization of artificial organic networks and AHNs in particular. The advantages of using organic networks are discussed with the rules to be followed to adapt the network to its objectives. Graph theory is used as the basis of the necessary formalism. Simulated and experimental examples of the use of fuzzy logic and genetic algorithms with organic neural networks are presented and a number of modeling problems suitable for treatment by AHNs are described: ·        approximation; ·        inference; ·        clustering; ·        control; ·        class...

  11. Artificial neural networks in NDT

    International Nuclear Information System (INIS)

    Artificial neural networks, simply known as neural networks, have attracted considerable interest in recent years largely because of a growing recognition of the potential of these computational paradigms as powerful alternative models to conventional pattern recognition or function approximation techniques. The neural networks approach is having a profound effect on almost all fields, and has been utilised in fields Where experimental inter-disciplinary work is being carried out. Being a multidisciplinary subject with a broad knowledge base, Nondestructive Testing (NDT) or Nondestructive Evaluation (NDE) is no exception. This paper explains typical applications of neural networks in NDT/NDE. Three promising types of neural networks are highlighted, namely, back-propagation, binary Hopfield and Kohonen's self-organising maps. (Author)

  12. Introduction to Concepts in Artificial Neural Networks

    Science.gov (United States)

    Niebur, Dagmar

    1995-01-01

    This introduction to artificial neural networks summarizes some basic concepts of computational neuroscience and the resulting models of artificial neurons. The terminology of biological and artificial neurons, biological and machine learning and neural processing is introduced. The concepts of supervised and unsupervised learning are explained with examples from the power system area. Finally, a taxonomy of different types of neurons and different classes of artificial neural networks is presented.

  13. Artificial neural networks in neurosurgery.

    Science.gov (United States)

    Azimi, Parisa; Mohammadi, Hasan Reza; Benzel, Edward C; Shahzadi, Sohrab; Azhari, Shirzad; Montazeri, Ali

    2015-03-01

    Artificial neural networks (ANNs) effectively analyze non-linear data sets. The aimed was A review of the relevant published articles that focused on the application of ANNs as a tool for assisting clinical decision-making in neurosurgery. A literature review of all full publications in English biomedical journals (1993-2013) was undertaken. The strategy included a combination of key words 'artificial neural networks', 'prognostic', 'brain', 'tumor tracking', 'head', 'tumor', 'spine', 'classification' and 'back pain' in the title and abstract of the manuscripts using the PubMed search engine. The major findings are summarized, with a focus on the application of ANNs for diagnostic and prognostic purposes. Finally, the future of ANNs in neurosurgery is explored. A total of 1093 citations were identified and screened. In all, 57 citations were found to be relevant. Of these, 50 articles were eligible for inclusion in this review. The synthesis of the data showed several applications of ANN in neurosurgery, including: (1) diagnosis and assessment of disease progression in low back pain, brain tumours and primary epilepsy; (2) enhancing clinically relevant information extraction from radiographic images, intracranial pressure processing, low back pain and real-time tumour tracking; (3) outcome prediction in epilepsy, brain metastases, lumbar spinal stenosis, lumbar disc herniation, childhood hydrocephalus, trauma mortality, and the occurrence of symptomatic cerebral vasospasm in patients with aneurysmal subarachnoid haemorrhage; (4) the use in the biomechanical assessments of spinal disease. ANNs can be effectively employed for diagnosis, prognosis and outcome prediction in neurosurgery.

  14. Artificial astrocytes improve neural network performance.

    Directory of Open Access Journals (Sweden)

    Ana B Porto-Pazos

    Full Text Available Compelling evidence indicates the existence of bidirectional communication between astrocytes and neurons. Astrocytes, a type of glial cells classically considered to be passive supportive cells, have been recently demonstrated to be actively involved in the processing and regulation of synaptic information, suggesting that brain function arises from the activity of neuron-glia networks. However, the actual impact of astrocytes in neural network function is largely unknown and its application in artificial intelligence remains untested. We have investigated the consequences of including artificial astrocytes, which present the biologically defined properties involved in astrocyte-neuron communication, on artificial neural network performance. Using connectionist systems and evolutionary algorithms, we have compared the performance of artificial neural networks (NN and artificial neuron-glia networks (NGN to solve classification problems. We show that the degree of success of NGN is superior to NN. Analysis of performances of NN with different number of neurons or different architectures indicate that the effects of NGN cannot be accounted for an increased number of network elements, but rather they are specifically due to astrocytes. Furthermore, the relative efficacy of NGN vs. NN increases as the complexity of the network increases. These results indicate that artificial astrocytes improve neural network performance, and established the concept of Artificial Neuron-Glia Networks, which represents a novel concept in Artificial Intelligence with implications in computational science as well as in the understanding of brain function.

  15. Introduction to artificial neural networks.

    Science.gov (United States)

    Grossi, Enzo; Buscema, Massimo

    2007-12-01

    The coupling of computer science and theoretical bases such as nonlinear dynamics and chaos theory allows the creation of 'intelligent' agents, such as artificial neural networks (ANNs), able to adapt themselves dynamically to problems of high complexity. ANNs are able to reproduce the dynamic interaction of multiple factors simultaneously, allowing the study of complexity; they can also draw conclusions on individual basis and not as average trends. These tools can offer specific advantages with respect to classical statistical techniques. This article is designed to acquaint gastroenterologists with concepts and paradigms related to ANNs. The family of ANNs, when appropriately selected and used, permits the maximization of what can be derived from available data and from complex, dynamic, and multidimensional phenomena, which are often poorly predictable in the traditional 'cause and effect' philosophy. PMID:17998827

  16. Artificial neural networks in nuclear medicine

    International Nuclear Information System (INIS)

    An analysis of the accessible literature on the diagnostic applicability of artificial neural networks in coronary artery disease and pulmonary embolism appears to be comparative to the diagnosis of experienced doctors dealing with nuclear medicine. Differences in the employed models of artificial neural networks indicate a constant search for the most optimal parameters, which could guarantee the ultimate accuracy in neural network activity. The diagnostic potential within systems containing artificial neural networks proves this calculation tool to be an independent or/and an additional device for supporting a doctor's diagnosis of artery disease and pulmonary embolism. (author)

  17. International Conference on Artificial Neural Networks (ICANN)

    CERN Document Server

    Mladenov, Valeri; Kasabov, Nikola; Artificial Neural Networks : Methods and Applications in Bio-/Neuroinformatics

    2015-01-01

    The book reports on the latest theories on artificial neural networks, with a special emphasis on bio-neuroinformatics methods. It includes twenty-three papers selected from among the best contributions on bio-neuroinformatics-related issues, which were presented at the International Conference on Artificial Neural Networks, held in Sofia, Bulgaria, on September 10-13, 2013 (ICANN 2013). The book covers a broad range of topics concerning the theory and applications of artificial neural networks, including recurrent neural networks, super-Turing computation and reservoir computing, double-layer vector perceptrons, nonnegative matrix factorization, bio-inspired models of cell communities, Gestalt laws, embodied theory of language understanding, saccadic gaze shifts and memory formation, and new training algorithms for Deep Boltzmann Machines, as well as dynamic neural networks and kernel machines. It also reports on new approaches to reinforcement learning, optimal control of discrete time-delay systems, new al...

  18. Rule Extraction using Artificial Neural Networks

    CERN Document Server

    Kamruzzaman, S M

    2010-01-01

    Artificial neural networks have been successfully applied to a variety of business application problems involving classification and regression. Although backpropagation neural networks generally predict better than decision trees do for pattern classification problems, they are often regarded as black boxes, i.e., their predictions are not as interpretable as those of decision trees. In many applications, it is desirable to extract knowledge from trained neural networks so that the users can gain a better understanding of the solution. This paper presents an efficient algorithm to extract rules from artificial neural networks. We use two-phase training algorithm for backpropagation learning. In the first phase, the number of hidden nodes of the network is determined automatically in a constructive fashion by adding nodes one after another based on the performance of the network on training data. In the second phase, the number of relevant input units of the network is determined using pruning algorithm. The ...

  19. Anomaly Detection with Artificial Immune Network

    Institute of Scientific and Technical Information of China (English)

    PENG Lingxi; LI Tao; LIU Xiaojie; CHEN Yuefeng; LIU Caiming; LIU Sunjun

    2007-01-01

    Inspired by the immune network theory, an adaptive anomaly detection paradigm based on artificial immune network,referred as APAI, is proposed. The implementation of the paradigm includes: initially, the first is to create the initial antibody network; then, through the learning of each training antigen, the antibody network is evolved and updated by the optimal antibodies. Finally, anomaly detection process is accomplished by majority vote of the k nearest neighbor antibodies in the network. The experiments used the famous Sonar Benchmark dataset in our study, which is taken from the UCI machine learning database.The obtained detection accuracy of APAI was 97.7%, which was very promising with regard to the other classification applications in the literature for this problem. In addition to its nonlinear classification properties, APAI possesses biological immune networkproperties such as clonal selection, immune network, and immune memory, which can be applied to pattern recognition, classification, and etc.

  20. Assessing Landslide Hazard Using Artificial Neural Network

    DEFF Research Database (Denmark)

    Farrokhzad, Farzad; Choobbasti, Asskar Janalizadeh; Barari, Amin;

    2011-01-01

    neural network has been developed for use in the stability evaluation of slopes under various geological conditions and engineering requirements. The Artificial neural network model of this research uses slope characteristics as input and leads to the output in form of the probability of failure...... and factor of safety. It can be stated that the trained neural networks are capable of predicting the stability of slopes and safety factor of landslide hazard in study area with an acceptable level of confidence. Landslide hazard analysis and mapping can provide useful information for catastrophic loss...... failure" which is main concentration of the current research and "liquefaction failure". Shear failures along shear planes occur when the shear stress along the sliding surfaces exceed the effective shear strength. These slides have been referred to as landslide. An expert system based on artificial...

  1. The Stellar parametrization using Artificial Neural Network

    CERN Document Server

    Giridhar, Sunetra; Kunder, Andrea; Muneer, S; Kumar, G Selva

    2012-01-01

    An update on recent methods for automated stellar parametrization is given. We present preliminary results of the ongoing program for rapid parametrization of field stars using medium resolution spectra obtained using Vainu Bappu Telescope at VBO, Kavalur, India. We have used Artificial Neural Network for estimating temperature, gravity, metallicity and absolute magnitude of the field stars. The network for each parameter is trained independently using a large number of calibrating stars. The trained network is used for estimating atmospheric parameters of unexplored field stars.

  2. Artificial neural network applications in ionospheric studies

    Directory of Open Access Journals (Sweden)

    L. R. Cander

    1998-06-01

    Full Text Available The ionosphere of Earth exhibits considerable spatial changes and has large temporal variability of various timescales related to the mechanisms of creation, decay and transport of space ionospheric plasma. Many techniques for modelling electron density profiles through entire ionosphere have been developed in order to solve the "age-old problem" of ionospheric physics which has not yet been fully solved. A new way to address this problem is by applying artificial intelligence methodologies to current large amounts of solar-terrestrial and ionospheric data. It is the aim of this paper to show by the most recent examples that modern development of numerical models for ionospheric monthly median long-term prediction and daily hourly short-term forecasting may proceed successfully applying the artificial neural networks. The performance of these techniques is illustrated with different artificial neural networks developed to model and predict the temporal and spatial variations of ionospheric critical frequency, f0F2 and Total Electron Content (TEC. Comparisons between results obtained by the proposed approaches and measured f0F2 and TEC data provide prospects for future applications of the artificial neural networks in ionospheric studies.

  3. Neutron spectrometry using artificial neural networks

    International Nuclear Information System (INIS)

    An artificial neural network has been designed to obtain neutron spectra from Bonner spheres spectrometer count rates. The neural network was trained using 129 neutron spectra. These include spectra from isotopic neutron sources; reference and operational spectra from accelerators and nuclear reactors, spectra based on mathematical functions as well as few energy groups and monoenergetic spectra. The spectra were transformed from lethargy to energy distribution and were re-binned to 31 energy groups using the MCNP 4C code. The re-binned spectra and the UTA4 response matrix were used to calculate the expected count rates in Bonner spheres spectrometer. These count rates were used as input and their respective spectra were used as output during the neural network training. After training, the network was tested with the Bonner spheres count rates produced by folding a set of neutron spectra with the response matrix. This set contains data used during network training as well as data not used. Training and testing was carried out using the Matlab(R) program. To verify the network unfolding performance, the original and unfolded spectra were compared using the root mean square error. The use of artificial neural networks to unfold neutron spectra in neutron spectrometry is an alternative procedure that overcomes the drawbacks associated with this ill-conditioned problem

  4. Neutron spectrometry with artificial neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Vega C, H.R.; Hernandez D, V.M.; Manzanares A, E.; Rodriguez, J.M.; Mercado S, G.A. [Universidad Autonoma de Zacatecas, A.P. 336, 98000 Zacatecas (Mexico); Iniguez de la Torre Bayo, M.P. [Universidad de Valladolid, Valladolid (Spain); Barquero, R. [Hospital Universitario Rio Hortega, Valladolid (Spain); Arteaga A, T. [Envases de Zacatecas, S.A. de C.V., Zacatecas (Mexico)]. e-mail: rvega@cantera.reduaz.mx

    2005-07-01

    An artificial neural network has been designed to obtain the neutron spectra from the Bonner spheres spectrometer's count rates. The neural network was trained using 129 neutron spectra. These include isotopic neutron sources; reference and operational spectra from accelerators and nuclear reactors, spectra from mathematical functions as well as few energy groups and monoenergetic spectra. The spectra were transformed from lethargy to energy distribution and were re-bin ned to 31 energy groups using the MCNP 4C code. Re-binned spectra and UTA4 response matrix were used to calculate the expected count rates in Bonner spheres spectrometer. These count rates were used as input and the respective spectrum was used as output during neural network training. After training the network was tested with the Bonner spheres count rates produced by a set of neutron spectra. This set contains data used during network training as well as data not used. Training and testing was carried out in the Mat lab program. To verify the network unfolding performance the original and unfolded spectra were compared using the {chi}{sup 2}-test and the total fluence ratios. The use of Artificial Neural Networks to unfold neutron spectra in neutron spectrometry is an alternative procedure that overcomes the drawbacks associated in this ill-conditioned problem. (Author)

  5. Artificial neural networks in neutron dosimetry

    Energy Technology Data Exchange (ETDEWEB)

    Vega C, H.R.; Hernandez D, V.M.; Manzanares A, E.; Mercado, G.A.; Perales M, W.A.; Robles R, J.A. [Unidades Academicas de Estudios Nucleares, UAZ, A.P. 336, 98000 Zacatecas (Mexico); Gallego, E.; Lorente, A. [Depto. de Ingenieria Nuclear, Universidad Politecnica de Madrid, (Spain)

    2005-07-01

    An artificial neural network has been designed to obtain the neutron doses using only the Bonner spheres spectrometer's count rates. Ambient, personal and effective neutron doses were included. 187 neutron spectra were utilized to calculate the Bonner count rates and the neutron doses. The spectra were transformed from lethargy to energy distribution and were re-binned to 31 energy groups using the MCNP 4C code. Re-binned spectra, UTA4 response matrix and fluence-to-dose coefficients were used to calculate the count rates in Bonner spheres spectrometer and the doses. Count rates were used as input and the respective doses were used as output during neural network training. Training and testing was carried out in Mat lab environment. The artificial neural network performance was evaluated using the {chi}{sup 2}- test, where the original and calculated doses were compared. The use of Artificial Neural Networks in neutron dosimetry is an alternative procedure that overcomes the drawbacks associated in this ill-conditioned problem. (Author)

  6. Artificial Neural Network for Displacement Vectors Determination

    Directory of Open Access Journals (Sweden)

    P. Bohmann

    1997-09-01

    Full Text Available An artificial neural network (NN for displacement vectors (DV determination is presented in this paper. DV are computed in areas which are essential for image analysis and computer vision, in areas where are edges, lines, corners etc. These special features are found by edges operators with the following filtration. The filtration is performed by a threshold function. The next step is DV computation by 2D Hamming artificial neural network. A method of DV computation is based on the full search block matching algorithms. The pre-processing (edges finding is the reason why the correlation function is very simple, the process of DV determination needs less computation and the structure of the NN is simpler.

  7. Analysis of SSR Using Artificial Neural Networks

    OpenAIRE

    Nagabhushana, BS; Chandrasekharaiah, HS

    1996-01-01

    Artificial neural networks (ANNs) are being advantageously applied to power system analysis problems. They possess the ability to establish complicated input-output mappings through a learning process, without any explicit programming. In this paper, an ANN based method for subsynchronous resonance (SSR) analysis is presented. The designed ANN outputs a measure of the possibility of the occurrence of SSR and is fully trained to accommodate the variations of power system parameters over the en...

  8. Practical introduction to artificial neural networks

    OpenAIRE

    Bougrain, Laurent

    2004-01-01

    What are they ? What for are they ? How to use them ? This article wants to answer these three fundamental questions about artificial neural networks that every engineer interested by this machine learning technique asks to oneself. We present the most useful architectures. We explain how to train them using a supervised or an unsupervised learning depending on the task we want to do : regression, discrimination or clustering. What kind of data can one use and how to prepare them ? Finally, w...

  9. Turing Computation with Recurrent Artificial Neural Networks

    OpenAIRE

    Carmantini, Giovanni S; Graben, Peter beim; Desroches, Mathieu; Rodrigues, Serafim

    2015-01-01

    We improve the results by Siegelmann & Sontag (1995) by providing a novel and parsimonious constructive mapping between Turing Machines and Recurrent Artificial Neural Networks, based on recent developments of Nonlinear Dynamical Automata. The architecture of the resulting R-ANNs is simple and elegant, stemming from its transparent relation with the underlying NDAs. These characteristics yield promise for developments in machine learning methods and symbolic computation with continuous time d...

  10. Web Page Categorization Using Artificial Neural Networks

    OpenAIRE

    S. M. Kamruzzaman

    2010-01-01

    Web page categorization is one of the challenging tasks in the world of ever increasing web technologies. There are many ways of categorization of web pages based on different approach and features. This paper proposes a new dimension in the way of categorization of web pages using artificial neural network (ANN) through extracting the features automatically. Here eight major categories of web pages have been selected for categorization; these are business & economy, education, government, en...

  11. Artificial Neural Networks in Stellar Astronomy

    Directory of Open Access Journals (Sweden)

    R. K. Gulati

    2001-01-01

    Full Text Available Next generation of optical spectroscopic surveys, such as the Sloan Digital Sky Survey and the 2 degree field survey, will provide large stellar databases. New tools will be required to extract useful information from these. We show the applications of artificial neural networks to stellar databases. In another application of this method, we predict spectral and luminosity classes from the catalog of spectral indices. We assess the importance of such methods for stellar populations studies.

  12. POWER SCALABLE IMPLEMENTATION OF ARTIFICIAL NEURAL NETWORKS

    OpenAIRE

    Modi, Sankalp; Wilson, Peter; Brown, Andrew

    2005-01-01

    As the use of Artificial Neural Network(ANN) in mobile embedded devices gets more pervasive, power consumption of ANN hardware is becoming a major limiting factor. Although considerable research efforts are now directed towards low-power implementations of ANN, the issue of dynamic power scalability of the implemented design has been largely overlooked. In this paper, we discuss the motivation and basic principles for implementing power scaling in ANN Hardware. With the help of a simple examp...

  13. Livermore Big Artificial Neural Network Toolkit

    Energy Technology Data Exchange (ETDEWEB)

    2016-07-01

    LBANN is a toolkit that is designed to train artificial neural networks efficiently on high performance computing architectures. It is optimized to take advantages of key High Performance Computing features to accelerate neural network training. Specifically it is optimized for low-latency, high bandwidth interconnects, node-local NVRAM, node-local GPU accelerators, and high bandwidth parallel file systems. It is built on top of the open source Elemental distributed-memory dense and spars-direct linear algebra and optimization library that is released under the BSD license. The algorithms contained within LBANN are drawn from the academic literature and implemented to work within a distributed-memory framework.

  14. A new approach to artificial neural networks.

    Science.gov (United States)

    Baptista Filho, B D; Cabral, E L; Soares, A J

    1998-01-01

    A novel approach to artificial neural networks is presented. The philosophy of this approach is based on two aspects: the design of task-specific networks, and a new neuron model with multiple synapses. The synapses' connective strengths are modified through selective and cumulative processes conducted by axo-axonic connections from a feedforward circuit. This new concept was applied to the position control of a planar two-link manipulator exhibiting excellent results on learning capability and generalization when compared with a conventional feedforward network. In the present paper, the example shows only a network developed from a neuronal reflexive circuit with some useful artifices, nevertheless without the intention of covering all possibilities devised.

  15. A Network Protection Framework through Artificial Immunity

    CERN Document Server

    Hilker, Michael

    2008-01-01

    Current network protection systems use a collection of intelligent components - e.g. classifiers or rule-based firewall systems to detect intrusions and anomalies and to secure a network against viruses, worms, or trojans. However, these network systems rely on individuality and support an architecture with less collaborative work of the protection components. They give less administration support for maintenance, but offer a large number of individual single points of failures - an ideal situation for network attacks to succeed. In this work, we discuss the required features, the performance, and the problems of a distributed protection system called {\\it SANA}. It consists of a cooperative architecture, it is motivated by the human immune system, where the components correspond to artificial immune cells that are connected for their collaborative work. SANA promises a better protection against intruders than common known protection systems through an adaptive self-management while keeping the resources effi...

  16. SANA - Network Protection through artificial Immunity

    CERN Document Server

    Hilker, Michael

    2008-01-01

    Current network protection systems use a collection of intelligent components - e.g. classifiers or rule-based firewall systems to detect intrusions and anomalies and to secure a network against viruses, worms, or trojans. However, these network systems rely on individuality and support an architecture with less collaborative work of the protection components. They give less administration support for maintenance, but offer a large number of individual single points of failures - an ideal situation for network attacks to succeed. In this work, we discuss the required features, the performance, and the problems of a distributed protection system called SANA. It consists of a cooperative architecture, it is motivated by the human immune system, where the components correspond to artificial immune cells that are connected for their collaborative work. SANA promises a better protection against intruders than common known protection systems through an adaptive self-management while keeping the resources efficientl...

  17. Applying Artificial Neural Networks for Face Recognition

    Directory of Open Access Journals (Sweden)

    Thai Hoang Le

    2011-01-01

    Full Text Available This paper introduces some novel models for all steps of a face recognition system. In the step of face detection, we propose a hybrid model combining AdaBoost and Artificial Neural Network (ABANN to solve the process efficiently. In the next step, labeled faces detected by ABANN will be aligned by Active Shape Model and Multi Layer Perceptron. In this alignment step, we propose a new 2D local texture model based on Multi Layer Perceptron. The classifier of the model significantly improves the accuracy and the robustness of local searching on faces with expression variation and ambiguous contours. In the feature extraction step, we describe a methodology for improving the efficiency by the association of two methods: geometric feature based method and Independent Component Analysis method. In the face matching step, we apply a model combining many Neural Networks for matching geometric features of human face. The model links many Neural Networks together, so we call it Multi Artificial Neural Network. MIT + CMU database is used for evaluating our proposed methods for face detection and alignment. Finally, the experimental results of all steps on CallTech database show the feasibility of our proposed model.

  18. Performance of artificial neural networks and genetical evolved artificial neural networks unfolding techniques

    International Nuclear Information System (INIS)

    With the Bonner spheres spectrometer neutron spectrum is obtained through an unfolding procedure. Monte Carlo methods, Regularization, Parametrization, Least-squares, and Maximum Entropy are some of the techniques utilized for unfolding. In the last decade methods based on Artificial Intelligence Technology have been used. Approaches based on Genetic Algorithms and Artificial Neural Networks (Ann) have been developed in order to overcome the drawbacks of previous techniques. Nevertheless the advantages of Ann still it has some drawbacks mainly in the design process of the network, vg the optimum selection of the architectural and learning Ann parameters. In recent years the use of hybrid technologies, combining Ann and genetic algorithms, has been utilized to. In this work, several Ann topologies were trained and tested using Ann and Genetically Evolved Artificial Neural Networks in the aim to unfold neutron spectra using the count rates of a Bonner sphere spectrometer. Here, a comparative study of both procedures has been carried out. (Author)

  19. Performance of artificial neural networks and genetical evolved artificial neural networks unfolding techniques

    Energy Technology Data Exchange (ETDEWEB)

    Ortiz R, J. M. [Escuela Politecnica Superior, Departamento de Electrotecnia y Electronica, Avda. Menendez Pidal s/n, Cordoba (Spain); Martinez B, M. R.; Vega C, H. R. [Universidad Autonoma de Zacatecas, Unidad Academica de Estudios Nucleares, Calle Cipres No. 10, Fracc. La Penuela, 98068 Zacatecas (Mexico); Gallego D, E.; Lorente F, A. [Universidad Politecnica de Madrid, Departamento de Ingenieria Nuclear, ETSI Industriales, C. Jose Gutierrez Abascal 2, 28006 Madrid (Spain); Mendez V, R.; Los Arcos M, J. M.; Guerrero A, J. E., E-mail: morvymm@yahoo.com.m [CIEMAT, Laboratorio de Metrologia de Radiaciones Ionizantes, Avda. Complutense 22, 28040 Madrid (Spain)

    2011-02-15

    With the Bonner spheres spectrometer neutron spectrum is obtained through an unfolding procedure. Monte Carlo methods, Regularization, Parametrization, Least-squares, and Maximum Entropy are some of the techniques utilized for unfolding. In the last decade methods based on Artificial Intelligence Technology have been used. Approaches based on Genetic Algorithms and Artificial Neural Networks (Ann) have been developed in order to overcome the drawbacks of previous techniques. Nevertheless the advantages of Ann still it has some drawbacks mainly in the design process of the network, vg the optimum selection of the architectural and learning Ann parameters. In recent years the use of hybrid technologies, combining Ann and genetic algorithms, has been utilized to. In this work, several Ann topologies were trained and tested using Ann and Genetically Evolved Artificial Neural Networks in the aim to unfold neutron spectra using the count rates of a Bonner sphere spectrometer. Here, a comparative study of both procedures has been carried out. (Author)

  20. Hair Loss Diagnosis Using Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Ahmad Esfandiari

    2012-09-01

    Full Text Available Hair is an appendage of the skin that plays an important role in the beauty of people's face. Daily averages of 50 to 80 hairs are shed naturally. Various factors are effective in hair loss. In this paper using the eight influence attributes of gender, age, genetic factors, surgery, pregnancy, Zinc deficiency, iron deficiency, anemia and the use of cosmetics, the amount of hair loss is predicted. This work has been performed using artificial neural networks. 60 percent of the collected data was used for train, 20 percent for validation and the remaining 20 percent is used for testing the neural networks. For this, various training algorithms has been used. The result of the implementation of these algorithms has been compared. It seems that neural networks can be successful to predict hair loss.

  1. Dynamic artificial neural networks with affective systems.

    Science.gov (United States)

    Schuman, Catherine D; Birdwell, J Douglas

    2013-01-01

    Artificial neural networks (ANNs) are processors that are trained to perform particular tasks. We couple a computational ANN with a simulated affective system in order to explore the interaction between the two. In particular, we design a simple affective system that adjusts the threshold values in the neurons of our ANN. The aim of this paper is to demonstrate that this simple affective system can control the firing rate of the ensemble of neurons in the ANN, as well as to explore the coupling between the affective system and the processes of long term potentiation (LTP) and long term depression (LTD), and the effect of the parameters of the affective system on its performance. We apply our networks with affective systems to a simple pole balancing example and briefly discuss the effect of affective systems on network performance.

  2. Comparing artificial and biological dynamical neural networks

    Science.gov (United States)

    McAulay, Alastair D.

    2006-05-01

    Modern computers can be made more friendly and otherwise improved by making them behave more like humans. Perhaps we can learn how to do this from biology in which human brains evolved over a long period of time. Therefore, we first explain a commonly used biological neural network (BNN) model, the Wilson-Cowan neural oscillator, that has cross-coupled excitatory (positive) and inhibitory (negative) neurons. The two types of neurons are used for frequency modulation communication between neurons which provides immunity to electromagnetic interference. We then evolve, for the first time, an artificial neural network (ANN) to perform the same task. Two dynamical feed-forward artificial neural networks use cross-coupling feedback (like that in a flip-flop) to form an ANN nonlinear dynamic neural oscillator with the same equations as the Wilson-Cowan neural oscillator. Finally we show, through simulation, that the equations perform the basic neural threshold function, switching between stable zero output and a stable oscillation, that is a stable limit cycle. Optical implementation with an injected laser diode and future research are discussed.

  3. Transient Stability Assessment using Artificial Neural Networks

    OpenAIRE

    Krishna, S; Padiyar, KR

    2000-01-01

    Online transient stability assessment (TSA) of a power system is not yet feasible due to the intensive computation involved. Artificial neural networks (ANN) have been proposed as one of the approaches to this problem because of their ability to quickly map nonlinear relationships between the input data and the output. In this paper a review of the previously published papers on TSA using ANN is presented. The paper also reports the results of the application of ANN to the problem of TSA of a...

  4. Proceedings of intelligent engineering systems through artificial neural networks

    International Nuclear Information System (INIS)

    This book contains the edited versions of the technical presentation of ANNIE '91, the first international meeting on Artificial Neural Networks in Engineering. The conference covered the theory of Artificial Neural Networks and its contributions in the engineering domain and attracted researchers from twelve countries. The papers in this edited book are grouped into four categories: Artificial Neural Network Architectures; Pattern Recognition; Adaptive Control, Diagnosis and Process Monitoring; and Neuro-Engineering Systems

  5. Web Page Categorization Using Artificial Neural Networks

    CERN Document Server

    Kamruzzaman, S M

    2010-01-01

    Web page categorization is one of the challenging tasks in the world of ever increasing web technologies. There are many ways of categorization of web pages based on different approach and features. This paper proposes a new dimension in the way of categorization of web pages using artificial neural network (ANN) through extracting the features automatically. Here eight major categories of web pages have been selected for categorization; these are business & economy, education, government, entertainment, sports, news & media, job search, and science. The whole process of the proposed system is done in three successive stages. In the first stage, the features are automatically extracted through analyzing the source of the web pages. The second stage includes fixing the input values of the neural network; all the values remain between 0 and 1. The variations in those values affect the output. Finally the third stage determines the class of a certain web page out of eight predefined classes. This stage i...

  6. Advances in Artificial Neural Networks – Methodological Development and Application

    Directory of Open Access Journals (Sweden)

    Yanbo Huang

    2009-08-01

    Full Text Available Artificial neural networks as a major soft-computing technology have been extensively studied and applied during the last three decades. Research on backpropagation training algorithms for multilayer perceptron networks has spurred development of other neural network training algorithms for other networks such as radial basis function, recurrent network, feedback network, and unsupervised Kohonen self-organizing network. These networks, especially the multilayer perceptron network with a backpropagation training algorithm, have gained recognition in research and applications in various scientific and engineering areas. In order to accelerate the training process and overcome data over-fitting, research has been conducted to improve the backpropagation algorithm. Further, artificial neural networks have been integrated with other advanced methods such as fuzzy logic and wavelet analysis, to enhance the ability of data interpretation and modeling and to avoid subjectivity in the operation of the training algorithm. In recent years, support vector machines have emerged as a set of high-performance supervised generalized linear classifiers in parallel with artificial neural networks. A review on development history of artificial neural networks is presented and the standard architectures and algorithms of artificial neural networks are described. Furthermore, advanced artificial neural networks will be introduced with support vector machines, and limitations of ANNs will be identified. The future of artificial neural network development in tandem with support vector machines will be discussed in conjunction with further applications to food science and engineering, soil and water relationship for crop management, and decision support for precision agriculture. Along with the network structures and training algorithms, the applications of artificial neural networks will be reviewed as well, especially in the fields of agricultural and biological

  7. Detection of Wildfires with Artificial Neural Networks

    Science.gov (United States)

    Umphlett, B.; Leeman, J.; Morrissey, M. L.

    2011-12-01

    Currently fire detection for the National Oceanic and Atmospheric Administration (NOAA) using satellite data is accomplished with algorithms and error checking human analysts. Artificial neural networks (ANNs) have been shown to be more accurate than algorithms or statistical methods for applications dealing with multiple datasets of complex observed data in the natural sciences. ANNs also deal well with multiple data sources that are not all equally reliable or equally informative to the problem. An ANN was tested to evaluate its accuracy in detecting wildfires utilizing polar orbiter numerical data from the Advanced Very High Resolution Radiometer (AVHRR). Datasets containing locations of known fires were gathered from the NOAA's polar orbiting satellites via the Comprehensive Large Array-data Stewardship System (CLASS). The data was then calibrated and navigation corrected using the Environment for Visualizing Images (ENVI). Fires were located with the aid of shapefiles generated via ArcGIS. Afterwards, several smaller ten pixel by ten pixel datasets were created for each fire (using the ENVI corrected data). Several datasets were created for each fire in order to vary fire position and avoid training the ANN to look only at fires in the center of an image. Datasets containing no fires were also created. A basic pattern recognition neural network was established with the MATLAB neural network toolbox. The datasets were then randomly separated into categories used to train, validate, and test the ANN. To prevent over fitting of the data, the mean squared error (MSE) of the network was monitored and training was stopped when the MSE began to rise. Networks were tested using each channel of the AVHRR data independently, channels 3a and 3b combined, and all six channels. The number of hidden neurons for each input set was also varied between 5-350 in steps of 5 neurons. Each configuration was run 10 times, totaling about 4,200 individual network evaluations. Thirty

  8. Layered learning of soccer robot based on artificial neural network

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    Discusses the application of artificial neural network for MIROSOT, introduces a layered model of BP network of soccer robot for learning basic behavior and cooperative behavior, and concludes from experimental results that the model is effective.

  9. Spatial predictive mapping using artificial neural networks

    Science.gov (United States)

    Noack, S.; Knobloch, A.; Etzold, S. H.; Barth, A.; Kallmeier, E.

    2014-11-01

    The modelling or prediction of complex geospatial phenomena (like formation of geo-hazards) is one of the most important tasks for geoscientists. But in practice it faces various difficulties, caused mainly by the complexity of relationships between the phenomena itself and the controlling parameters, as well by limitations of our knowledge about the nature of physical/ mathematical relationships and by restrictions regarding accuracy and availability of data. In this situation methods of artificial intelligence, like artificial neural networks (ANN) offer a meaningful alternative modelling approach compared to the exact mathematical modelling. In the past, the application of ANN technologies in geosciences was primarily limited due to difficulties to integrate it into geo-data processing algorithms. In consideration of this background, the software advangeo® was developed to provide a normal GIS user with a powerful tool to use ANNs for prediction mapping and data preparation within his standard ESRI ArcGIS environment. In many case studies, such as land use planning, geo-hazards analysis and prevention, mineral potential mapping, agriculture & forestry advangeo® has shown its capabilities and strengths. The approach is able to add considerable value to existing data.

  10. Mesh deformation based on artificial neural networks

    Science.gov (United States)

    Stadler, Domen; Kosel, Franc; Čelič, Damjan; Lipej, Andrej

    2011-09-01

    In the article a new mesh deformation algorithm based on artificial neural networks is introduced. This method is a point-to-point method, meaning that it does not use connectivity information for calculation of the mesh deformation. Two already known point-to-point methods, based on interpolation techniques, are also presented. In contrast to the two known interpolation methods, the new method does not require a summation over all boundary nodes for one displacement calculation. The consequence of this fact is a shorter computational time of mesh deformation, which is proven by different deformation tests. The quality of the deformed meshes with all three deformation methods was also compared. Finally, the generated and the deformed three-dimensional meshes were used in the computational fluid dynamics numerical analysis of a Francis water turbine. A comparison of the analysis results was made to prove the applicability of the new method in every day computation.

  11. Evolving A-Type Artificial Neural Networks

    CERN Document Server

    Orr, Ewan

    2011-01-01

    We investigate Turing's notion of an A-type artificial neural network. We study a refinement of Turing's original idea, motivated by work of Teuscher, Bull, Preen and Copeland. Our A-types can process binary data by accepting and outputting sequences of binary vectors; hence we can associate a function to an A-type, and we say the A-type {\\em represents} the function. There are two modes of data processing: clamped and sequential. We describe an evolutionary algorithm, involving graph-theoretic manipulations of A-types, which searches for A-types representing a given function. The algorithm uses both mutation and crossover operators. We implemented the algorithm and applied it to three benchmark tasks. We found that the algorithm performed much better than a random search. For two out of the three tasks, the algorithm with crossover performed better than a mutation-only version.

  12. Computational Ecology: Artificial Neural Networks and Their Applications

    Directory of Open Access Journals (Sweden)

    WenJun Zhang

    2011-04-01

    Full Text Available A book, Computational Ecology: Artificial Neural Networks and Their Applications, published in 2010, was introduced and reviewed. This book provides readers with deep insights on algorithms, codes, and applications of artificial neural networks in ecology. A science discipline, computational ecology, is clearly defined and outlined in the book.

  13. Artificial neural networks in predicting current in electric arc furnaces

    Science.gov (United States)

    Panoiu, M.; Panoiu, C.; Iordan, A.; Ghiormez, L.

    2014-03-01

    The paper presents a study of the possibility of using artificial neural networks for the prediction of the current and the voltage of Electric Arc Furnaces. Multi-layer perceptron and radial based functions Artificial Neural Networks implemented in Matlab were used. The study is based on measured data items from an Electric Arc Furnace in an industrial plant in Romania.

  14. DEM interpolation based on artificial neural networks

    Science.gov (United States)

    Jiao, Limin; Liu, Yaolin

    2005-10-01

    This paper proposed a systemic resolution scheme of Digital Elevation model (DEM) interpolation based on Artificial Neural Networks (ANNs). In this paper, we employ BP network to fit terrain surface, and then detect and eliminate the samples with gross errors. This paper uses Self-organizing Feature Map (SOFM) to cluster elevation samples. The study area is divided into many more homogenous tiles after clustering. BP model is employed to interpolate DEM in each cluster. Because error samples are eliminated and clusters are built, interpolation result is better. The case study indicates that ANN interpolation scheme is feasible. It also shows that ANN can get a more accurate result by comparing ANN with polynomial and spline interpolation. ANN interpolation doesn't need to determine the interpolation function beforehand, so manmade influence is lessened. The ANN interpolation is more automatic and intelligent. At the end of the paper, we propose the idea of constructing ANN surface model. This model can be used in multi-scale DEM visualization, and DEM generalization, etc.

  15. Artificial neural networks in neutron dosimetry

    International Nuclear Information System (INIS)

    An artificial neural network (ANN) has been designed to obtain neutron doses using only the count rates of a Bonner spheres spectrometer (BSS). Ambient, personal and effective neutron doses were included. One hundred and eighty-one neutron spectra were utilised to calculate the Bonner count rates and the neutron doses. The spectra were transformed from lethargy to energy distribution and were re-binned to 31 energy groups using the MCNP 4C code. Re-binned spectra, UTA4 response matrix and fluence-to-dose coefficients were used to calculate the count rates in the BSS and the doses. Count rates were used as input and the respective doses were used as output during neural network training. Training and testing were carried out in the MATLABR environment. The impact of uncertainties in BSS count rates upon the dose quantities calculated with the ANN was investigated by modifying by ±5% the BSS count rates used in the training set. The use of ANNs in neutron dosimetry is an alternative procedure that overcomes the drawbacks associated with this ill-conditioned problem. (authors)

  16. Groundwater Level Predictions Using Artificial Neural Networks

    Institute of Scientific and Technical Information of China (English)

    毛晓敏; 尚松浩; 刘翔

    2002-01-01

    The prediction of groundwater level is important for the use and management of groundwater resources. In this paper, the artificial neural networks (ANN) were used to predict groundwater level in the Dawu Aquifer of Zibo in Eastern China. The first step was an auto-correlation analysis of the groundwater level which showed that the monthly groundwater level was time dependent. An auto-regression type ANN (ARANN) model and a regression-auto-regression type ANN (RARANN) model using back-propagation algorithm were then used to predict the groundwater level. Monthly data from June 1988 to May 1998 was used for the network training and testing. The results show that the RARANN model is more reliable than the ARANN model, especially in the testing period, which indicates that the RARANN model can describe the relationship between the groundwater fluctuation and main factors that currently influence the groundwater level. The results suggest that the model is suitable for predicting groundwater level fluctuations in this area for similar conditions in the future.

  17. Hurst Parameter Estimation Using Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    S..Ledesma-Orozco

    2011-08-01

    Full Text Available The Hurst parameter captures the amount of long-range dependence (LRD in a time series. There are severalmethods to estimate the Hurst parameter, being the most popular: the variance-time plot, the R/S plot, theperiodogram, and Whittle’s estimator. The first three are graphical methods, and the estimation accuracy depends onhow the plot is interpreted and calculated. In contrast, Whittle’s estimator is based on a maximum likelihood techniqueand does not depend on a graph reading; however, it is computationally expensive. A new method to estimate theHurst parameter is proposed. This new method is based on an artificial neural network. Experimental results showthat this method outperforms traditional approaches, and can be used on applications where a fast and accurateestimate of the Hurst parameter is required, i.e., computer network traffic control. Additionally, the Hurst parameterwas computed on series of different length using several methods. The simulation results show that the proposedmethod is at least ten times faster than traditional methods.

  18. Patterning and predicting aquatic macroinvertebrate diversities using artificial neural network

    NARCIS (Netherlands)

    Park, Y.S.; Verdonschot, P.F.M.; Chon, T.S.; Lek, S.

    2003-01-01

    A counterpropagation neural network (CPN) was applied to predict species richness (SR) and Shannon diversity index (SH) of benthic macroinvertebrate communities using 34 environmental variables. The data were collected at 664 sites at 23 different water types such as springs, streams, rivers, canals

  19. Mechanical stress in abdominal aortic aneurysms using artificial neural networks

    OpenAIRE

    Soudah Prieto, Eduardo; Rodriguez, Jose; López González, Roberto

    2015-01-01

    Combination of numerical modeling and artificial intelligence (AI) in bioengineering processes are a promising pathway for the further development of bioengineering sciences. The objective of this work is to use Artificial Neural Networks (ANN) to reduce the long computational times needed in the analysis of shear stress in the Abdominal Aortic Aneurysm (AAA) by finite element methods (FEM). For that purpose two different neural networks are created. The first neural network (Mesh Neural Netw...

  20. Artificial Neural Network applied to lightning flashes

    Science.gov (United States)

    Gin, R. B.; Guedes, D.; Bianchi, R.

    2013-05-01

    The development of video cameras enabled cientists to study lightning discharges comportment with more precision. The main goal of this project is to create a system able to detect images of lightning discharges stored in videos and classify them using an Artificial Neural Network (ANN)using C Language and OpenCV libraries. The developed system, can be split in two different modules: detection module and classification module. The detection module uses OpenCV`s computer vision libraries and image processing techniques to detect if there are significant differences between frames in a sequence, indicating that something, still not classified, occurred. Whenever there is a significant difference between two consecutive frames, two main algorithms are used to analyze the frame image: brightness and shape algorithms. These algorithms detect both shape and brightness of the event, removing irrelevant events like birds, as well as detecting the relevant events exact position, allowing the system to track it over time. The classification module uses a neural network to classify the relevant events as horizontal or vertical lightning, save the event`s images and calculates his number of discharges. The Neural Network was implemented using the backpropagation algorithm, and was trained with 42 training images , containing 57 lightning events (one image can have more than one lightning). TheANN was tested with one to five hidden layers, with up to 50 neurons each. The best configuration achieved a success rate of 95%, with one layer containing 20 neurons (33 test images with 42 events were used in this phase). This configuration was implemented in the developed system to analyze 20 video files, containing 63 lightning discharges previously manually detected. Results showed that all the lightning discharges were detected, many irrelevant events were unconsidered, and the event's number of discharges was correctly computed. The neural network used in this project achieved a

  1. Liquefaction Microzonation of Babol City Using Artificial Neural Network

    DEFF Research Database (Denmark)

    Farrokhzad, F.; Choobbasti, A.J.; Barari, Amin

    2012-01-01

    that will be less susceptible to damage during earthquakes. The scope of present study is to prepare the liquefaction microzonation map for the Babol city based on Seed and Idriss (1983) method using artificial neural network. Artificial neural network (ANN) is one of the artificial intelligence (AI) approaches...... is proposed in this paper. To meet this objective, an effort is made to introduce a total of 30 boreholes data in an area of 7 km2 which includes the results of field tests into the neural network model and the prediction of artificial neural network is checked in some test boreholes, finally the liquefaction...... that can be classified as machine learning. Simplified methods have been practiced by researchers to assess nonlinear liquefaction potential of soil. In order to address the collective knowledge built-up in conventional liquefaction engineering, an alternative general regression neural network model...

  2. Forecasting Zakat collection using artificial neural network

    Science.gov (United States)

    Sy Ahmad Ubaidillah, Sh. Hafizah; Sallehuddin, Roselina

    2013-04-01

    'Zakat', "that which purifies" or "alms", is the giving of a fixed portion of one's wealth to charity, generally to the poor and needy. It is one of the five pillars of Islam, and must be paid by all practicing Muslims who have the financial means (nisab). 'Nisab' is the minimum level to determine whether there is a 'zakat' to be paid on the assets. Today, in most Muslim countries, 'zakat' is collected through a decentralized and voluntary system. Under this voluntary system, 'zakat' committees are established, which are tasked with the collection and distribution of 'zakat' funds. 'Zakat' promotes a more equitable redistribution of wealth, and fosters a sense of solidarity amongst members of the 'Ummah'. The Malaysian government has established a 'zakat' center at every state to facilitate the management of 'zakat'. The center has to have a good 'zakat' management system to effectively execute its functions especially in the collection and distribution of 'zakat'. Therefore, a good forecasting model is needed. The purpose of this study is to develop a forecasting model for Pusat Zakat Pahang (PZP) to predict the total amount of collection from 'zakat' of assets more precisely. In this study, two different Artificial Neural Network (ANN) models using two different learning algorithms are developed; Back Propagation (BP) and Levenberg-Marquardt (LM). Both models are developed and compared in terms of their accuracy performance. The best model is determined based on the lowest mean square error and the highest correlations values. Based on the results obtained from the study, BP neural network is recommended as the forecasting model to forecast the collection from 'zakat' of assets for PZP.

  3. Artificial neural network models for image understanding

    Science.gov (United States)

    Kulkarni, Arun D.; Byars, P.

    1991-06-01

    In this paper we introduce a new class of artificial neural network (ANN) models based on transformed domain feature extraction. Many optical and/or digital recognition systems based on transformed domain feature extraction are available in practice. Optical systems are inherently parallel in nature and are preferred for real time applications, whereas digital systems are more suitable for nonlinear operations. In our ANN models we combine advantages of both digital and optical systems. Many transformed domain feature extraction techniques have been developed during the last three decades. They include: the Fourier transform (FT), the Walsh Hadamard transform (WHT), the discrete cosine transform (DCT), etc. As an example, we have developed ANN models using the FT and WHT domain features. The models consist of two stages, the feature extraction stage and the recognition stage. We have used back-propagation and competitive learning algorithms in the recognition stage. We have used these ANN models for invariant object recognition. The models have been used successfully to recognize various types of aircraft, and also have been tested with test patterns. ANN models based on other transforms can be developed in a similar fashion.

  4. Artificial neural network for violation analysis

    International Nuclear Information System (INIS)

    Barrier removal (BR) is a safety-related violation, and it can be analyzed in terms of benefits, costs, and potential deficits. In order to allow designers to integrate BR into the risk analysis during the initial design phase or during re-design work, we propose a connectionist method integrating self-organizing maps (SOM). The basic SOM is an artificial neural network that, on the basis of the information contained in a multi-dimensional space, generates a space of lesser dimensions. Three algorithms--Unsupervised SOM, Supervised SOM, and Hierarchical SOM--have been developed to permit BR classification and prediction in terms of the different criteria. The proposed method can be used, on the one hand, to foresee/predict the possibility level of a new/changed barrier (prospective analysis), and on the other hand, to synthetically regroup/rearrange the BR of a given human-machine system (retrospective analysis). We applied this method to the BR analysis of an experimental railway simulator, and our preliminary results are presented here

  5. Electronic circuits modeling using artificial neural networks

    Directory of Open Access Journals (Sweden)

    Andrejević Miona V.

    2003-01-01

    Full Text Available In this paper artificial neural networks (ANN are applied to modeling of electronic circuits. ANNs are used for application of the black-box modeling concept in the time domain. Modeling process is described, so the topology of the ANN, the testing signal used for excitation, together with the complexity of ANN are considered. The procedure is first exemplified in modeling of resistive circuits. MOS transistor, as a four-terminal device, is modeled. Then nonlinear negative resistive characteristic is modeled in order to be used as a piece-wise linear resistor in Chua's circuit. Examples of modeling nonlinear dynamic circuits are given encompassing a variety of modeling problems. A nonlinear circuit containing quartz oscillator is considered for modeling. Verification of the concept is performed by verifying the ability of the model to generalize i.e. to create acceptable responses to excitations not used during training. Implementation of these models within a behavioral simulator is exemplified. Every model is implemented in realistic surrounding in order to show its interaction, and of course, its usage and purpose.

  6. Development and Evolution of Neural Networks in an Artificial Chemistry

    OpenAIRE

    Astor, Jens C.; Adami, Christoph

    1998-01-01

    We present a model of decentralized growth for Artificial Neural Networks (ANNs) inspired by the development and the physiology of real nervous systems. In this model, each individual artificial neuron is an autonomous unit whose behavior is determined only by the genetic information it harbors and local concentrations of substrates modeled by a simple artificial chemistry. Gene expression is manifested as axon and dendrite growth, cell division and differentiation, substrate production and c...

  7. Artificial Neural Networks: A New Approach to Predicting Application Behavior.

    Science.gov (United States)

    Gonzalez, Julie M. Byers; DesJardins, Stephen L.

    2002-01-01

    Applied the technique of artificial neural networks to predict which students were likely to apply to one research university. Compared the results to the traditional analysis tool, logistic regression modeling. Found that the addition of artificial intelligence models was a useful new tool for predicting student application behavior. (EV)

  8. Geochemical characterization of oceanic basalts using artificial neural network

    Digital Repository Service at National Institute of Oceanography (India)

    Das, P.; Iyer, S.D.

    method is specifically needed to identify the OFB as normal (N-MORB), enriched (E-MORB) and ocean island basalts (OIB). Artificial Neural Network (ANN) technique as a supervised Learning Vector Quantisation (LVQ) is applied to identify the inherent...

  9. Impulsive Neural Networks Algorithm Based on the Artificial Genome Model

    Directory of Open Access Journals (Sweden)

    Yuan Gao

    2014-05-01

    Full Text Available To describe gene regulatory networks, this article takes the framework of the artificial genome model and proposes impulsive neural networks algorithm based on the artificial genome model. Firstly, the gene expression and the cell division tree are applied to generate spiking neurons with specific attributes, neural network structure, connection weights and specific learning rules of each neuron. Next, the gene segment duplications and divergence model are applied to design the evolutionary algorithm of impulsive neural networks at the level of the artificial genome. The dynamic changes of developmental gene regulatory networks are controlled during the whole evolutionary process. Finally, the behavior of collecting food for autonomous intelligent agent is simulated, which is driven by nerves. Experimental results demonstrate that the algorithm in this article has the evolutionary ability on large-scale impulsive neural networks

  10. Transient stability Assessment using Artificial Neural Network Considering Fault Location

    OpenAIRE

    P.K.Olulope; Folly, K. A.; Chowdhury, S.; Chowdhury, S. P.

    2010-01-01

    This paper describes the capability of artificial neural network for predicting the critical clearing time of power system. It combines the advantages of time domain integration schemes with artificial neural network for real time transient stability assessment. The training of ANN is done using selected features as input and critical fault clearing time (CCT) as desire target. A single contingency was applied and the target CCT was found using time domain simulatio...

  11. Artificial neural network based modelling of internal combustion engine performance

    OpenAIRE

    Boruah, Dibakor; Thakur, Pintu Kumar; Baruah, Dipal

    2016-01-01

    The present study aims to quantify the applicability of artificial neural network as a black-box model for internal combustion engine performance. In consequence, an artificial neural network (ANN) based model for a four cylinder, four stroke internal combustion diesel engine has been developed on the basis of specific input and output factors, which have been taken from experimental readings for different load and engine speed circumstances. The input parameters that have been used to create...

  12. Application of Artificial Neural Networks to Contraception Study

    Institute of Scientific and Technical Information of China (English)

    周利锋; 高尔生; 金丕焕

    1998-01-01

    As a newly developed border line science, the artificial neural network (ANN)has been applied in many fields. The ANN was used in the selection of contraceptives in the article, and the performances of the artificial neural networks and traditional multivariate logistic regression analysis method were compared with the training data and the testing data by receiver operating characteristic (ROC) curves. The results imply that ANN may be applied and developed further in statistics and medical fields hopefully.

  13. Building an Artificial Idiotopic Immune Model Based on Artificial Neural Network Ideology

    Directory of Open Access Journals (Sweden)

    Hossam Meshref

    2013-01-01

    Full Text Available In the literature, there were many research efforts that utilized the artificial immune networks to model their designed applications, but they were considerably complicated, and restricted to a few areas that such as computer security applications. The objective of this research is to introduce a new model for artificial immune networks that adopts features from other biological successful models to overcome its complexity such as the artificial neural networks. Common concepts between the two systems were investigated to design a simple, yet a robust, model of artificial immune networks. Three artificial neural networks learning models were available to choose from in the research design: supervised, unsupervised, and reinforcement learning models. However, it was found that the reinforcement model is the most suitable model. Research results examined network parameters, and appropriate relations between concentration ranges and their dependent parameters as well as the expected reward during network learning. In conclusion, it is recommended the use of the designed model by other researchers in different applications such as controlling robots in hazardous environment to save human lives as well as using it on image retrieval in general to help the police department identify suspects.

  14. Indoor Positioning System Using Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Hamid Mehmood

    2010-01-01

    Full Text Available Problem statement: Location knowledge in indoor environment using Indoor Positioning Systems (IPS has become very useful and popular in recent years. A number of Location Based Services (LBS have been developed, which are based on IPS, these LBS include asset tracking, inventory management and security based applications. Many next-generation LBS applications such as social networking, local search, advertising and geo-tagging are expected to be used in urban and indoor environments where GNSS either underperforms in terms of fix times or accuracy, or fails altogether. To develop an IPS based on Wi-Fi Received Signal Strength (RSS using Artificial Neural Networks (ANN, which should use already available Wi-Fi infrastructure in a heterogeneous environment. Approach: This study discussed the use of ANN for IPS using RSS in an indoor wireless facility which has varying human activity, material of walls and type of Wireless Access Points (WAP, hence simulating a heterogeneous environment. The proposed system used backpropogation method with 4 input neurons, 2 output neurons and 4 hidden layers. The model was trained with three different types of training data. The accuracy assessment for each training data was performed by computing the distance error and average distance error. Results: The results of the experiments showed that using ANN with the proposed method of collecting training data, maximum accuracy of 0.7 m can be achieved, with 30% of the distance error less than 1 m and 60% of the distance error within the range of 1-2 m. Whereas maximum accuracy of 1.01 can be achieved with the commonly used method of collecting training data. The proposed model also showed 67% more accuracy as compared to a probabilistic model. Conclusion: The results indicated that ANN based IPS can provide accuracy and precision which is quite adequate for the development of indoor LBS while using the already available Wi-Fi infrastructure, also the proposed method

  15. The efficacy of ultrasonic irrigation to remove artificially placed dentine debris from human root canals prepared using instruments of varying taper

    NARCIS (Netherlands)

    van der Sluis, LWM; Wu, MK; Wesselink, PR

    2005-01-01

    Aim To investigate the influence of the taper of root canals on the effectiveness of ultrasonic irrigation to remove artificially placed dentine debris. Method Forty-four maxillary and mandibular canines were selected after bucco-lingual and mesio-distal radiographs indicated that their internal dia

  16. Segmentation of magnetic resonance images using an artificial neural network.

    OpenAIRE

    Piraino, D. W.; Amartur, S. C.; Richmond, B. J.; Schils, J. P.; Thome, J. M.; Weber, P. B.

    1991-01-01

    Signal intensities from intermediate and T2 weighted spin echo images of the brain were used as inputs into an artificial neural network (ANN). The signal intensities were used to train the network to recognize anatomically-important segments. The ANN was a self-organizing map (SOM) neural network which develops a continuous topographical map of the signal intensities within the two images. The neural network segmented images demonstrated good correlation with white matter, gray matter, and c...

  17. ECO INVESTMENT PROJECT MANAGEMENT THROUGH TIME APPLYING ARTIFICIAL NEURAL NETWORKS

    Directory of Open Access Journals (Sweden)

    Tamara Gvozdenović

    2007-06-01

    Full Text Available he concept of project management expresses an indispensable approach to investment projects. Time is often the most important factor in these projects. The artificial neural network is the paradigm of data processing, which is inspired by the one used by the biological brain, and it is used in numerous, different fields, among which is the project management. This research is oriented to application of artificial neural networks in managing time of investment project. The artificial neural networks are used to define the optimistic, the most probable and the pessimistic time in PERT method. The program package Matlab: Neural Network Toolbox is used in data simulation. The feed-forward back propagation network is chosen.

  18. Some structural determinants of Pavlovian conditioning in artificial neural networks

    NARCIS (Netherlands)

    Sanchez, Jose M.; Galeazzi, Juan M.; Burgos, Jose E.

    2010-01-01

    This paper investigates the possible role of neuroanatomical features in Pavlovian conditioning, via computer simulations with layered, feedforward artificial neural networks. The networks' structure and functioning are described by a strongly bottom-up model that takes into account the roles of hip

  19. Multiple image sensor data fusion through artificial neural networks

    Science.gov (United States)

    With multisensor data fusion technology, the data from multiple sensors are fused in order to make a more accurate estimation of the environment through measurement, processing and analysis. Artificial neural networks are the computational models that mimic biological neural networks. With high per...

  20. Optimal Brain Surgeon on Artificial Neural Networks in

    DEFF Research Database (Denmark)

    Christiansen, Niels Hørbye; Job, Jonas Hultmann; Klyver, Katrine;

    2012-01-01

    It is shown how the procedure know as optimal brain surgeon can be used to trim and optimize artificial neural networks in nonlinear structural dynamics. Beside optimizing the neural network, and thereby minimizing computational cost in simulation, the surgery procedure can also serve as a quick...

  1. THE ARTIFICIAL NEURAL NETWORK OF FORECASTING OPEN MINING SLOPE STABILITY

    Institute of Scientific and Technical Information of China (English)

    魏春启; 白润才

    2000-01-01

    The artificial neural network model which forecasts Open Mining Slope stability is established by neural network theory and method. The nonlinear reflection relation between stability target of open mining slope and its influence factor is described. The method of forecasting Open Mining Slope stability is brought forward.

  2. Parameter optimization of pharmacokinetics based on artificial immune network

    Institute of Scientific and Technical Information of China (English)

    LIU Li; ZHOU Shao-dan; LU Hong-wen; XIE Fen; XU Wen-bo

    2008-01-01

    A new method for parameter optimization of pharmacokinetics based on an artificial immune network named PKAIN is proposed.To improve local searching ability of the artificial immune network,a partition-based concurrent simplex mutation is developed.By means of evolution of network cells in the PKAIN artificial immune network,an optimal set of parameters of a given pharmacokinetic model is obtained.The Laplace transform is applied to the pharmacokinetic difierential equations of remifentanil and its major metabolite,remifentanil acid.The PKAIN method is used to optimize parameters of the derived compartment models.Experimental results show that the twocompartment model is sufficient for the pharmacokinetic study of remifentanil acid for patients with mild degree of renal impairment.

  3. Study on optimization control method based on artificial neural network

    Institute of Scientific and Technical Information of China (English)

    FU Hua; SUN Shao-guang; XU Zhen-Iiang

    2005-01-01

    In the goal optimization and control optimization process the problems with common artificial neural network algorithm are unsure convergence, insufficient post-training network precision, and slow training speed, in which partial minimum value question tends to occur. This paper conducted an in-depth study on the causes of the limitations of the algorithm, presented a rapid artificial neural network algorithm, which is characterized by integrating multiple algorithms and by using their complementary advantages. The salient feature of the method is self-organization, which can effectively prevent the optimized results from tending to be partial minimum values. Overall optimization can be achieved with this method, goal function can be searched for in overall scope. With optimization control of coal mine ventilator as a practical application, the paper proves that by integrating multiple artificial neural network algorithms, best control optimization and goal optimized can be achieved.

  4. Canalization and control in automata networks: body segmentation in Drosophila melanogaster.

    Directory of Open Access Journals (Sweden)

    Manuel Marques-Pita

    Full Text Available We present schema redescription as a methodology to characterize canalization in automata networks used to model biochemical regulation and signalling. In our formulation, canalization becomes synonymous with redundancy present in the logic of automata. This results in straightforward measures to quantify canalization in an automaton (micro-level, which is in turn integrated into a highly scalable framework to characterize the collective dynamics of large-scale automata networks (macro-level. This way, our approach provides a method to link micro- to macro-level dynamics--a crux of complexity. Several new results ensue from this methodology: uncovering of dynamical modularity (modules in the dynamics rather than in the structure of networks, identification of minimal conditions and critical nodes to control the convergence to attractors, simulation of dynamical behaviour from incomplete information about initial conditions, and measures of macro-level canalization and robustness to perturbations. We exemplify our methodology with a well-known model of the intra- and inter cellular genetic regulation of body segmentation in Drosophila melanogaster. We use this model to show that our analysis does not contradict any previous findings. But we also obtain new knowledge about its behaviour: a better understanding of the size of its wild-type attractor basin (larger than previously thought, the identification of novel minimal conditions and critical nodes that control wild-type behaviour, and the resilience of these to stochastic interventions. Our methodology is applicable to any complex network that can be modelled using automata, but we focus on biochemical regulation and signalling, towards a better understanding of the (decentralized control that orchestrates cellular activity--with the ultimate goal of explaining how do cells and tissues 'compute'.

  5. Application of artificial neural networks in particle physics

    International Nuclear Information System (INIS)

    The application of artificial neural networks in particle physics is reviewed. The use of feed-forward nets is most common for event classification and function approximation. This network type is best suited for a hardware implementation and special VLSI chips are available which are used in fast trigger processors. Also discussed are fully connected networks of the Hopfield type for pattern recognition in tracking detectors. (orig.)

  6. Application of artificial neural networks in particle physics

    International Nuclear Information System (INIS)

    The application of Artificial Neural Networks in Particle Physics is reviewed. Most common is the use of feed-forward nets for event classification and function approximation. This network type is best suited for a hardware implementation and special VLSI chips are available which are used in fast trigger processors. Also discussed are fully connected networks of the Hopfield type for pattern recognition in tracking detectors. (orig.)

  7. Neurons vs Weights Pruning in Artificial Neural Networks

    OpenAIRE

    Bondarenko, Andrey; Borisov, Arkady; Alekseeva, Ludmila

    2015-01-01

    Artificial neural networks (ANN) are well known for their good classification abilities. Recent advances in deep learning imposed second ANN renaissance. But neural networks possesses some problems like choosing hyper parameters such as neuron layers count and sizes which can greatly influence classification rate. Thus pruning techniques were developed that can reduce network sizes, increase its generalization abilities and overcome overfitting. Pruning approaches, in contrast to growing neur...

  8. PREDICTION OF LEAF SPRING PARAMETERS USING ARTIFICIAL NEURAL NETWORKS

    OpenAIRE

    Dr.D.V.V.KRISHNA PRASAD; J.P.KARTHIK

    2013-01-01

    In this paper an attempt is made to predict the optimum design parameters using artificial neural networks. For this static and dynamic analysis on various leaf spring configuration is carried out by ANSYS and is used as training data for neural network. Training data includes cross section of the leaf, load on the leaf spring, stresses, displacement and natural frequencies. By creating a network using thickness and width of the leaf, load on the leaf spring as input parameters and stresses, ...

  9. Improved Local Weather Forecasts Using Artificial Neural Networks

    DEFF Research Database (Denmark)

    Wollsen, Morten Gill; Jørgensen, Bo Nørregaard

    2015-01-01

    using an artificial neural network. The neural network used is a NARX network, which is known to model non-linear systems well. The predictions are compared to both a design reference year as well as commercial weather forecasts based upon numerical modelling. The results presented in this paper show...... that the network outperforms the commercial forecast for lower step aheads (network’s performance is in the range of the commercial forecast. However, the neural network approach is fast, fairly precise and allows for further expansion with higher resolution....

  10. Quantitative Structure Pharmacokinetic Relationship Using Artificial Neural Network: A Review

    Directory of Open Access Journals (Sweden)

    S. K. Singh

    2009-10-01

    Full Text Available Quantitative structure activity relationship (QSAR has become a tool for designing in various areas like drugs, food additive, Pesticides, biochemical reactant, environmental pollutant and toxic products. In QSAR biological activity can be related with physicochemical properties and in QSPkR (Quantitative Structure Pharmacokinetic Relationship, pharmacokinetic properties can be related with physicochemical properties, relation found in terms of quantity. A number of literature and review article have been published on Quantitative structure pharmacokinetic relationship. But prediction of human pharmacokinetic properties of known and unknown is much difficult job in pharmaceutical industry. Pharmacokinetic data of animal cannot be put straightforward. Artificial neural network (ANN is used to predict the pharmacokinetic properties. Artificial neural network has basic structure like biological brain and compose of neurons which are interconnected to each other. The present review not only compiles the literature of QSPkR using ANN, but gives detail about the physicochemical properties and artificial neural network.

  11. Artificial neural network based approach to transmission lines protection

    International Nuclear Information System (INIS)

    The aim of this paper is to present and accurate fault detection technique for high speed distance protection using artificial neural networks. The feed-forward multi-layer neural network with the use of supervised learning and the common training rule of error back-propagation is chosen for this study. Information available locally at the relay point is passed to a neural network in order for an assessment of the fault location to be made. However in practice there is a large amount of information available, and a feature extraction process is required to reduce the dimensionality of the pattern vectors, whilst retaining important information that distinguishes the fault point. The choice of features is critical to the performance of the neural networks learning and operation. A significant feature in this paper is that an artificial neural network has been designed and tested to enhance the precision of the adaptive capabilities for distance protection

  12. Artificial Neural Network in Harmonic Reduction of STATCOM

    Institute of Scientific and Technical Information of China (English)

    Li Hongmei; Li Zhenran; Zheng Peiying

    2005-01-01

    To eliminate harmonic pollution incurred from the static synchronous compensator(STATCOM), a method of applying artificial neural network is presented. When PWM wave is formed based on the harmonic suppression theory, a concave is set on certain angle of the square wave to suppress unnecessary harmonics, by timely and on-line determining the chopping angle corresponding to respective harmonics through artificial neural network, i.e. by setting the position of concave to eliminate corresponding harmonics, the harmonic component on output voltage of the inverter can be improved. To conclude through computer simulation test, the perfect control effect has been proved.

  13. Adaptive Neurons For Artificial Neural Networks

    Science.gov (United States)

    Tawel, Raoul

    1990-01-01

    Training time decreases dramatically. In improved mathematical model of neural-network processor, temperature of neurons (in addition to connection strengths, also called weights, of synapses) varied during supervised-learning phase of operation according to mathematical formalism and not heuristic rule. Evidence that biological neural networks also process information at neuronal level.

  14. Mandarin Chinese Tone Recognition with an Artificial Neural Network

    Institute of Scientific and Technical Information of China (English)

    XU Li; ZHANG Wenle; ZHOU Ning; LEE Chaoyang; LI Yongxin; CHEN Xiuwu; ZHAO Xiaoyan

    2006-01-01

    Mandarin Chinese tone patterns vary in one of the four ways, i.e, (1) high level; (2) rising; (3) low falling and rising; and (4) high falling. The present study is to examine the efficacy of an artificial neural network in recognizing these tone patterns. Speech data were recorded from 12 children (3-6 years of age) and 15 adults. All subjects were native Mandarin Chinese speakers. The fundamental frequencies (FO) of each monosyllabic word of the speech data were extracted with an autocorrelation method. The pitch data(i.e., the FO contours) were the inputs to a feed-forward backpropagation artificial neural network. The number of inputs to the neural network varied from 1 to 16 and the hidden layer of the network contained neurons that varied from 1 to 16 in number. The output of the network consisted of four neurons representing the four tone patterns of Mandarin Chinese. After being trained with the Levenberg-Marquardt optimization, the neural network was able to successfully classify the tone patterns with an accuracy of about 90% correct for speech samples from both adults and children. The artificial neural network may provide an objective and effective way of assessing tone production in prelingually-deafened children who have received cochlear implants.

  15. Term Structure of Interest Rates Based on Artificial Neural Network

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    In light of the nonlinear approaching capability of artificial neural networks ( ANN), the term structure of interest rates is predicted using The generalized regression neural network (GRNN) and back propagation (BP) neural networks models. The prediction performance is measured with US interest rate data. Then, RBF and BP models are compared with Vasicek's model and Cox-Ingersoll-Ross (CIR) model. The comparison reveals that neural network models outperform Vasicek's model and CIR model,which are more precise and closer to the real market situation.

  16. Numerical solution of differential equations by artificial neural networks

    Science.gov (United States)

    Meade, Andrew J., Jr.

    1995-01-01

    Conventionally programmed digital computers can process numbers with great speed and precision, but do not easily recognize patterns or imprecise or contradictory data. Instead of being programmed in the conventional sense, artificial neural networks (ANN's) are capable of self-learning through exposure to repeated examples. However, the training of an ANN can be a time consuming and unpredictable process. A general method is being developed by the author to mate the adaptability of the ANN with the speed and precision of the digital computer. This method has been successful in building feedforward networks that can approximate functions and their partial derivatives from examples in a single iteration. The general method also allows the formation of feedforward networks that can approximate the solution to nonlinear ordinary and partial differential equations to desired accuracy without the need of examples. It is believed that continued research will produce artificial neural networks that can be used with confidence in practical scientific computing and engineering applications.

  17. Techniques of Image Processing Based on Artificial Neural Networks

    Institute of Scientific and Technical Information of China (English)

    LI Wei-qing; WANG Qun; WANG Cheng-biao

    2006-01-01

    This paper presented an online quality inspection system based on artificial neural networks. Chromatism classification and edge detection are two difficult problems in glass steel surface quality inspection. Two artificial neural networks were made and the two problems were solved. The one solved chromatism classification. Hue,saturation and their probability of three colors, whose appearing probabilities were maximum in color histogram,were selected as input parameters, and the number of output node could be adjusted with the change of requirement. The other solved edge detection. In this neutral network, edge detection of gray scale image was able to be tested with trained neural networks for a binary image. It prevent the difficulty that the number of needed training samples was too large if gray scale images were directly regarded as training samples. This system is able to be applied to not only glass steel fault inspection but also other product online quality inspection and classification.

  18. Artificial neural networks technology for neutron spectrometry and dosimetry

    International Nuclear Information System (INIS)

    Artificial Neural Network Technology has been applied to unfold neutron spectra and to calculate 13 dosimetric quantities using seven count rates from a Bonner Sphere Spectrometer with a 6LiI(Eu). Two different networks, one for spectrometry and another for dosimetry, were designed. To train and test both networks, 177 neutron spectra from the IAEA compilation were utilised. Spectra were re-binned into 31 energy groups, and the dosimetric quantities were calculated using the MCNP code and the fluence-to-dose conversion coefficients from ICRP 74. Neutron spectra and UTA4 response matrix were used to calculate the expected count rates in the Bonner spectrometer. Spectra and H*(10) of 239PuBe and 241AmBe were experimentally obtained and compared with those determined with the artificial neural networks. (authors)

  19. Hydrography - HYDROGRAPHY_HIGHRES_FLOWLINE_NHD_USGS: Streams, Rivers, Canals, Ditches, Artificial Paths, Coastlines, Connectors, and Pipelines in Watersheds of Indiana (U. S. Geological Survey, 1:24,000, Line Shapefile)

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — HYDROGRAPHY_HIGHRES_FLOWLINE_NHD_USGS.SHP is a line shapefile that contains streams, rivers, canals, ditches, artificial paths, coastlines, connectors and pipelines...

  20. Hydrography - HYDROGRAPHY_LOCALRES_FLOWLINE_NHD_IN: Streams, Rivers, Canals, Ditches, Artificial Paths, Coastlines, Connectors, and Pipelines in Watersheds of Indiana (U. S. Geological Survey, 1:2,400, Line Shapefile)

    Data.gov (United States)

    NSGIC GIS Inventory (aka Ramona) — HYDROGRAPHY_LOCALRES_FLOWLINE_NHD_IN.SHP is a line shapefile that contains streams, rivers, canals, ditches, artificial paths, coastlines, connectors and pipelines...

  1. Time series prediction using artificial neural network for power stabilization

    International Nuclear Information System (INIS)

    Time series prediction has been applied to many business and scientific applications. Prominent among them are stock market prediction, weather forecasting, etc. Here, this technique has been applied to forecast plasma torch voltages to stabilize power using a backpropagation, a model of artificial neural network. The Extended-Delta-Bar-Delta algorithm is used to improve the convergence rate of the network and also to avoid local minima. Results from off-line data was quite promising to use in on-line

  2. AUTOMATED DEFECT CLASSIFICATION USING AN ARTIFICIAL NEURAL NETWORK

    International Nuclear Information System (INIS)

    The automated defect classification algorithm based on artificial neural network with multilayer backpropagation structure was utilized. The selected features of flaws were used as input data. In order to train the neural network it is necessary to prepare learning data which is representative database of defects. Database preparation requires the following steps: image acquisition and pre-processing, image enhancement, defect detection and feature extraction. The real digital radiographs of welded parts of a ship were used for this purpose.

  3. Modeling of Relative Humidity Using Artificial Neural Network

    OpenAIRE

    Samer AlSadi; Tamer Khatib

    2012-01-01

    This paper presents a relative humidity predictions using feedforward artificial neural network (FFNN). Relative humidity values obtained from weather records for Malaysia are used in training the FFNNs. The prediction of the relative humidity is in terms of Sun shine ration and cloud cover. However, three statistical parameters, namely, mean absolute percentage error, MAPE, mean bias error, MBE, and root mean square error, RMSE are used to evaluate the neural networks. Based on results, the ...

  4. Application of Artificial Neural Network in Indicator Diagram

    Institute of Scientific and Technical Information of China (English)

    WuXiaodong; JiangHua; HanGuoqing

    2004-01-01

    Indicator diagram plays an important role in identifying the production state of oil wells. With an ability to reflect any non-linear mapping relationship, the artificial neural network (ANN) can be used in shape identification. This paper illuminates ANN realization in identifying fault kinds of indicator diagrams, including a back-propagation algorithm, characteristics of the indicator diagram and some examples. It is concluded that the buildup of a neural network and the abstract of indicator diagrams are important to successful application.

  5. Research on Artificial Neural Network Method for Credit Application

    Institute of Scientific and Technical Information of China (English)

    MingxingLi; PingHeng; PeiwuDong

    2004-01-01

    Considering our country's present situation, in this paper we provide ten evaluation indexes of the credit application management, which is used as the input vector of neural network. Then we set up a three-layer back propagation model for the credit application evaluation based on the artificial neural network. We also analyzed the model using the real data; the testing result indicates that the model is a good method and a good tool.

  6. Evolving Spiking Neural Networks for Control of Artificial Creatures

    Directory of Open Access Journals (Sweden)

    Arash Ahmadi

    2013-10-01

    Full Text Available To understand and analysis behavior of complicated and intelligent organisms, scientists apply bio-inspired concepts including evolution and learning to mathematical models and analyses. Researchers utilize these perceptions in different applications, searching for improved methods andapproaches for modern computational systems. This paper presents a genetic algorithm based evolution framework in which Spiking Neural Network (SNN of artificial creatures are evolved for higher chance of survival in a virtual environment. The artificial creatures are composed ofrandomly connected Izhikevich spiking reservoir neural networks using population activity rate coding. Inspired by biological neurons, the neuronal connections are considered with different axonal conduction delays. Simulations results prove that the evolutionary algorithm has thecapability to find or synthesis artificial creatures which can survive in the environment successfully.

  7. Development and Evolution of Neural Networks in an Artificial Chemistry

    CERN Document Server

    Astor, J C; Astor, Jens C.; Adami, Christoph

    1998-01-01

    We present a model of decentralized growth for Artificial Neural Networks (ANNs) inspired by the development and the physiology of real nervous systems. In this model, each individual artificial neuron is an autonomous unit whose behavior is determined only by the genetic information it harbors and local concentrations of substrates modeled by a simple artificial chemistry. Gene expression is manifested as axon and dendrite growth, cell division and differentiation, substrate production and cell stimulation. We demonstrate the model's power with a hand-written genome that leads to the growth of a simple network which performs classical conditioning. To evolve more complex structures, we implemented a platform-independent, asynchronous, distributed Genetic Algorithm (GA) that allows users to participate in evolutionary experiments via the World Wide Web.

  8. Applications of network analysis for adaptive management of artificial drainage systems in landscapes vulnerable to sea level rise

    Science.gov (United States)

    Poulter, Benjamin; Goodall, Jonathan L.; Halpin, Patrick N.

    2008-08-01

    SummaryThe vulnerability of coastal landscapes to sea level rise is compounded by the existence of extensive artificial drainage networks initially built to lower water tables for agriculture, forestry, and human settlements. These drainage networks are found in landscapes with little topographic relief where channel flow is characterized by bi-directional movement across multiple time-scales and related to precipitation, wind, and tidal patterns. The current configuration of many artificial drainage networks exacerbates impacts associated with sea level rise such as salt-intrusion and increased flooding. This suggests that in the short-term, drainage networks might be managed to mitigate sea level rise related impacts. The challenge, however, is that hydrologic processes in regions where channel flow direction is weakly related to slope and topography require extensive parameterization for numerical models which is limited where network size is on the order of a hundred or more kilometers in total length. Here we present an application of graph theoretic algorithms to efficiently investigate network properties relevant to the management of a large artificial drainage system in coastal North Carolina, USA. We created a digital network model representing the observation network topology and four types of drainage features (canal, collector and field ditches, and streams). We applied betweenness-centrality concepts (using Dijkstra's shortest path algorithm) to determine major hydrologic flowpaths based off of hydraulic resistance. Following this, we identified sub-networks that could be managed independently using a community structure and modularity approach. Lastly, a betweenness-centrality algorithm was applied to identify major shoreline entry points to the network that disproportionately control water movement in and out of the network. We demonstrate that graph theory can be applied to solving management and monitoring problems associated with sea level rise

  9. Recurrent Artificial Neural Networks and Finite State Natural Language Processing.

    Science.gov (United States)

    Moisl, Hermann

    It is argued that pessimistic assessments of the adequacy of artificial neural networks (ANNs) for natural language processing (NLP) on the grounds that they have a finite state architecture are unjustified, and that their adequacy in this regard is an empirical issue. First, arguments that counter standard objections to finite state NLP on the…

  10. A Neuron- and a Synapse Chip for Artificial Neural Networks

    DEFF Research Database (Denmark)

    Lansner, John; Lehmann, Torsten

    1992-01-01

    A cascadable, analog, CMOS chip set has been developed for hardware implementations of artificial neural networks (ANN's):I) a neuron chip containing an array of neurons with hyperbolic tangent activation functions and adjustable gains, and II) a synapse chip (or a matrix-vector multiplier) where...

  11. Artificial neural networks as a tool in urban storm drainage

    DEFF Research Database (Denmark)

    Loke, E.; Warnaars, E.A.; Jacobsen, P.

    1997-01-01

    The introduction of Artificial Neural Networks (ANNs) as a tool in the field of urban storm drainage is discussed. Besides some basic theory on the mechanics of ANNs and a general classification of the different types of ANNs, two ANN application examples are presented: The prediction of runoff...

  12. Introducing Artificial Neural Networks through a Spreadsheet Model

    Science.gov (United States)

    Rienzo, Thomas F.; Athappilly, Kuriakose K.

    2012-01-01

    Business students taking data mining classes are often introduced to artificial neural networks (ANN) through point and click navigation exercises in application software. Even if correct outcomes are obtained, students frequently do not obtain a thorough understanding of ANN processes. This spreadsheet model was created to illuminate the roles of…

  13. Optimal control learning with artificial neural networks

    International Nuclear Information System (INIS)

    This paper shows neural networks capabilities in optimal control applications of non linear dynamic systems. Our method is issued of a classical method concerning the direct research of the optimal control using gradient techniques. We show that neural approach and backpropagation paradigm are able to solve efficiently equations relative to necessary conditions for an optimizing solution. We have taken into account the known capabilities of multi layered networks in approximation functions. And for dynamic systems, we have generalized the indirect learning of inverse model adaptive architecture that is capable to define an optimal control in relation to a temporal criterion. (orig.)

  14. Practical application of artificial neural networks in the neurosciences

    Science.gov (United States)

    Pinti, Antonio

    1995-04-01

    This article presents a practical application of artificial multi-layer perceptron (MLP) neural networks in neurosciences. The data that are processed are labeled data from the visual analysis of electrical signals of human sleep. The objective of this work is to automatically classify into sleep stages the electrophysiological signals recorded from electrodes placed on a sleeping patient. Two large data bases were designed by experts in order to realize this study. One data base was used to train the network and the other to test its generalization capacity. The classification results obtained with the MLP network were compared to a type K nearest neighbor Knn non-parametric classification method. The MLP network gave a better result in terms of classification than the Knn method. Both classification techniques were implemented on a transputer system. With both networks in their final configuration, the MLP network was 160 times faster than the Knn model in classifying a sleep period.

  15. Morphological Classification of Galaxies Using Artificial Neural Networks

    CERN Document Server

    Ball, N M

    2001-01-01

    The results of morphological galaxy classifications performed by humans and by automated methods are compared. In particular, a comparison is made between the eyeball classifications of 454 galaxies in the Sloan Digital Sky Survey (SDSS) commissioning data (Shimasaku et al. 2001) with those of supervised artificial neural network programs constructed using the MATLAB Neural Network Toolbox package. Networks in this package have not previously been used for galaxy classification. It is found that simple neural networks are able to improve on the results of linear classifiers, giving correlation coefficients of the order of 0.8 +/- 0.1, compared with those of around 0.7 +/- 0.1 for linear classifiers. The networks are trained using the resilient backpropagation algorithm, which, to the author's knowledge, has not been specifically used in the galaxy classification literature. The galaxy parameters used and the network architecture are both important, and in particular the galaxy concentration index, a measure o...

  16. Artificial neural networks in the nuclear engineering (Part 2)

    International Nuclear Information System (INIS)

    The field of Artificial Neural Networks (ANN), one of the branches of Artificial Intelligence has been waking up a lot of interest in the Nuclear Engineering (NE). ANN can be used to solve problems of difficult modeling, when the data are fail or incomplete and in high complexity problems of control. The first part of this work began a discussion with feed-forward neural networks in back-propagation. In this part of the work, the Multi-synaptic neural networks is applied to control problems. Also, the self-organized maps is presented in a typical pattern classification problem: transients classification. The main purpose of the work is to show that ANN can be successfully used in NE if a carefully choice of its type is done: the application sets this choice. (author)

  17. Transient stability Assessment using Artificial Neural Network Considering Fault Location

    Directory of Open Access Journals (Sweden)

    P.K.Olulope

    2010-06-01

    Full Text Available This paper describes the capability of artificial neural network for predicting the critical clearing time of power system. It combines the advantages of time domain integration schemes with artificial neural network for real time transient stability assessment. The training of ANN is done using selected features as input and critical fault clearing time (CCT as desire target. A single contingency was applied and the target CCT was found using time domain simulation. Multi layer feed forward neural network trained with Levenberg Marquardt (LM back propagation algorithm is used to provide the estimated CCT. The effectiveness of ANN, the method is demonstrated on single machine infinite bus system (SMIB. The simulation shows that ANN can provide fast and accurate mapping which makes it applicable to real time scenario.

  18. Probabilistic Wind Power Forecasting with Hybrid Artificial Neural Networks

    DEFF Research Database (Denmark)

    Wan, Can; Song, Yonghua; Xu, Zhao;

    2016-01-01

    probabilities of prediction errors provide an alternative yet effective solution. This article proposes a hybrid artificial neural network approach to generate prediction intervals of wind power. An extreme learning machine is applied to conduct point prediction of wind power and estimate model uncertainties...... actual wind power data from Denmark. The numerical results demonstrate that the proposed hybrid artificial neural network approach is effective and efficient for probabilistic forecasting of wind power and has high potential in practical applications....... via a bootstrap technique. Subsequently, the maximum likelihood estimation method is employed to construct a distinct neural network to estimate the noise variance of forecasting results. The proposed approach has been tested on multi-step forecasting of high-resolution (10-min) wind power using...

  19. Artificial Neural Network Model for Predicting Compressive

    Directory of Open Access Journals (Sweden)

    Salim T. Yousif

    2013-05-01

    Full Text Available   Compressive strength of concrete is a commonly used criterion in evaluating concrete. Although testing of the compressive strength of concrete specimens is done routinely, it is performed on the 28th day after concrete placement. Therefore, strength estimation of concrete at early time is highly desirable. This study presents the effort in applying neural network-based system identification techniques to predict the compressive strength of concrete based on concrete mix proportions, maximum aggregate size (MAS, and slump of fresh concrete. Back-propagation neural networks model is successively developed, trained, and tested using actual data sets of concrete mix proportions gathered from literature.    The test of the model by un-used data within the range of input parameters shows that the maximum absolute error for model is about 20% and 88% of the output results has absolute errors less than 10%. The parametric study shows that water/cement ratio (w/c is the most significant factor  affecting the output of the model.     The results showed that neural networks has strong potential as a feasible tool for predicting compressive strength of concrete.

  20. A Novel Evolutionary Feedforward Neural Network with Artificial Immunology

    Institute of Scientific and Technical Information of China (English)

    宫新保; 臧小刚; 周希朗

    2003-01-01

    A hybrid algorithm to design the multi-layer feedforward neural network was proposed. Evolutionaryprogramming is used to design the network that makes the training process tending to global optima. Artificial im-munology combined with simulated annealing algorithm is used to specify the initial weight vectors, therefore improves the probabiligy of training algorithm to converge to global optima. The applications of the neural networkin the modulation-style recognition of analog modulated rader signals demonstrate the good performance of the net-work.

  1. Artificial Neural Network Model for Optical Fiber Direction Coupler Design

    Institute of Scientific and Technical Information of China (English)

    李九生; 鲍振武

    2004-01-01

    A new approach to the design of the optical fiber direction coupler by using neural network is proposed. To train the artificial neural network,the coupling length is defined as the input sample, and the coupling ratio is defined as the output sample. Compared with the numerical value calculation of the theoretical formula, the error of the neural network model output is 1% less.Then, through the model, to design a broadband or a single wavelength optical fiber direction coupler becomes easy. The method is proved to be reliable, accurate and time-saving. So it is promising in the field of both investigation and application.

  2. Applications of artificial neural network chips

    International Nuclear Information System (INIS)

    In a collaboration between CERN and Royal Institute of Technology Stockholm a so called Asynchronous Transfer Mode (ATM) test setup was developed. The main goal of the task was the experimental verification of the harware design principles and methods, partly the application of the test setup for testing the neural network controlled self-routing, asynchronous event-building ATM networks. We took part in the first implementation of the IBM Zero Instruction Set Computer (ZISC036)[2] on a PC-486 ISA-bus card. This chip has been designed for cost-effective recognition and classification in real time. After building the PC interface card and testing the main functions of the built-in logic a code for character recognition was developed for comparing its performance to other RBF-type methods. The results show that the ZISC036 is performing quite well. The most attractive feature of the chip is the speed: if it is operated at 20 MHz, 64 component the evaluation is ready in 0.5 μ sec. (K.A.) 2 refs.; 1 fig

  3. Study on the fitting ways of artificial neural networks

    Institute of Scientific and Technical Information of China (English)

    SHAO Liang-shan; WANG Jun; SUN Shao-guang

    2008-01-01

    Function simulation, which is called virtual reality too, is popularly applied to solve uncertain problems. Good performance of hidden layers and perfect capability of function simulation make artificial neural networks one of the best choices to simulate functions with form unknown. Inputs and outputs were used to train the structure of the artificial neural network to make the outputs of network vary with the given inputs and keep consistent with the original data within tolerance. However, we couldn't get expected results by using samples of a simple two-variable-model for the cause of dimensional difference. The way of artificial neural networks to fit functions, which uses "multi-dimensional surface" of high dimension to fit "multi-dimensional line" of low dimension, was proved; the conclusion that good effects of fitting don't mean good function modeling when a dimensional difference exists was provided, and a suggestion of "surface collecting" in practical engineering application was proposed when collecting useful data.

  4. On the design of artificial auto-associative neuronal networks.

    Science.gov (United States)

    Reimann, Stefan

    1998-06-01

    In this paper, we consider the problem of how to construct an artificial neuronal network such that it reproduces a given set of patterns in an exact manner. It turns out that the structure of the weight matrix of the network represents the structure of the set of patterns it is acting on, not the patterns themselves. Conditions are discussed under which the associative network memorizes a certain subset of these patterns. Our formal approach is based on the simple observation that neural networks are structured sets of neurons. By regarding recurrent neural networks as dynamical systems with symmetry, the category of G-sets and G-morphisms appears as a natural framework for evaluating their structure and functioning analytically.

  5. Metanetworks of artificially evolved regulatory networks

    CERN Document Server

    Danacı, Burçin

    2014-01-01

    We study metanetworks arising in genotype and phenotype spaces, in the context of a model population of Boolean graphs evolved under selection for short dynamical attractors. We define the adjacency matrix of a graph as its genotype, which gets mutated in the course of evolution, while its phenotype is its set of dynamical attractors. Metanetworks in the genotype and phenotype spaces are formed, respectively, by genetic proximity and by phenotypic similarity, the latter weighted by the sizes of the basins of attraction of the shared attractors. We find that populations of evolved networks form giant clusters in genotype space, have Poissonian degree distributions but exhibit hierarchically organized $k$-core decompositions, while random populations of Boolean graphs are typically so far removed from each other genetically that they cannot form a metanetwork. In phenotype space, the metanetworks of evolved populations are super robust both under the elimination of weak connections and random removal of nodes. ...

  6. Echo state networks as an alternative to traditional artificial neural networks in rainfall–runoff modelling

    Directory of Open Access Journals (Sweden)

    N. J. de Vos

    2013-01-01

    Full Text Available Despite theoretical benefits of recurrent artificial neural networks over their feedforward counterparts, it is still unclear whether the former offer practical advantages as rainfall–runoff models. The main drawback of recurrent networks is the increased complexity of the training procedure due to their architecture. This work uses the recently introduced and conceptually simple echo state networks for streamflow forecasts on twelve river basins in the Eastern United States, and compares them to a variety of traditional feedforward and recurrent approaches. Two modifications on the echo state network models are made that increase the hydrologically relevant information content of their internal state. The results show that the echo state networks outperform feedforward networks and are competitive with state-of-the-art recurrent networks, across a range of performance measures. This, along with their simplicity and ease of training, suggests that they can be considered promising alternatives to traditional artificial neural networks in rainfall–runoff modelling.

  7. Echo state networks as an alternative to traditional artificial neural networks in rainfall-runoff modelling

    Science.gov (United States)

    de Vos, N. J.

    2013-01-01

    Despite theoretical benefits of recurrent artificial neural networks over their feedforward counterparts, it is still unclear whether the former offer practical advantages as rainfall-runoff models. The main drawback of recurrent networks is the increased complexity of the training procedure due to their architecture. This work uses the recently introduced and conceptually simple echo state networks for streamflow forecasts on twelve river basins in the Eastern United States, and compares them to a variety of traditional feedforward and recurrent approaches. Two modifications on the echo state network models are made that increase the hydrologically relevant information content of their internal state. The results show that the echo state networks outperform feedforward networks and are competitive with state-of-the-art recurrent networks, across a range of performance measures. This, along with their simplicity and ease of training, suggests that they can be considered promising alternatives to traditional artificial neural networks in rainfall-runoff modelling.

  8. DESIGN AND ANALOG VLSI IMPLEMENTATION OF ARTIFICIAL NEURAL NETWORK

    Directory of Open Access Journals (Sweden)

    D.Yammenavar

    2011-08-01

    Full Text Available Nature has evolved highly advanced systems capable of performing complex computations, adoption andlearning using analog computations. Furthermore nature has evolved techniques to deal with impreciseanalog computations by using redundancy and massive connectivity. In this paper we are making use ofArtificial Neural Network to demonstrate the way in which the biological system processes in analogdomain.We are using 180nm CMOS VLSI technology for implementing circuits which performs arithmeticoperations and for implementing Neural Network. The arithmetic circuits presented here are based onMOS transistors operating in subthreshold region. The basic blocks of artificial neuron are multiplier,adder and neuron activation function.The functionality of designed neural network is verified for analog operations like signal amplificationand frequency multiplication. The network designed can be adopted for digital operations like AND, ORand NOT. The network realizes its functionality for the trained targets which is verified using simulationresults. The schematic, Layout design and verification of proposed Neural Network is carried out usingCadence Virtuoso tool.

  9. Forecast Share Prices with Artificial Neural Network in Crisis Periods

    Directory of Open Access Journals (Sweden)

    Feyyaz Zeren

    2014-09-01

    Full Text Available Crisis periods present quite a significant moment for financial markets. Considering not losing and changing the crisis periods into opportunities, forecasts of share prices during these periods have an importance for the investors. In this study, daily closing prices of Borsa Istanbul National 100 index during the three big crisis periods, as 1994, 2001, and 2008, have been tried to be forecasted, by using artificial neural networks. As a result of this study, it is determined that in the forecasts of Borsa Istanbul, artificial neural networks show high performance. This result was proved by both comparing the values that occurred and forecasted on the graphics, and Mean Absolute Percentage Error (MAPE calculations

  10. Prediction of Concrete Compressive Strength by Evolutionary Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Mehdi Nikoo

    2015-01-01

    Full Text Available Compressive strength of concrete has been predicted using evolutionary artificial neural networks (EANNs as a combination of artificial neural network (ANN and evolutionary search procedures, such as genetic algorithms (GA. In this paper for purpose of constructing models samples of cylindrical concrete parts with different characteristics have been used with 173 experimental data patterns. Water-cement ratio, maximum sand size, amount of gravel, cement, 3/4 sand, 3/8 sand, and coefficient of soft sand parameters were considered as inputs; and using the ANN models, the compressive strength of concrete is calculated. Moreover, using GA, the number of layers and nodes and weights are optimized in ANN models. In order to evaluate the accuracy of the model, the optimized ANN model is compared with the multiple linear regression (MLR model. The results of simulation verify that the recommended ANN model enjoys more flexibility, capability, and accuracy in predicting the compressive strength of concrete.

  11. Using Artificial Neural Networks To Forecast Financial Time Series

    OpenAIRE

    Aamodt, Rune

    2010-01-01

    This thesis investigates the application of artificial neural networks (ANNs) for forecasting financial time series (e.g. stock prices).The theory of technical analysis dictates that there are repeating patterns that occur in the historic prices of stocks, and that identifying these patterns can be of help in forecasting future price developments. A system was therefore developed which contains several ``agents'', each producing recommendations on the stock price based on some aspect of techn...

  12. Artificial neural networks : applications in morphometric and landscape features analysis

    OpenAIRE

    Ehsani, Amir Houshang

    2007-01-01

    In this thesis a semi-automatic method is developed to analyze morphometric features and landscape elements based on Self Organizing Map (SOM) as a unsupervised Artificial Neural Network algorithm. Analysis and parameterization of topography into simple and homogenous land elements (landform) can play an important role as basic information in planning processes and environmental modeling. Landforms and land cover are the main components of landscapes. Landscapes are dynamic systems that invol...

  13. Hierarchical Artificial Bee Colony Algorithm for RFID Network Planning Optimization

    OpenAIRE

    Lianbo Ma; Hanning Chen; Kunyuan Hu; Yunlong Zhu

    2014-01-01

    This paper presents a novel optimization algorithm, namely, hierarchical artificial bee colony optimization, called HABC, to tackle the radio frequency identification network planning (RNP) problem. In the proposed multilevel model, the higher-level species can be aggregated by the subpopulations from lower level. In the bottom level, each subpopulation employing the canonical ABC method searches the part-dimensional optimum in parallel, which can be constructed into a complete solution for t...

  14. Artificial Neural Networks in Applications of Industrial Robots

    Institute of Scientific and Technical Information of China (English)

    王克胜; JonathanLienhardt; 袁庆丰; 方明伦

    2004-01-01

    Artificial neural networks (ANNs) have been widely used to solve a number of problems to which analytical solutions are difficult to obtain using traditional mathematical approaches.Such problems exist also in the analysis of industrial robots. This paper presents an overview of ANN applications to robot kinematics, dynamics,control, trajectory and path planning, and sensing. Reasons for using or not using ANNs to industrial robots are explained as well.

  15. Evolving Spiking Neural Networks for Control of Artificial Creatures

    OpenAIRE

    Arash Ahmadi

    2013-01-01

    To understand and analysis behavior of complicated and intelligent organisms, scientists apply bio-inspired concepts including evolution and learning to mathematical models and analyses. Researchers utilize these perceptions in different applications, searching for improved methods andapproaches for modern computational systems. This paper presents a genetic algorithm based evolution framework in which Spiking Neural Network (SNN) of artificial creatures are evolved for higher chance of survi...

  16. Activated sludge process based on artificial neural network

    Institute of Scientific and Technical Information of China (English)

    张文艺; 蔡建安

    2002-01-01

    Considering the difficulty of creating water quality model for activated sludge system, a typical BP artificial neural network model has been established to simulate the operation of a waste water treatment facilities. The comparison of prediction results with the on-spot measurements shows the model, the model is accurate and this model can also be used to realize intelligentized on-line control of the wastewater processing process.

  17. Application of Artificial Neural Networks for Predicting Generated Wind Power

    OpenAIRE

    Vijendra Singh

    2016-01-01

    This paper addresses design and development of an artificial neural network based system for prediction of wind energy produced by wind turbines. Now in the last decade, renewable energy emerged as an additional alternative source for electrical power generation. We need to assess wind power generation capacity by wind turbines because of its non-exhaustible nature. The power generation by electric wind turbines depends on the speed of wind, flow direction, fluctuations, density of air, gener...

  18. RECOGNITION OF CDNA MICROARRAY IMAGE USING FEEDFORWARD ARTIFICIAL NEURAL NETWORK

    Directory of Open Access Journals (Sweden)

    R. M. Farouk

    2014-09-01

    Full Text Available The complementary DNA (cDNA sequence considered the magic biometric technique for personal identification. Microarray image processing used for the concurrent genes identification. In this paper, we present a new method for cDNA recognition based on the artificial neural network (ANN. We have segmented the location of the spots in a cDNA microarray. Thus, a precise localization and segmenting of a spot are essential to obtain a more exact intensity measurement, leading to a more accurate gene expression measurement. The segmented cDNA microarray image resized and used as an input for the proposed artificial neural network. For matching and recognition, we have trained the artificial neural network. Recognition results are given for the galleries of cDNA sequences . The numerical results show that, the proposed matching technique is an effective in the cDNA sequences process. The experimental results of our matching approach using different databases shows that, the proposed technique is an effective matching performance.

  19. RECOGNITION OF CDNA MICROARRAY IMAGE USING FEEDFORWARD ARTIFICIAL NEURAL NETWORK

    Directory of Open Access Journals (Sweden)

    R. M. Farouk

    2014-07-01

    Full Text Available The complementary DNA (cDNA sequence considered th e magic biometric technique for personal identification. Microarray image processing used fo r the concurrent genes identification. In this pape r, we present a new method for cDNA recognition based on the artificial neural network (ANN. We have segmented the location of the spots in a cDNA micro array. Thus, a precise localization and segmenting of a spot are essential to obtain a more exact intensity measurement, leading to a more accurate gene expression measurement. The segmented cDNA microarr ay image resized and used as an input for the proposed artificial neural network. For matching an d recognition, we have trained the artificial neura l network. Recognition results are given for the gall eries of cDNA sequences . The numerical results sho w that, the proposed matching technique is an effecti ve in the cDNA sequences process. The experimental results of our matching approach using different da tabases shows that, the proposed technique is an effective matching performance.

  20. The importance of artificial neural networks in biomedicine

    Energy Technology Data Exchange (ETDEWEB)

    Burke, H.B. [New York Medical College, Valhalla, NY (United States)

    1995-12-31

    The future explanatory power in biomedicine will be at the molecular-genetic level of analysis (rather than the epidemiologic-demographic or anatomic-cellular levels). This is the level of complex systems. Complex systems are characterized by nonlinearity and complex interactions. It is difficult for traditional statistical methods to capture complex systems because traditional methods attempt to find the model that best fits the statistician`s understanding of the phenomenon; complex systems are difficult to understand and therefore difficult to fit with a simple model. Artificial neural networks are nonparametric regression models. They can capture any phenomena, to any degree of accuracy (depending on the adequacy of the data and the power of the predictors), without prior knowledge of the phenomena. Further, artificial neural networks can be represented, not only as formulae, but also as graphical models. Graphical models can increase analytic power and flexibility. Artificial neural networks are a powerful method for capturing complex phenomena, but their use requires a paradigm shift, from exploratory analysis of the data to exploratory analysis of the model.

  1. Static human face recognition using artificial neural networks

    International Nuclear Information System (INIS)

    This paper presents a novel method of human face recognition using digital computers. A digital PC camera is used to take the BMP images of the human faces. An artificial neural network using Back Propagation Algorithm is developed as a recognition engine. The BMP images of the faces serve as the input patterns for this engine. A software 'Face Recognition' has been developed to recognize the human faces for which it is trained. Once the neural network is trained for patterns of the faces, the software is able to detect and recognize them with success rate of about 97%. (author)

  2. Artificial Neural Networks for Thermochemical Conversion of Biomass

    DEFF Research Database (Denmark)

    Puig Arnavat, Maria; Bruno, Joan Carles

    2015-01-01

    Artificial neural networks (ANNs), extensively used in different fields, have been applied for modeling biomass gasification processes in fluidized bed reactors. Two ANN models are presented, one for circulating fluidized bed gasifiers and another for bubbling fluidized bed gasifiers. Both models...... other authors. The obtained results show that the percentage composition of the main four gas species in producer gas (CO, CO2, H2, CH4) and producer gas yield for a biomass fluidized bed gasifier, can be successfully predicted by applying neural networks. The results obtained show high agreement...

  3. Modelling of word usage frequency dynamics using artificial neural network

    International Nuclear Information System (INIS)

    In this paper the method for modelling of word usage frequency time series is proposed. An artificial feedforward neural network was used to predict word usage frequencies. The neural network was trained using the maximum likelihood criterion. The Google Books Ngram corpus was used for the analysis. This database provides a large amount of data on frequency of specific word forms for 7 languages. Statistical modelling of word usage frequency time series allows finding optimal fitting and filtering algorithm for subsequent lexicographic analysis and verification of frequency trend models

  4. Prediction of Skin Penetration using Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Sangita Saini,

    2010-06-01

    Full Text Available The artificial neural networks (ANN technologies provide on-line capability to analyze many inputs and provide information to multiple outputs, and have the capability to learn or adapt to changing conditions. No doubt that the determination of Skin permeability is a time consuming process; which involves a quite tedious work. Material and method: Software Neurodimension was used for this study. A data set was taken from literature and used to train the network. A set of 20 compounds were used to construct the ANN models for training and 10 compounds used for prediction of skin penetration (n=30, molecular weight>500 da. Skin permeability expressed in log Kp (cm/h. Abraham descriptors of R2 (excess molar refraction, π2 H dipolarity/polarizability, Σα2 H, Σβ2 H (the overall or effective hydrogen-bond acidity and basicity, and Vx (the McGowan haracteristic volume were obtained. Result: The correlation between the skin permeability coefficient and the Abraham descriptors were obtained from the trained neural network. The regression coefficient was 0.856 for training subset and MSE was 0.04. In addition, thepredictability of the neural network model was compared to the experimental data. This paper uses artificial neural network for prediction of Skin permeability study.

  5. Optimizing Artificial Neural Networks using Cat Swarm Optimization Algorithm

    Directory of Open Access Journals (Sweden)

    John Paul T. Yusiong

    2012-12-01

    Full Text Available An Artificial Neural Network (ANN is an abstract representation of the biological nervous system which has the ability to solve many complex problems. The interesting attributes it exhibits makes an ANN capable of “learning”. ANN learning is achieved by training the neural network using a training algorithm. Aside from choosing a training algorithm to train ANNs, the ANN structure can also be optimized by applying certain pruning techniques to reduce network complexity. The Cat Swarm Optimization (CSO algorithm, a swarm intelligence-based optimization algorithm mimics the behavior of cats, is used as the training algorithm and the Optimal Brain Damage (OBD method as the pruning algorithm. This study suggests an approach to ANN training through the simultaneous optimization of the connection weights and ANN structure. Experiments performed on benchmark datasets taken from the UCI machine learning repository show that the proposed CSONN-OBD is an effective tool for training neural networks.

  6. Prediction of Short Term Electric Load Using Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Jayant D. Sawarkar

    2012-06-01

    Full Text Available Artificial Neural Network (ANN Method is applied to fore cast the short-term load for a large power system. A nonlinear load model is proposed and several structures of ANN for short term forecasting are tested. Inputs to the ANN are past loads and the output of the ANN is the load forecast for a given day. The network with one or two hidden layers is tested with various combinations of neurons, and the results are compared in terms of forecasting error. To demonstrate the effectiveness of the proposed approach, publicly available data from the State load Dispatch Centre, Airoli, Navi Mumbai’s web site has been taken to forecast the hourly load. We predicted the hourly load demand with a high degree of accuracy. Historical load data was divided into two parts where half of them are used for training and the other half is used for testing the ANN. Learning methods such as artificial neural networks, and more recently, support vector regression machines have been introduced to this field. In practices we often expect a fast forecasting, while standard algorithms based on the whole data set are time consuming. An absolute mean error of 2.26% was achieved when the trained network was tested on data

  7. Evolutionary Algorithm for Optimal Connection Weights in Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    G.V.R. Sagar, S. Venkata Chalam, Manoj Kumar Singh

    2011-12-01

    Full Text Available A neural network may be considered as an adaptive system that progressively self-organizes inorder to approximate the solution, making the problem solver free from the need to accuratelyand unambiguously specify the steps towards the solution. Moreover, Evolutionary computationcan be integrated with artificial Neural Network to increase the performance at various levels; inresult such neural network is called Evolutionary ANN. In this paper very important issue of neuralnetwork namely adjustment of connection weights for learning presented by Genetic algorithmover feed forward architecture. To see the performance of developed solution comparison hasgiven with respect to well established method of learning called gradient decent method. Abenchmark problem of classification, XOR, has taken to justify the experiment. Presented methodis not only having very probability to achieve the global minima but also having very fastconvergence.

  8. Backpropagation Artificial Neural Network To Detect Hyperthermic Seizures In Rats

    Directory of Open Access Journals (Sweden)

    Rakesh Kumar Sinha

    2003-02-01

    Full Text Available A three-layered feed-forward back-propagation Artificial Neural Network was used to classify the seizure episodes in rats. Seizure patterns were induced by subjecting anesthetized rats to a Biological Oxygen Demand incubator at 45-47ºC for 30 to 60 minutes. Selected fast Fourier transform data of one second epochs of electroencephalogram were used to train and test the network for the classification of seizure and normal patterns. The results indicate that the present network with the architecture of 40-12-1 (input-hidden-output nodes agrees with manual scoring of seizure and normal patterns with a high recognition rate of 98.6%.

  9. HIV lipodystrophy case definition using artificial neural network modelling

    DEFF Research Database (Denmark)

    Ioannidis, John P A; Trikalinos, Thomas A; Law, Matthew;

    2003-01-01

    OBJECTIVE: A case definition of HIV lipodystrophy has recently been developed from a combination of clinical, metabolic and imaging/body composition variables using logistic regression methods. We aimed to evaluate whether artificial neural networks could improve the diagnostic accuracy. METHODS......: The database of the case-control Lipodystrophy Case Definition Study was split into 504 subjects (265 with and 239 without lipodystrophy) used for training and 284 independent subjects (152 with and 132 without lipodystrophy) used for validation. Back-propagation neural networks with one or two middle layers...... were trained and validated. Results were compared against logistic regression models using the same information. RESULTS: Neural networks using clinical variables only (41 items) achieved consistently superior performance than logistic regression in terms of specificity, overall accuracy and area under...

  10. An Artificial Neural Network for Data Forecasting Purposes

    Directory of Open Access Journals (Sweden)

    Catalina Lucia COCIANU

    2015-01-01

    Full Text Available Considering the fact that markets are generally influenced by different external factors, the stock market prediction is one of the most difficult tasks of time series analysis. The research reported in this paper aims to investigate the potential of artificial neural networks (ANN in solving the forecast task in the most general case, when the time series are non-stationary. We used a feed-forward neural architecture: the nonlinear autoregressive network with exogenous inputs. The network training function used to update the weight and bias parameters corresponds to gradient descent with adaptive learning rate variant of the backpropagation algorithm. The results obtained using this technique are compared with the ones resulted from some ARIMA models. We used the mean square error (MSE measure to evaluate the performances of these two models. The comparative analysis leads to the conclusion that the proposed model can be successfully applied to forecast the financial data.

  11. XDANNG: XML based Distributed Artificial Neural Network with Globus Toolkit

    CERN Document Server

    Mahini, Hamidreza; Ghofrani, Javad

    2009-01-01

    Artificial Neural Network is one of the most common AI application fields. This field has direct and indirect usages most sciences. The main goal of ANN is to imitate biological neural networks for solving scientific problems. But the level of parallelism is the main problem of ANN systems in comparison with biological systems. To solve this problem, we have offered a XML-based framework for implementing ANN on the Globus Toolkit Platform. Globus Toolkit is well known management software for multipurpose Grids. Using the Grid for simulating the neuron network will lead to a high degree of parallelism in the implementation of ANN. We have used the XML for improving flexibility and scalability in our framework.

  12. Application of artificial neural networks to micro gas turbines

    Energy Technology Data Exchange (ETDEWEB)

    Bartolini, C.M.; Caresana, F.; Comodi, G.; Pelagalli, L.; Renzi, M.; Vagni, S. [Dipartimento di Energetica, Facolta di Ingegneria, Universita Politecnica delle Marche, via Brecce Bianche, 60131 Ancona (Italy)

    2011-01-15

    In this work, artificial neural networks (ANNs) were applied to describe the performance of a micro gas turbine (MGT). In particular, they were used (i) to complete performance diagrams for unavailable experimental data; (ii) to assess the influence of ambient parameters on performance; and (iii) to analyze and predict emissions of pollutants in the exhausts. The experimental data used to feed the ANNs were acquired from a manufacturer's test bed. Though large, the data set did not cover the whole working range of the turbine; ANNs and an artificial neural fuzzy interference system (ANFIS) were therefore applied to fill information gaps. The results of this investigation were also used for sensitivity analysis of the machine's behavior in different ambient conditions. ANNs can effectively evaluate both MGT performance and emissions in real installations in any climate, the worst R{sup 2} in the validation set being 0.9962. (author)

  13. Multiple simultaneous fault diagnosis via hierarchical and single artificial neural networks

    International Nuclear Information System (INIS)

    Process fault diagnosis involves interpreting the current status of the plant given sensor reading and process knowledge. There has been considerable work done in this area with a variety of approaches being proposed for process fault diagnosis. Neural networks have been used to solve process fault diagnosis problems in chemical process, as they are well suited for recognizing multi-dimensional nonlinear patterns. In this work, the use of Hierarchical Artificial Neural Networks in diagnosing the multi-faults of a chemical process are discussed and compared with that of Single Artificial Neural Networks. The lower efficiency of Hierarchical Artificial Neural Networks , in comparison to Single Artificial Neural Networks, in process fault diagnosis is elaborated and analyzed. Also, the concept of a multi-level selection switch is presented and developed to improve the performance of hierarchical artificial neural networks. Simulation results indicate that application of multi-level selection switch increase the performance of the hierarchical artificial neural networks considerably

  14. Prediction of littoral drift with artificial neural networks

    Directory of Open Access Journals (Sweden)

    A. K. Singh

    2007-07-01

    Full Text Available The amount of sand moving parallel to a coastline forms a prerequisite for many harbour design projects. Such information is currently obtained through various empirical formulae. Despite much research in the past an accurate and reliable estimation of the rate of sand drift has still remained as a problem. The current study addresses this issue through the use of artificial neural networks (ANN. Feed forward networks were developed to predict the sand drift from a variety of causative variables. The best network was selected after trying out many alternatives. In order to improve the accuracy further its outcome was used to develop another network. Such simple two-stage training yielded most satisfactory results. An equation combining the network and a non-linear regression is presented for quick field usage. An attempt was made to see how both ANN and statistical regression differ in processing the input information. The network was validated by confirming its consistency with the underlying physical process.

  15. Prediction of littoral drift with artificial neural networks

    Directory of Open Access Journals (Sweden)

    A. K. Singh

    2008-02-01

    Full Text Available The amount of sand moving parallel to a coastline forms a prerequisite for many harbor design projects. Such information is currently obtained through various empirical formulae. Despite so many works in the past an accurate and reliable estimation of the rate of sand drift has still remained as a problem. The current study addresses this issue through the use of artificial neural networks (ANN. Feed forward networks were developed to predict the sand drift from a variety of causative variables. The best network was selected after trying out many alternatives. In order to improve the accuracy further its outcome was used to develop another network. Such simple two-stage training yielded most satisfactory results. An equation combining the network and a non-linear regression is presented for quick field usage. An attempt was made to see how both ANN and statistical regression differ in processing the input information. The network was validated by confirming its consistency with underlying physical process.

  16. Artificial immune kernel clustering network for unsupervised image segmentation

    Institute of Scientific and Technical Information of China (English)

    Wenlong Huang; Licheng Jiao

    2008-01-01

    An immune kernel clustering network (IKCN) is proposed based on the combination of the artificial immune network and the support vector domain description (SVDD) for the unsupervised image segmentation. In the network, a new antibody neighborhood and an adaptive learning coefficient, which is inspired by the long-term memory in cerebral cortices are presented. Starting from IKCN algorithm, we divide the image feature sets into subsets by the antibodies, and then map each subset into a high dimensional feature space by a mercer kernel, where each antibody neighborhood is represented as a support vector hypersphere. The clustering results of the local support vector hyperspheres are combined to yield a global clustering solution by the minimal spanning tree (MST), where a predefined number of clustering is not needed. We compare the proposed methods with two common clustering algorithms for the artificial synthetic data set and several image data sets, including the synthetic texture images and the SAR images, and encouraging experimental results are obtained.

  17. Landslide susceptibility analysis using an artificial neural network model

    Science.gov (United States)

    Mansor, Shattri; Pradhan, Biswajeet; Daud, Mohamed; Jamaludin, Normalina; Khuzaimah, Zailani

    2007-10-01

    This paper deals with landslide susceptibility analysis using an artificial neural network model for Cameron Highland, Malaysia. Landslide locations were identified in the study area from interpretation of aerial photographs and field surveys. Topographical/geological data and satellite images were collected and processed using GIS and image processing tools. There are ten landslide inducing parameters which are considered for the landslide hazards. These parameters are topographic slope, aspect, curvature and distance from drainage, all derived from the topographic database; geology and distance from lineament, derived from the geologic database; landuse from Landsat satellite images; soil from the soil database; precipitation amount, derived from the rainfall database; and the vegetation index value from SPOT satellite images. Landslide hazard was analyzed using landslide occurrence factors employing the logistic regression model. The results of the analysis were verified using the landslide location data and compared with logistic regression model. The accuracy of hazard map observed was 85.73%. The qualitative landslide susceptibility analysis was carried out using an artificial neural network model by doing map overlay analysis in GIS environment. This information could be used to estimate the risk to population, property and existing infrastructure like transportation network.

  18. Prediction of Electrochemical Machining Process Parameters using Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Hoda Hosny Abuzied

    2012-01-01

    Full Text Available Electrochemical machining (ECM is a non-traditional machining process used mainly to cut hard or difficult to cut metals, where the application of a more traditional process is not convenient. It offers several special advantages including higher machining rate, better precision and control, and a wider range of materials that can be machined. A suitable selection of machining parameters for the ECM process relies heavily on the operator’s technologies and experience because of their numerous and diverse range. Machining parameters provided by the machine tool builder cannot meet the operator’s requirements. So, artificial neural networks were introduced as an efficient approach to predict the values of resulting surface roughness and material removal rate. Many researchers usedartificial neural networks (ANN in improvement of ECM process and also in other nontraditional machining processes as well be seen in later sections. The present study is, initiated to predict values of some of resulting process parameters such as metal removal rate(MRR, and surface roughness (Ra using artificial neural networks based on variation of certain predominant parameters of an electrochemical broaching process such as applied voltage, feed rate and electrolyte flow rate. ANN was found to be an efficient approach as it reduced time & effort required to predict material removal rate & surface roughness if they were found experimentally using trial & error method. To validate the proposed approach the predicted values of surface roughness and material removal rate were compared with a previously obtained ones from the experimental work.

  19. Gap Filling of Daily Sea Levels by Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Lyubka Pashova

    2013-06-01

    Full Text Available In the recent years, intelligent methods as artificial neural networks are successfully applied for data analysis from different fields of the geosciences. One of the encountered practical problems is the availability of gaps in the time series that prevent their comprehensive usage for the scientific and practical purposes. The article briefly describes two types of the artificial neural network (ANN architectures - Feed-Forward Backpropagation (FFBP and recurrent Echo state network (ESN. In some cases, the ANN can be used as an alternative on the traditional methods, to fill in missing values in the time series. We have been conducted several experiments to fill the missing values of daily sea levels spanning a 5-years period using both ANN architectures. A multiple linear regression for the same purpose has been also applied. The sea level data are derived from the records of the tide gauge Burgas, which is located on the western Black Sea coast. The achieved results have shown that the performance of ANN models is better than that of the classical one and they are very promising for the real-time interpolation of missing data in the time series.

  20. ARTIFICIAL NEURAL NETWORKS BASED GEARS MATERIAL SELECTION HYBRID INTELLIGENT SYSTEM

    Institute of Scientific and Technical Information of China (English)

    X.C. Li; W.X. Zhu; G. Chen; D.S. Mei; J. Zhang; K.M. Chen

    2003-01-01

    An artificial neural networks(ANNs) based gear material selection hybrid intelligent system is established by analyzing the individual advantages and weakness of expert system (ES) and ANNs and the applications in material select of them. The system mainly consists of tow parts: ES and ANNs. By being trained with much data samples,the back propagation (BP) ANN gets the knowledge of gear materials selection, and is able to inference according to user input. The system realizes the complementing of ANNs and ES. Using this system, engineers without materials selection experience can conveniently deal with gear materials selection.

  1. Product Assembly Cost Estimation Based on Artificial Neural Networks

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    This paper proposes a method for assembly cost estimation in actual manufacture during the design phase using artificial neural networks (ANN). It can support the de signers in cost effectiveness, then help to control the total cost. The method was used in the assembly cost estimation of the crucial parts of some railway stock products. As a compari son, we use the linear regression (LR) model in the same field. The result shows that ANN model performs better than the LR model in assembly cost estimation.

  2. Artificial Neural Networks for Dairy Industry: A Review

    Directory of Open Access Journals (Sweden)

    Sumit Goyal

    2012-06-01

    Full Text Available Artificial Neural Networks (ANNs have been implemented in almost every field, viz., science, technology, medicine and engineering as they have proved useful tools for obtaining the desired output including the analyses and shelf life prediction in case of food products. This review discusses the systematic information available in the literature concerning the implementation of ANN models for shelf life determination of milk, various dairy products, viz., yoghurt, chocolate, butter, cheese, kalakand, dairy confections and milk based coffee drinks, which would be very useful for dairy industry, consumers, wholesalers, retailers, regulatory agencies, food researchers and academicians.

  3. Artificial Neural Network Model of Hydrocarbon Migration and Accumulation

    Institute of Scientific and Technical Information of China (English)

    刘海滨; 吴冲龙

    2002-01-01

    Based on the dynamic simulation of the 3-D structure the sedimentary modeling, the unit entity model has been adopted to transfer the heterogeneous complex pas sage system into limited simple homogeneous entity, and then the traditional dyn amic simulation has been used to calculate the phase and the drive forces of the hyd rocarbon , and the artificial neural network(ANN) technology has been applied to resolve such problems as the direction, velocity and quantity of the hydrocarbo n migration among the unit entities. Through simulating of petroleum migration a nd accumulation in Zhu Ⅲ depression, the complex mechanism of hydrocarbon migra tion and accumulation has been opened out.

  4. Inflow forecasting using Artificial Neural Networks for reservoir operation

    OpenAIRE

    Chiamsathit, Chuthamat; Adeloye, Adebayo J.; Bankaru-Swamy, Soundharajan

    2016-01-01

    In this study, multi-layer perceptron (MLP) artificial neural networks have been applied to forecast one-month-ahead inflow for the Ubonratana reservoir, Thailand. To assess how well the forecast inflows have performed in the operation of the reservoir, simulations were carried out guided by the systems rule curves. As basis of comparison, four inflow situations were considered: (1) inflow known and assumed to be the historic (Type A); (2) inflow known and assumed to be the ...

  5. A Neuron- and a Synapse Chip for Artificial Neural Networks

    OpenAIRE

    Lansner, John; Lehmann, Torsten

    1992-01-01

    A cascadable, analog, CMOS chip set has been developed for hardware implementations of artificial neural networks (ANN's):I) a neuron chip containing an array of neurons with hyperbolic tangent activation functions and adjustable gains, and II) a synapse chip (or a matrix-vector multiplier) where the matrix is stored on-chip as differential voltages on capacitors. In principal any ANN configuration can be made using these chips. A neuron array of 4 neurons and a 4 × 4 matrix-vector multiplie...

  6. Discrimination between earthquakes and chemical explosions using artificial neural networks

    International Nuclear Information System (INIS)

    An Artificial Neural Network (ANN) for discriminating between earthquakes and chemical explosions located at epicentral distances, Δ <5 deg from Gauribidanur Array (GBA) has been developed using the short period digital seismograms recorded at GBA. For training the ANN spectral amplitude ratios between P and Lg phases computed at 13 different frequencies in the frequency range of 2-8 Hz, corresponding to 20 earthquakes and 23 chemical explosions were used along with other parameters like magnitude, epicentral distance and amplitude ratios Rg/P and Rg/Lg. After training and development, the ANN has correctly identified a set of 21 test events, comprising 6 earthquakes and 15 chemical explosions. (author)

  7. Accuracy Driven Artificial Neural Networks in Stock Market Prediction

    Directory of Open Access Journals (Sweden)

    Selvan Simon

    2012-06-01

    Full Text Available Globalization has made the stock market prediction (SMP accuracy more challenging and rewarding for the researchers and other participants in the stock market. Local and global economic situations alongwith the company’s financial strength and prospects have to be taken into account to improve the prediction accuracy. Artificial Neural Networks (ANN has been identified to be one of the dominant data mining techniques in stock market prediction area. In this paper, we survey different ANN models that have been experimented in SMP with the special enhancement techniques used with them to improve the accuracy. Also, we explore the possible research strategies in this accuracy driven ANN models.

  8. Artificial Neural Networks for Detection of Malaria in RBCs

    CERN Document Server

    Pandit, Purnima

    2016-01-01

    Malaria is one of the most common diseases caused by mosquitoes and is a great public health problem worldwide. Currently, for malaria diagnosis the standard technique is microscopic examination of a stained blood film. We propose use of Artificial Neural Networks (ANN) for the diagnosis of the disease in the red blood cell. For this purpose features / parameters are computed from the data obtained by the digital holographic images of the blood cells and is given as input to ANN which classifies the cell as the infected one or otherwise.

  9. A Hybrid Artificial Neural Network Model for Forecasting Short Time Series

    OpenAIRE

    Mohan, Anil

    2012-01-01

    Forecasting has long been the domain of traditional statistical models. Recent research has shown that novel and complex forecasting models do not necessarily outperform simpler models. These include in particular Artificial Neural Networks (ANNs). Even though claims of superior forecasting performance were made by Neural Network researchers, these claims were often unsubstantiated. Artificial neural networks are information processing paradigms motivated by the information ...

  10. Artificial Neural Networks for Nonlinear Dynamic Response Simulation in Mechanical Systems

    DEFF Research Database (Denmark)

    Christiansen, Niels Hørbye; Høgsberg, Jan Becker; Winther, Ole

    2011-01-01

    It is shown how artificial neural networks can be trained to predict dynamic response of a simple nonlinear structure. Data generated using a nonlinear finite element model of a simplified wind turbine is used to train a one layer artificial neural network. When trained properly the network is able...

  11. Hybrid multiobjective evolutionary design for artificial neural networks.

    Science.gov (United States)

    Goh, Chi-Keong; Teoh, Eu-Jin; Tan, Kay Chen

    2008-09-01

    Evolutionary algorithms are a class of stochastic search methods that attempts to emulate the biological process of evolution, incorporating concepts of selection, reproduction, and mutation. In recent years, there has been an increase in the use of evolutionary approaches in the training of artificial neural networks (ANNs). While evolutionary techniques for neural networks have shown to provide superior performance over conventional training approaches, the simultaneous optimization of network performance and architecture will almost always result in a slow training process due to the added algorithmic complexity. In this paper, we present a geometrical measure based on the singular value decomposition (SVD) to estimate the necessary number of neurons to be used in training a single-hidden-layer feedforward neural network (SLFN). In addition, we develop a new hybrid multiobjective evolutionary approach that includes the features of a variable length representation that allow for easy adaptation of neural networks structures, an architectural recombination procedure based on the geometrical measure that adapts the number of necessary hidden neurons and facilitates the exchange of neuronal information between candidate designs, and a microhybrid genetic algorithm ( microHGA) with an adaptive local search intensity scheme for local fine-tuning. In addition, the performances of well-known algorithms as well as the effectiveness and contributions of the proposed approach are analyzed and validated through a variety of data set types.

  12. Artificial neural network modeling of dissolved oxygen in reservoir.

    Science.gov (United States)

    Chen, Wei-Bo; Liu, Wen-Cheng

    2014-02-01

    The water quality of reservoirs is one of the key factors in the operation and water quality management of reservoirs. Dissolved oxygen (DO) in water column is essential for microorganisms and a significant indicator of the state of aquatic ecosystems. In this study, two artificial neural network (ANN) models including back propagation neural network (BPNN) and adaptive neural-based fuzzy inference system (ANFIS) approaches and multilinear regression (MLR) model were developed to estimate the DO concentration in the Feitsui Reservoir of northern Taiwan. The input variables of the neural network are determined as water temperature, pH, conductivity, turbidity, suspended solids, total hardness, total alkalinity, and ammonium nitrogen. The performance of the ANN models and MLR model was assessed through the mean absolute error, root mean square error, and correlation coefficient computed from the measured and model-simulated DO values. The results reveal that ANN estimation performances were superior to those of MLR. Comparing to the BPNN and ANFIS models through the performance criteria, the ANFIS model is better than the BPNN model for predicting the DO values. Study results show that the neural network particularly using ANFIS model is able to predict the DO concentrations with reasonable accuracy, suggesting that the neural network is a valuable tool for reservoir management in Taiwan. PMID:24078053

  13. Intrusion Detection Systems Based on Artificial Intelligence Techniques in Wireless Sensor Networks

    OpenAIRE

    Nabil Ali Alrajeh; Lloret, J

    2013-01-01

    Intrusion detection system (IDS) is regarded as the second line of defense against network anomalies and threats. IDS plays an important role in network security. There are many techniques which are used to design IDSs for specific scenario and applications. Artificial intelligence techniques are widely used for threats detection. This paper presents a critical study on genetic algorithm, artificial immune, and artificial neural network (ANN) based IDSs techniques used in wireless sensor netw...

  14. Selection in sugarcane families with artificial neural networks

    Directory of Open Access Journals (Sweden)

    Bruno Portela Brasileiro

    2015-04-01

    Full Text Available The objective of this study was to evaluate Artificial Neural Networks (ANN applied in an selection process within sugarcane families. The best ANN model produced no mistake, but was able to classify all genotypes correctly, i.e., the network made the same selective choice as the breeder during the simulation individual best linear unbiased predictor (BLUPIS, demonstrating the ability of the ANN to learn from the inputs and outputs provided in the training and validation phases. Since the ANN-based selection facilitates the identification of the best plants and the development of a new selection strategy in the best families, to ensure that the best genotypes of the population are evaluated in the following stages of the breeding program, we recommend to rank families by BLUP, followed by selection of the best families and finally, select the seedlings by ANN, from information at the individual level in the best families.

  15. Automatic segmentation of cerebral MR images using artificial neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Alirezaie, J.; Jernigan, M.E. [Univ. of Waterloo, Ontario (Canada); Nahmias, C. [McMaster Univ., Hamilton, Ontario (Canada)

    1996-12-31

    In this paper we present an unsupervised clustering technique for multispectral segmentation of magnetic resonance (MR) images of the human brain. Our scheme utilizes the Self Organizing Feature Map (SOFM) artificial neural network for feature mapping and generates a set of codebook vectors. By extending the network with an additional layer the map will be classified and each tissue class will be labelled. An algorithm has been developed for extracting the cerebrum from the head scan prior to the segmentation. Extracting the cerebrum is performed by stripping away the skull pixels from the T2 image. Three tissue types of the brain: white matter, gray matter and cerebral spinal fluid (CSF) are segmented accurately. To compare the results with other conventional approaches we applied the c-means algorithm to the problem.

  16. PREDICTION AND CLASSIFICATION OF THUNDERSTORMS USING ARTIFICIAL NEURAL NETWORK

    Directory of Open Access Journals (Sweden)

    AMIT KESARKAR

    2011-05-01

    Full Text Available Natural calamities cause heavy destruction to both life and property. Prediction of such calamities well in advance is inevitable. Prediction and classification of thunderstorms using ArtificialNeural Network (ANN is presented in this paper. The Numerical Weather Prediction (NWP models used today suffer from course resolution and inaccuracy. Two geographical locations are considered for our study namely, Paradeep in the west cost of India and Wollemi National Park, New South Wales, (Australia. ANN has designed to forecasts the occurrence of thunderstorm in these regions. Inputparameter selection is very critical in ANN design, Eight input parameters were identified to train the network. The output nodes clearly classifies the days with and without thunderstorms, thus successfully predicting thunderstorm activity in the specified regions.

  17. Extraction of Symbolic Rules from Artificial Neural Networks

    CERN Document Server

    Kamruzzaman, S M

    2010-01-01

    Although backpropagation ANNs generally predict better than decision trees do for pattern classification problems, they are often regarded as black boxes, i.e., their predictions cannot be explained as those of decision trees. In many applications, it is desirable to extract knowledge from trained ANNs for the users to gain a better understanding of how the networks solve the problems. A new rule extraction algorithm, called rule extraction from artificial neural networks (REANN) is proposed and implemented to extract symbolic rules from ANNs. A standard three-layer feedforward ANN is the basis of the algorithm. A four-phase training algorithm is proposed for backpropagation learning. Explicitness of the extracted rules is supported by comparing them to the symbolic rules generated by other methods. Extracted rules are comparable with other methods in terms of number of rules, average number of conditions for a rule, and predictive accuracy. Extensive experimental studies on several benchmarks classification ...

  18. Fault Tolerant Characteristics of Artificial Neural Network Electronic Hardware

    Science.gov (United States)

    Zee, Frank

    1995-01-01

    The fault tolerant characteristics of analog-VLSI artificial neural network (with 32 neurons and 532 synapses) chips are studied by exposing them to high energy electrons, high energy protons, and gamma ionizing radiations under biased and unbiased conditions. The biased chips became nonfunctional after receiving a cumulative dose of less than 20 krads, while the unbiased chips only started to show degradation with a cumulative dose of over 100 krads. As the total radiation dose increased, all the components demonstrated graceful degradation. The analog sigmoidal function of the neuron became steeper (increase in gain), current leakage from the synapses progressively shifted the sigmoidal curve, and the digital memory of the synapses and the memory addressing circuits began to gradually fail. From these radiation experiments, we can learn how to modify certain designs of the neural network electronic hardware without using radiation-hardening techniques to increase its reliability and fault tolerance.

  19. Design of Jetty Piles Using Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Yongjei Lee

    2014-01-01

    Full Text Available To overcome the complication of jetty pile design process, artificial neural networks (ANN are adopted. To generate the training samples for training ANN, finite element (FE analysis was performed 50 times for 50 different design cases. The trained ANN was verified with another FE analysis case and then used as a structural analyzer. The multilayer neural network (MBPNN with two hidden layers was used for ANN. The framework of MBPNN was defined as the input with the lateral forces on the jetty structure and the type of piles and the output with the stress ratio of the piles. The results from the MBPNN agree well with those from FE analysis. Particularly for more complex modes with hundreds of different design cases, the MBPNN would possibly substitute parametric studies with FE analysis saving design time and cost.

  20. Prediction of Austenite Formation Temperatures Using Artificial Neural Networks

    Science.gov (United States)

    Schulze, P.; Schmidl, E.; Grund, T.; Lampke, T.

    2016-03-01

    For the modeling and design of heat treatments, in consideration of the development/ transformation of the microstructure, different material data depending on the chemical composition, the respective microstructure/phases and the temperature are necessary. Material data are, e.g. the thermal conductivity, heat capacity, thermal expansion and transformation data etc. The quality of thermal simulations strongly depends on the accuracy of the material data. For many materials, the required data - in particular for different microstructures and temperatures - are rare in the literature. In addition, a different chemical composition within the permitted limits of the considered steel alloy cannot be predicted. A solution for this problem is provided by the calculation of material data using Artificial Neural Networks (ANN). In the present study, the start and finish temperatures of the transformation from the bcc lattice to the fcc lattice structure of hypoeutectoid steels are calculated using an Artificial Neural Network. An appropriate database containing different transformation temperatures (austenite formation temperatures) to train the ANN is selected from the literature. In order to find a suitable feedforward network, the network topologies as well as the activation functions of the hidden layers are varied and subsequently evaluated in terms of the prediction accuracy. The transformation temperatures calculated by the ANN exhibit a very good compliance compared to the experimental data. The results show that the prediction performance is even higher compared to classical empirical equations such as Andrews or Brandis. Therefore, it can be assumed that the presented ANN is a convenient tool to distinguish between bcc and fcc phases in hypoeutectoid steels.

  1. Comparison of sonar discrimination: dolphin and an artificial neural network.

    Science.gov (United States)

    Au, W W

    1994-05-01

    The capability of an echolocating dolphin to discriminate differences in the wall thickness of cylinders (3.81 cm o.d. and 12.7 cm length) was determined by Au and Pawloski [J. Comp. Physiol. A 170, 41-47 (1992)]. The dolphin was required to discriminate a standard target from comparison targets of differing wall thicknesses. Performance varied from 96% to 56% correct depending on the wall thickness of the comparison targets. The 75% correct threshold was determined to be wall thickness differences of -0.23 mm for comparison targets with thinner walls and +0.27 mm for comparison targets with thicker walls than the standard. The dolphin performance was unchanged in the presence of artificial broadband masking noise until the echo-energy-to-noise ratio fell below approximately 15 dB. A counterpropagation artificial neural network was used to examine broadband echo features from the same cylinders. Features of the echoes were determined by passing them through a filter bank of constant-Q filters. Echo features of the standard and each comparison target were analyzed in pairs by a neural network having two output nodes. Twenty echoes per target were used in the training set and 30 additional echoes per target were used in the test set. For the noise free condition, the network performed at a comparable level to the dolphin for Q values between 4 and 5. In the presence of noise, Q values between 7 and 8 were needed before the network could perform at a comparable level to the dolphin for echo-energy-to-noise ratios of 10 and 15 dB.(ABSTRACT TRUNCATED AT 250 WORDS) PMID:8207144

  2. Stellar Image Interpretation System Using Artificial Neural Networks:

    Directory of Open Access Journals (Sweden)

    A. El-Bassuny Alawy

    2004-01-01

    Full Text Available A supervised Artificial Neural Network (ANN based system is being developed employing the Bi-polar function for identifying stellar images in CCD frames. It is based on feed-forward artificial neural networks with error back-propagation learning. It has been coded in C language. The learning process was performed on a 341 input pattern set, while a similar set was used for testing. The present approach has been applied on a CCD frame of the open star cluster M67. The results obtained have been discussed and compared with those derived in our previous work employing the Uni-polar function and by a package known in the astronomical community (DAOPHOT-II. Full agreement was found between the present approach, that of Elnagahy et al, and the standard astronomical data for the cluster. It has been shown that the developed technique resembles that of the Uni-Polar function, possessing a simple, much faster yet reliable approach. Moreover, neither prior knowledge on, nor initial data from, the frame to be analysed is required, as it is for DAOPHOT-II. 

  3. Prediction aluminum corrosion inhibitor efficiency using artificial neural network (ANN)

    Science.gov (United States)

    Ebrahimi, Sh; Kalhor, E. G.; Nabavi, S. R.; Alamiparvin, L.; Pogaku, R.

    2016-06-01

    In this study, activity of some Schiff bases as aluminum corrosion inhibitor was investigated using artificial neural network (ANN). Hence, corrosion inhibition efficiency of Schiff bases (in any type) were gathered from different references. Then these molecules were drawn and optimized in Hyperchem software. Molecular descriptors generating and descriptors selection were fulfilled by Dragon software and principal component analysis (PCA) method, respectively. These structural descriptors along with environmental descriptors (ambient temperature, time of exposure, pH and the concentration of inhibitor) were used as input variables. Furthermore, aluminum corrosion inhibition efficiency was used as output variable. Experimental data were split into three sets: training set (for model building) and test set (for model validation) and simulation (for general model). Modeling was performed by Multiple linear regression (MLR) methods and artificial neural network (ANN). The results obtained in linear models showed poor correlation between experimental and theoretical data. However nonlinear model presented satisfactory results. Higher correlation coefficient of ANN (R > 0.9) revealed that ANN can be successfully applied for prediction of aluminum corrosion inhibitor efficiency of Schiff bases in different environmental conditions.

  4. Application of Artificial Neural Networks for Predicting Generated Wind Power

    Directory of Open Access Journals (Sweden)

    Vijendra Singh

    2016-03-01

    Full Text Available This paper addresses design and development of an artificial neural network based system for prediction of wind energy produced by wind turbines. Now in the last decade, renewable energy emerged as an additional alternative source for electrical power generation. We need to assess wind power generation capacity by wind turbines because of its non-exhaustible nature. The power generation by electric wind turbines depends on the speed of wind, flow direction, fluctuations, density of air, generator hours, seasons of an area, and wind turbine position. During a particular season, wind power generation access can be increased. In such a case, wind energy generation prediction is crucial for transmission of generated wind energy to a power grid system. It is advisable for the wind power generation industry to predict wind power capacity to diagnose it. The present paper proposes an effort to apply artificial neural network technique for measurement of the wind energy generation capacity by wind farms in Harshnath, Sikar, Rajasthan, India.

  5. Application of Artificial Neural Network Approach for Estimating Reference Evapotranspiration

    Directory of Open Access Journals (Sweden)

    Khyati N. Vyas

    2016-08-01

    Full Text Available The process of evapotranspiration (ET is a vital part of the water cycle. Exact estimation of the value of ET is necessary for designing irrigation systems and water resources management. Accurate estimation of ET is essential in agriculture, its over-estimation leads to cause the waste of valuable water resources and its underestimation leads to the plant moisture stress and decrease in the crop yield. The well known Penman-Monteith (PM equation always performs the highest accuracy results of estimating reference Evapotranspiration (ET0 among the existing methods is without any discussion. However, the equation requires climatic data that are not always available particularly for a developing country. ET0 is a complex process which is depending on a number of interacting meteorological factors, such as temperature, humidity, wind speed, and radiation. The lack of physical understanding of ET0 process and unavailability of all appropriate data results in imprecise estimation of ET0. Over the past two decades, artificial neural networks (ANNs have been increasingly applied in modeling of hydrological processes because of their ability in mapping the input–output relationship without any understanding of physical process. This paper investigates for the first time in the semiarid environment of Junagadh, the potential of an artificial neural network (ANN for estimating ET0 with limited climatic data set.

  6. Predicting oil price movements: A dynamic Artificial Neural Network approach

    International Nuclear Information System (INIS)

    Price of oil is important for the economies of oil exporting and oil importing countries alike. Therefore, insight into the likely future behaviour and patterns of oil prices can improve economic planning and reduce the impacts of oil market fluctuations. This paper aims to improve the application of Artificial Neural Network (ANN) techniques to prediction of oil price. We develop a dynamic Nonlinear Auto Regressive model with eXogenous input (NARX) as a form of ANN to account for the time factor. We estimate the model using macroeconomic data from OECD countries. In order to compare the results, we develop time series and ANN static models. We then use the output of time series model to develop a NARX model. The NARX model is trained with historical data from 1974 to 2004 and the results are verified with data from 2005 to 2009. The results show that NARX model is more accurate than time series and static ANN models in predicting oil prices in general as well as in predicting the occurrence of oil price shocks. - Highlights: • Nonlinear Auto Regressive model with eXogenous (NARX) inputs is developed for predicting oil prices. • The results of NARX model in oil price forecasting is more accurate than those of time series and Artificial Neural Network. • The NARX model predicts the price shocks in the oil market. • The NARX model is dynamic and accounts for the factor of time

  7. Electrical Load Forecasting in Power Distribution Network by Using Artificial Neural Network

    OpenAIRE

    Ali Nahari; Habib Rostami; Rahman Dashti

    2013-01-01

    Today, one of most important concerns in electrical power markets and distribution network is supplying the customer demands. In order to manage the market it is necessary to forecast the usage of electrical power in distribution network. The pattern of electrical power usage depends on many different parameters such as the week days, seasons, weather condition and etc. Today, researchers by using an artificial intelligence based on the natural intelligence are trying to forecast the costumer...

  8. Control of a hybrid compensator in a power network by an artificial neural network

    Directory of Open Access Journals (Sweden)

    I. S. Shaw

    1998-07-01

    Full Text Available Increased interest in the elimination of distortion in electrical power networks has led to the development of various compensator topologies. The increasing cost of electrical energy necessitates the cost-effective operation of any of these topologies. This paper considers the development of an artificial neural network based controller, trained by means of the backpropagation method, that ensures the cost-effective operation of the hybrid compensator consisting of various converters and filters.

  9. Soil NO emissions modelling using artificial neural network

    Energy Technology Data Exchange (ETDEWEB)

    Delon, Claire. e-mail: delc@aero.obs-mip.fr; Serca, Dominique; Dupont, Richard; Delmas, Robert [Laboratoire d' Aerologie, 14 avenue E. Belin, 31400 Toulouse (France); Boissard, Christophe; Dutot, Alain [Laboratoire Interuniversitaire des Systemes Atmospheriques, 94010 Creteil (France); Laville, Patricia [INRA, EGC, 78830 Thiverval-Grignon (France); Rosnay, Patricia de [CESBIO, 31400 Toulouse (France)

    2007-07-15

    Soils are considered as an important source for NO emissions, but the uncertainty in quantifying these emissions worldwide remains large due to the lack of field experiments and high variability in time and space of environmental parameters influencing NO emissions. In this study, the development of a relationship for NO flux emission from soil with pertinent environmental parameters is proposed. An Artificial Neural Network (ANN) is used to find the best non-linear regression between NO fluxes and seven environmental variables, introduced step by step: soil surface temperature, surface water filled pore space, soil temperature at depth (20-30 cm), fertilisation rate, sand percentage in the soil, pH and wind speed. The network performance is evaluated each time a new variable is introduced in the network, i.e. each variable is justified and evaluated in improving the network performance. A resulting equation linking NO flux from soil and the seven variables is proposed, and shows to perform well with measurements (R2 = 0.71), whereas other regression models give a poor correlation coefficient between calculation and measurements (R2 = 0.12 for known algorithms used at regional or global scales). ANN algorithm is shown to be a good alternative between biogeochemical and large-scale models, for future application at regional scale.

  10. Artificial Neural Networks for Diagnosis of Kidney Stones Disease

    Directory of Open Access Journals (Sweden)

    Koushal Kumar

    2012-07-01

    Full Text Available Artificial Neural networks are often used as a powerful discriminating classifier for tasks in medical diagnosis for early detection of diseases. They have several advantages over parametric classifiers such as discriminate analysis. The objective of this paper is to diagnose kidney stone disease by using three different neural network algorithms which have different architecture and characteristics. The aim of this work is to compare the performance of all three neural networks on the basis of its accuracy, time taken to build model, and training data set size. We will use Learning vector quantization (LVQ, two layers feed forward perceptron trained with back propagation training algorithm and Radial basis function (RBF networks for diagnosis of kidney stone disease. In this work we used Waikato Environment for Knowledge Analysis (WEKA version 3.7.5 as simulation tool which is an open source tool. The data set we used for diagnosis is real world data with 1000 instances and 8 attributes. In the end part we check the performance comparison of different algorithms to propose the best algorithm for kidney stone diagnosis. So this will helps in early identification of kidney stone in patients and reduces the diagnosis time.

  11. Arabic Named Entity Recognition Using Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Naji F. Mohammed

    2012-01-01

    Full Text Available Problem statement: Named Entity Recognition (NER is a task to identify proper names as well as temporal and numeric expressions, in an open-domain text. The NER task can help to improve the performance of various Natural Language Processing (NLP applications such as Information Extraction (IE, Information Retrieval (IR and Question Answering (QA tasks. This study discusses on the Named Entity Recognition of Arabic (NERA. The motivation is due to the lack of resources for Arabic named entities and to enhance the accuracy that has been reached in previous NERA systems. Approach: This system is designed based on neural network approach. The main task of neural network approach is to automatically learn to recognize component patterns and make intelligent decisions based on available data and it can also be applied to classify new information within large databases. The use of machine learning approach to classify NER from Arabic text based on neural network technique is proposed. Neural network approach has performed successfully in many areas of artificial intelligence. The system involves three stages: the first stage is pre-processing that cleans the collected data, the second involves converting Arabic letters to Roman alphabets and the final stage applies neural network to classify the collected data. Results: The accuracy of the system is 92 %. The system is compared with decision tree using the same data. The results showed that the neural network approach achieved better than decision tree. Conclusion: These results prove that our technique is capable to recognize named entities of Arabic texts.

  12. Forest Fire Detection Using Artificial Neural Network Algorithm Implemented in Wireless Sensor Networks

    Institute of Scientific and Technical Information of China (English)

    Yongsheng Liu; Yansong Yang; Chang Liu; Yu Gu

    2015-01-01

    A forest fire is a severe threat to forest resources and human life. In this paper, we propose a forest⁃fire detection system that has an artificial neural network algorithm implemented in a wireless sensor network (WSN). The proposed detection system mitigates the threat of forest fires by provide accurate fire alarm with low maintenance cost. The accuracy is increased by the novel multi⁃criteria detection, referred to as an alarm decision depends on multiple attributes of a forest fire. The multi⁃criteria detection is im⁃plemented by the artificial neural network algorithm. Meanwhile, we have developed a prototype of the proposed system consisting of the solar batter module, the fire detection module and the user interface module.

  13. 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)

  14. Artificial Neural Network L* from different magnetospheric field models

    Science.gov (United States)

    Yu, Y.; Koller, J.; Zaharia, S. G.; Jordanova, V. K.

    2011-12-01

    The third adiabatic invariant L* plays an important role in modeling and understanding the radiation belt dynamics. The popular way to numerically obtain the L* value follows the recipe described by Roederer [1970], which is, however, slow and computational expensive. This work focuses on a new technique, which can compute the L* value in microseconds without losing much accuracy: artificial neural networks. Since L* is related to the magnetic flux enclosed by a particle drift shell, global magnetic field information needed to trace the drift shell is required. A series of currently popular empirical magnetic field models are applied to create the L* data pool using 1 million data samples which are randomly selected within a solar cycle and within the global magnetosphere. The networks, trained from the above L* data pool, can thereby be used for fairly efficient L* calculation given input parameters valid within the trained temporal and spatial range. Besides the empirical magnetospheric models, a physics-based self-consistent inner magnetosphere model (RAM-SCB) developed at LANL is also utilized to calculate L* values and then to train the L* neural network. This model better predicts the magnetospheric configuration and therefore can significantly improve the L*. The above neural network L* technique will enable, for the first time, comprehensive solar-cycle long studies of radiation belt processes. However, neural networks trained from different magnetic field models can result in different L* values, which could cause mis-interpretation of radiation belt dynamics, such as where the source of the radiation belt charged particle is and which mechanism is dominant in accelerating the particles. Such a fact calls for attention to cautiously choose a magnetospheric field model for the L* calculation.

  15. Solar Energy Prediction for Malaysia Using Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Tamer Khatib

    2012-01-01

    Full Text Available This paper presents a solar energy prediction method using artificial neural networks (ANNs. An ANN predicts a clearness index that is used to calculate global and diffuse solar irradiations. The ANN model is based on the feed forward multilayer perception model with four inputs and one output. The inputs are latitude, longitude, day number, and sunshine ratio; the output is the clearness index. Data from 28 weather stations were used in this research, and 23 stations were used to train the network, while 5 stations were used to test the network. In addition, the measured solar irradiations from the sites were used to derive an equation to calculate the diffused solar irradiation, a function of the global solar irradiation and the clearness index. The proposed equation has reduced the mean absolute percentage error (MAPE in estimating the diffused solar irradiation compared with the conventional equation. Based on the results, the average MAPE, mean bias error and root mean square error for the predicted global solar irradiation are 5.92%, 1.46%, and 7.96%. The MAPE in estimating the diffused solar irradiation is 9.8%. A comparison with previous work was done, and the proposed approach was found to be more efficient and accurate than previous methods.

  16. Consistently Trained Artificial Neural Network for Automatic Ship Berthing Control

    Directory of Open Access Journals (Sweden)

    Y.A. Ahmed

    2015-09-01

    Full Text Available In this paper, consistently trained Artificial Neural Network controller for automatic ship berthing is discussed. Minimum time course changing manoeuvre is utilised to ensure such consistency and a new concept named ‘virtual window’ is introduced. Such consistent teaching data are then used to train two separate multi-layered feed forward neural networks for command rudder and propeller revolution output. After proper training, several known and unknown conditions are tested to judge the effectiveness of the proposed controller using Monte Carlo simulations. After getting acceptable percentages of success, the trained networks are implemented for the free running experiment system to judge the network’s real time response for Esso Osaka 3-m model ship. The network’s behaviour during such experiments is also investigated for possible effect of initial conditions as well as wind disturbances. Moreover, since the final goal point of the proposed controller is set at some distance from the actual pier to ensure safety, therefore a study on automatic tug assistance is also discussed for the final alignment of the ship with actual pier.

  17. Consumer Choice Prediction: Artificial Neural Networks versus Logistic Models

    Directory of Open Access Journals (Sweden)

    Christopher Gan

    2005-01-01

    Full Text Available Conventional econometric models, such as discriminant analysis and logistic regression have been used to predict consumer choice. However, in recent years, there has been a growing interest in applying artificial neural networks (ANN to analyse consumer behaviour and to model the consumer decision-making process. The purpose of this paper is to empirically compare the predictive power of the probability neural network (PNN, a special class of neural networks and a MLFN with a logistic model on consumers’ choices between electronic banking and non-electronic banking. Data for this analysis was obtained through a mail survey sent to 1,960 New Zealand households. The questionnaire gathered information on the factors consumers’ use to decide between electronic banking versus non-electronic banking. The factors include service quality dimensions, perceived risk factors, user input factors, price factors, service product characteristics and individual factors. In addition, demographic variables including age, gender, marital status, ethnic background, educational qualification, employment, income and area of residence are considered in the analysis. Empirical results showed that both ANN models (MLFN and PNN exhibit a higher overall percentage correct on consumer choice predictions than the logistic model. Furthermore, the PNN demonstrates to be the best predictive model since it has the highest overall percentage correct and a very low percentage error on both Type I and Type II errors.

  18. Modeling of methane emissions using artificial neural network approach

    Directory of Open Access Journals (Sweden)

    Stamenković Lidija J.

    2015-01-01

    Full Text Available The aim of this study was to develop a model for forecasting CH4 emissions at the national level, using Artificial Neural Networks (ANN with broadly available sustainability, economical and industrial indicators as their inputs. ANN modeling was performed using two different types of architecture; a Backpropagation Neural Network (BPNN and a General Regression Neural Network (GRNN. A conventional multiple linear regression (MLR model was also developed in order to compare model performance and assess which model provides the best results. ANN and MLR models were developed and tested using the same annual data for 20 European countries. The ANN model demonstrated very good performance, significantly better than the MLR model. It was shown that a forecast of CH4 emissions at the national level using the ANN model can be made successfully and accurately for a future period of up to two years, thereby opening the possibility to apply such a modeling technique which can be used to support the implementation of sustainable development strategies and environmental management policies. [Projekat Ministarstva nauke Republike Srbije, br. 172007

  19. Artificial neural networks application in duplex/triplex elevator group control system:

    OpenAIRE

    Imrak, C. Erdem

    2008-01-01

    Artificial neural networks can offer the better solution to the passenger call distribution problem when compared to the conventional elevator control systems. Therefore, the application of neural networks in elevator group control system is discussed. The significance of introducing artificial neural networks is presented. Elevator group control systems with neural networks can predict the next stopping floors to stop by considering what has been learnt by processing the changes in passenger...

  20. Didactic Strategy Discussion Based on Artificial Neural Networks Results.

    Science.gov (United States)

    Andina, D.; Bermúdez-Valbuena, R.

    2009-04-01

    Artificial Neural Networks (ANNs) are a mathematical model of the main known characteristics of biological brian dynamics. ANNs inspired in biological reality have been useful to design machines that show some human-like behaviours. Based on them, many experimentes have been succesfully developed emulating several biologial neurons characteristics, as learning how to solve a given problem. Sometimes, experimentes on ANNs feedback to biology and allow advances in understanding the biological brian behaviour, allowing the proposal of new therapies for medical problems involving neurons performing. Following this line, the author present results on artificial learning on ANN, and interpret them aiming to reinforce one of this two didactic estrategies to learn how to solve a given difficult task: a) To train with clear, simple, representative examples and feel confidence in brian generalization capabilities to achieve succes in more complicated cases. b) To teach with a set of difficult cases of the problem feeling confidence that the brian will efficiently solve the rest of cases if it is able to solve the difficult ones. Results may contribute in the discussion of how to orientate the design innovative succesful teaching strategies in the education field.

  1. 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.

  2. Applications of artificial neural networks (ANNs) in food science.

    Science.gov (United States)

    Huang, Yiqun; Kangas, Lars J; Rasco, Barbara A

    2007-01-01

    Artificial neural networks (ANNs) have been applied in almost every aspect of food science over the past two decades, although most applications are in the development stage. ANNs are useful tools for food safety and quality analyses, which include modeling of microbial growth and from this predicting food safety, interpreting spectroscopic data, and predicting physical, chemical, functional and sensory properties of various food products during processing and distribution. ANNs hold a great deal of promise for modeling complex tasks in process control and simulation and in applications of machine perception including machine vision and electronic nose for food safety and quality control. This review discusses the basic theory of the ANN technology and its applications in food science, providing food scientists and the research community an overview of the current research and future trend of the applications of ANN technology in the field.

  3. Artificial neural network analysis of triple effect absorption refrigeration systems

    Energy Technology Data Exchange (ETDEWEB)

    Hajizadeh Aghdam, A. [Department of Mechanical Engineering, Islamic Azad University (Iran, Islamic Republic of)], email: a.hajizadeh@iaukashan.ac.ir; Nazmara, H.; Farzaneh, B. [Department of Mechanical Engineering, University of Tabriz (Iran, Islamic Republic of)], email: h.nazmara@nioec.org, email: b_farzaneh_ms@yahoo.com

    2011-07-01

    In this study, artificial neural networks are utilized to predict the performance of triple effect series and parallel flow absorption refrigeration systems, with lithium bromide/water as the working fluid. Important parameters such as high generator and evaporator temperatures were varied and their effects on the performance characteristics of the refrigeration unit were observed. Absorption refrigeration systems make energy savings possible because they can use heat energy to produce cooling, in place of the electricity used for conventional vapour compression chillers. In addition, non-conventional sources of energy (such as solar, waste heat, and geothermal) can be utilized as their primary energy input. Moreover, absorption units use environmentally friendly working fluid pairs instead of CFCs and HCFCs, which affect the ozone layer. Triple effect absorption cycles were analysed. Results apply for both series and parallel flow systems. A relative preference for parallel-flow over series-flow is also shown.

  4. Artificial Neural Network Modeling of Forest Tree Growth

    CERN Document Server

    Gordon, C

    1999-01-01

    The problem of modeling forest tree growth curves with an artificial neural network (NN) is examined. The NN parametric form is shown to be a suitable model if each forest tree plot is assumed to consist of several differently growing sub-plots. The predictive Bayesian approach is used in estimating the NN output. Data from the correlated curve trend (CCT) experiments are used. The NN predictions are compared with those of one of the best parametric solutions, the Schnute model. Analysis of variance (ANOVA) methods are used to evaluate whether any observed differences are statistically significant. From a Frequentist perspective the differences between the Schnute and NN approach are found not to be significant. However, a Bayesian ANOVA indicates that there is a 93% probability of the NN approach producing better predictions on average.

  5. CYLINDIRICAL SPUR GEARS DESIGN BASED ON ARTIFICIAL NEURAL NETWORKS

    Directory of Open Access Journals (Sweden)

    İhsan TOKTAŞ

    2007-03-01

    Full Text Available In this study, testing and training data sets of Artificial Neural Networks(ANNs models have been produced by employing analytical design calculations of cylindrical spur gears. In the input layer, the constraints and requirement values of cylindrical spur gears are used while at the output layer the modules (e.g. the bending and contact stress and the number of tooths are used. These data have been presented to train a multi layered, single directed, hierarchically connected ANNs using Scaled Conjugate Gradient (SCG and Levenberg-Marquardt (LM Back Propagation algorithms with the logistic sigmoid transfer function. The outcomes demonstrated that, the ANN based model have been very successful and the testing data produced very low level of errors. It has been shown that, the ANN based mechanism may be used in the design of cylindrical spur gears instead of analytical calculations.

  6. Prediction of the plasma distribution using an artificial neural network

    Institute of Scientific and Technical Information of China (English)

    Li Wei; Chen JunFang; Wang Teng

    2009-01-01

    In this work, an artificial neural network (ANN) model is established using a back-propagation training algorithm in order to predict the plasma spatial distribution in an electron cyclotron resonance (ECR) - plasma-enhanced chemical vapor deposition (PECVD) plasma system. In our model, there are three layers: the input layer, the hidden layer and the output layer. The input layer is composed of five neurons: the radial position, the axial position, the gas pressure,the microwave power and the magnet coil current. The output layer is our target output neuron: the plasma density.The accuracy of our prediction is tested with the experimental data obtained by a Langmuir probe, and ANN results show a good agreement with the experimental data. It is concluded that ANN is a useful tool in dealing with some nonlinear problems of the plasma spatial distribution.

  7. Artificial metaplasticity neural network applied to credit scoring.

    Science.gov (United States)

    Marcano-Cedeño, Alexis; Marin-de-la-Barcena, A; Jimenez-Trillo, J; Piñuela, J A; Andina, D

    2011-08-01

    The assessment of the risk of default on credit is important for financial institutions. Different Artificial Neural Networks (ANN) have been suggested to tackle the credit scoring problem, however, the obtained error rates are often high. In the search for the best ANN algorithm for credit scoring, this paper contributes with the application of an ANN Training Algorithm inspired by the neurons' biological property of metaplasticity. This algorithm is especially efficient when few patterns of a class are available, or when information inherent to low probability events is crucial for a successful application, as weight updating is overemphasized in the less frequent activations than in the more frequent ones. Two well-known and readily available such as: Australia and German data sets has been used to test the algorithm. The results obtained by AMMLP shown have been superior to state-of-the-art classification algorithms in credit scoring.

  8. Offline Arabic Handwriting Recognition Using Artificial Neural Network

    CERN Document Server

    Zaidan, A A; Jalab, Hamid A; Alanazi, Hamdan O; Alnaqeib, Rami

    2010-01-01

    The ambition of a character recognition system is to transform a text document typed on paper into a digital format that can be manipulated by word processor software Unlike other languages, Arabic has unique features, while other language doesn't have, from this language these are seven or eight language such as ordo, jewie and Persian writing, Arabic has twenty eight letters, each of which can be linked in three different ways or separated depending on the case. The difficulty of the Arabic handwriting recognition is that, the accuracy of the character recognition which affects on the accuracy of the word recognition, in additional there is also two or three from for each character, the suggested solution by using artificial neural network can solve the problem and overcome the difficulty of Arabic handwriting recognition.

  9. High power fuel cell simulator based on artificial neural network

    Energy Technology Data Exchange (ETDEWEB)

    Chavez-Ramirez, Abraham U.; Munoz-Guerrero, Roberto [Departamento de Ingenieria Electrica, CINVESTAV-IPN. Av. Instituto Politecnico Nacional No. 2508, D.F. CP 07360 (Mexico); Duron-Torres, S.M. [Unidad Academica de Ciencias Quimicas, Universidad Autonoma de Zacatecas, Campus Siglo XXI, Edif. 6 (Mexico); Ferraro, M.; Brunaccini, G.; Sergi, F.; Antonucci, V. [CNR-ITAE, Via Salita S. Lucia sopra Contesse 5-98126 Messina (Italy); Arriaga, L.G. [Centro de Investigacion y Desarrollo Tecnologico en Electroquimica S.C., Parque Tecnologico Queretaro, Sanfandila, Pedro Escobedo, Queretaro (Mexico)

    2010-11-15

    Artificial Neural Network (ANN) has become a powerful modeling tool for predicting the performance of complex systems with no well-known variable relationships due to the inherent properties. A commercial Polymeric Electrolyte Membrane fuel cell (PEMFC) stack (5 kW) was modeled successfully using this tool, increasing the number of test into the 7 inputs - 2 outputs-dimensional spaces in the shortest time, acquiring only a small amount of experimental data. Some parameters could not be measured easily on the real system in experimental tests; however, by receiving the data from PEMFC, the ANN could be trained to learn the internal relationships that govern this system, and predict its behavior without any physical equations. Confident accuracy was achieved in this work making possible to import this tool to complex systems and applications. (author)

  10. Retrieving Atmospheric Precipitable Water Vapor Using Artificial Neural Network Approach

    Directory of Open Access Journals (Sweden)

    Wang Xin

    2013-07-01

    Full Text Available Discussing of water vapor and its variation is the important issue for synoptic meteorology and meteorology. In physical Atmospheric, the moisture content of the earth atmosphere is one of the most important parameters, it is hard to represent water vapor because of its space-time variation. High-spectral resolution Atmospheric Infrared Sounder (AIRS data can be used to retrieve the small scale vertical structure of air temperature, which provided a more accurate and good initial field for the numerical forecasting and the large-scale weather analysis. This paper proposes an artificial neural network to retrieve the clear sky atmospheric radiation data from AIRS and comparing with the AIRS Level-2 standard product, and gain a good inversion results.

  11. Forecasting of IBOVESPA returns using feedforward evolutionary artificial neural networks

    Directory of Open Access Journals (Sweden)

    Edgar Leite dos Santos Filho

    2011-12-01

    Full Text Available Facing the challenges of anticipating financial market uncertainties and movements, and the necessity of taking buy or sell decisions supported by rational methods, market traders found in statistics and econometrics methods, the base to support their decisions. In several scientific papers about forecasting financial time series, method selection keeps as central concern. This paper compares the performance of evolutionary feedforward artificial neural network (EANN and an AR+GARCH model, for one step ahead forecasting of IBOVESPA returns. The EANN is trained by self-adapting differential evolution algorithm and AR+GARCH model is adjusted to be used as performance reference. The root mean square error (RMSE and U-Theil inequality coefficient were used as performance metrics. Simulation results showed  EANN feedforward achieved better results, fit better and captured the nonlinear behavior of returns.

  12. The application of artificial neural networks in indirect cost estimation

    Science.gov (United States)

    Leśniak, Agnieszka

    2013-10-01

    Estimating of the costs of construction project is one of the most important task in the management of the project. The total costs can be divided into direct costs that are related to executing the works, and indirect costs that accompany delivery. A precise costs estimation is usually a highly labour and time-intensive task especially when using manual calculation methods. This paper presents Artificial Neural Network (ANN) approach to predicting index of indirect cost of construction projects in Poland. A quantitative study was undertaken on the factors conditioning indirect costs of polish construction projects and a determination was made of the actual costs incurred by enterprises during project implementation. As a result of these studies, a data set was assembled covering 72 real-life cases of building projects constructed in Poland.

  13. Artificial neural networks in foodstuffs: a critical review

    Directory of Open Access Journals (Sweden)

    S. Goyal

    2012-11-01

    Full Text Available This paper provides a critical review of literature concerning the artificial neural networks (ANN in foodstuffs. The main aim is to provide background information, motivation for applications and an exposition to the methodologies employed in the development of ANN techniques in foodstuffs. This review includes that all the latest works on the application of ANN to foodstuffs which have been reported excellently with positive and encouraging results. This review paper highlights the methodologies and algorithms employed for ANN models suitable for various foodstuffs, viz., avocados, tomatoes, cherries, grape, mosambi juice, apple juice, chicken nuggets, pistachio nuts, potato chips, kalakand, cakes, processed cheese, butter, milk and other foodstuffs. This review paper would be very beneficial for those working in food industry, academicians, students, researchers, scientists, factories manufacturing the food products and regulatory authorities, as it provides comprehensive latest information.

  14. Artificial Neural Networks in Fruits: A Comprehensive Review

    Directory of Open Access Journals (Sweden)

    Sumit Goyal

    2014-04-01

    Full Text Available This review discusses the application of artificial neural networks (ANN modeling in fruits. It covers all fruits in which ANN modeling has been applied. ANN is quite a new and easy computational modeling approach used for prediction, which has become popular and accepted by food industry, researchers, scientists and students. ANNs have been applied in almost every field of science and technology, viz., speech synthesis & recognition, pattern classification, adaptive interfaces between humans & complex physical systems, clustering, function approximation, image data compression, non-linear system modeling, associative memory, combinatorial optimization, control and several others, as they have proved valuable tools for obtaining the required output. ANN provides an exciting alternative method for solving a variety of problems in different areas of science and engineering. The aim of this communication is to discover the recent advances of ANN technology implemented in fruits, and discuss the critical role that ANN plays in predictive modelling.

  15. Searching for turbulence models by artificial neural network

    CERN Document Server

    Gamahara, Masataka

    2016-01-01

    Artificial neural network (ANN) is tested as a tool for finding a new subgrid model of the subgrid-scale (SGS) stress in large-eddy simulation. ANN is used to establish a functional relation between the grid-scale (GS) flow field and the SGS stress without any assumption of the form of function. Data required for training and test of ANN are provided by direct numerical simulation (DNS) of a turbulent channel flow. It is shown that ANN can establish a model similar to the gradient model. The correlation coefficients between the real SGS stress and the output of ANN are comparable to or larger than similarity models, but smaller than a two-parameter dynamic mixed model.

  16. Ascending Thermal Localization and Its Strongest Zone Centering by Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Ivan Suzdalev

    2011-04-01

    Full Text Available Thermal localization and their strongest zone centering by artificial neural networks (ANN, and it are used by the automatic or semiautomatic control system of unmanned aerial vehicles (UAV. Artificial neural network take input data from aircraft avionics. Actual thermal model of space and its value’s correlation with other factors are researched as well. Article in Lithuanian

  17. Network modeling of membrane-based artificial cellular systems

    Science.gov (United States)

    Freeman, Eric C.; Philen, Michael K.; Leo, Donald J.

    2013-04-01

    Computational models are derived for predicting the behavior of artificial cellular networks for engineering applications. The systems simulated involve the use of a biomolecular unit cell, a multiphase material that incorporates a lipid bilayer between two hydrophilic compartments. These unit cells may be considered building blocks that enable the fabrication of complex electrochemical networks. These networks can incorporate a variety of stimuli-responsive biomolecules to enable a diverse range of multifunctional behavior. Through the collective properties of these biomolecules, the system demonstrates abilities that recreate natural cellular phenomena such as mechanotransduction, optoelectronic response, and response to chemical gradients. A crucial step to increase the utility of these biomolecular networks is to develop mathematical models of their stimuli-responsive behavior. While models have been constructed deriving from the classical Hodgkin-Huxley model focusing on describing the system as a combination of traditional electrical components (capacitors and resistors), these electrical elements do not sufficiently describe the phenomena seen in experiment as they are not linked to the molecular scale processes. From this realization an advanced model is proposed that links the traditional unit cell parameters such as conductance and capacitance to the molecular structure of the system. Rather than approaching the membrane as an isolated parallel plate capacitor, the model seeks to link the electrical properties to the underlying chemical characteristics. This model is then applied towards experimental cases in order that a more complete picture of the underlying phenomena responsible for the desired sensing mechanisms may be constructed. In this way the stimuli-responsive characteristics may be understood and optimized.

  18. Identifing Atmospheric Pollutant Sources Using Artificial Neural Networks

    Science.gov (United States)

    Paes, F. F.; Campos, H. F.; Luz, E. P.; Carvalho, A. R.

    2008-05-01

    The estimation of the area source pollutant strength is a relevant issue for atmospheric environment. This characterizes an inverse problem in the atmospheric pollution dispersion. In the inverse analysis, an area source domain is considered, where the strength of such area source term is assumed unknown. The inverse problem is solved by using a supervised artificial neural network: multi-layer perceptron. The conection weights of the neural network are computed from delta rule - learning process. The neural network inversion is compared with results from standard inverse analysis (regularized inverse solution). In the regularization method, the inverse problem is formulated as a non-linear optimization approach, whose the objective function is given by the square difference between the measured pollutant concentration and the mathematical models, associated with a regularization operator. In our numerical experiments, the forward problem is addressed by a source-receptor scheme, where a regressive Lagrangian model is applied to compute the transition matrix. The second order maximum entropy regularization is used, and the regularization parameter is calculated by the L-curve technique. The objective function is minimized employing a deterministic scheme (a quasi-Newton algorithm) [1] and a stochastic technique (PSO: particle swarm optimization) [2]. The inverse problem methodology is tested with synthetic observational data, from six measurement points in the physical domain. The best inverse solutions were obtained with neural networks. References: [1] D. R. Roberti, D. Anfossi, H. F. Campos Velho, G. A. Degrazia (2005): Estimating Emission Rate and Pollutant Source Location, Ciencia e Natura, p. 131-134. [2] E.F.P. da Luz, H.F. de Campos Velho, J.C. Becceneri, D.R. Roberti (2007): Estimating Atmospheric Area Source Strength Through Particle Swarm Optimization. Inverse Problems, Desing and Optimization Symposium IPDO-2007, April 16-18, Miami (FL), USA, vol 1, p

  19. Query Based Approach Towards Spam Attacks Using Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Gaurav Kumar Tak

    2010-10-01

    Full Text Available Currently, spam and scams are passive attack over the inbox which can initiated to steal someconfidential information, to spread Worms, Viruses, Trojans, cookies and Sometimes they are used forphishing attacks. Spam mails are the major issue over mail boxes as well as over the internet. Spam mailscan be the cause of phishing attack, hacking of banking accounts, attacks on confidential data. Spammingis growing at a rapid rate since sending a flood of mails is easy and very cheap. Spam mails disturb themind-peace, waste time and consume various resources e.g., memory space and network bandwidth, sofiltering of spam mails is a big issue in cyber security.This paper presents an novel approach of spam filtering which is based on some query generatedapproach on the knowledge base and also use some artificial neural network methods to detect the spammails based on their behavior. analysis of the mail header, cross validation. Proposed methodologyincludes the 7 several steps which are well defined and achieve the higher accuracy. It works well with allkinds of spam mails (text based spam as well as image spam. Our tested data and experiments resultsshows promising results, and spam’s are detected out at least 98.17 % with 0.12% false positive.

  20. Estimation of local rainfall erosivity using artificial neural network

    Directory of Open Access Journals (Sweden)

    Paulo Tarso Sanches Oliveira

    2011-08-01

    Full Text Available The information retrieval of local values of rainfall erosivity is essential for soil loss estimation with the Universal Soil Loss Equation (USLE, and thus is very useful in soil and water conservation planning. In this manner, the objective of this study was to develop an Artificial Neural Network (ANN with the capacity of estimating, with satisfactory accuracy, the rainfall erosivity in any location of the Mato Grosso do Sul state. We used data from rain erosivity, latitude, longitude, altitude of pluviometric and pluviographic stations located in the state to train and test an ANN. After training with various network configurations, we selected the best performance and higher coefficient of determination calculated on the basis of data erosivity of the sample test and the values estimated by ANN. In evaluating the results, the confidence and the agreement indices were used in addition to the coefficient of determination. It was found that it is possible to estimate the rainfall erosivity for any location in the state of Mato Grosso do Sul, in a reliable way, using only data of geographical coordinates and altitude.

  1. Geochemical characterization of oceanic basalts using Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Iyer Sridhar D

    2009-12-01

    Full Text Available Abstract The geochemical discriminate diagrams help to distinguish the volcanics recovered from different tectonic settings but these diagrams tend to group the ocean floor basalts (OFB under one class i.e., as mid-oceanic ridge basalts (MORB. Hence, a method is specifically needed to identify the OFB as normal (N-MORB, enriched (E-MORB and ocean island basalts (OIB. We have applied Artificial Neural Network (ANN technique as a supervised Learning Vector Quantisation (LVQ to identify the inherent geochemical signatures present in the Central Indian Ocean Basin (CIOB basalts. A range of N-MORB, E-MORB and OIB dataset was used for training and testing of the network. Although the identification of the characters as N-MORB, E-MORB and OIB is completely dependent upon the training data set for the LVQ, but to a significant extent this method is found to be successful in identifying the characters within the CIOB basalts. The study helped to geochemically delineate the CIOB basalts as N-MORB with perceptible imprints of E-MORB and OIB characteristics in the form of moderately enriched rare earth and incompatible elements. Apart from the fact that the magmatic processes are difficult to be deciphered, the architecture performs satisfactorily.

  2. Atmospheric controls on Puerto Rico precipitation using artificial neural networks

    Science.gov (United States)

    Ramseyer, Craig A.; Mote, Thomas L.

    2016-01-01

    The growing need for local climate change scenarios has given rise to a wide range of empirical climate downscaling techniques. One of the most critical decisions in these methodologies is the selection of appropriate predictor variables for the downscaled surface predictand. A systematic approach to selecting predictor variables should be employed to ensure that the most important variables are utilized for the study site where the climate change scenarios are being developed. Tropical study areas have been far less examined than mid- and high-latitudes in the climate downscaling literature. As a result, studies analyzing optimal predictor variables for tropics are limited. The objectives of this study include developing artificial neural networks for six sites around Puerto Rico to develop nonlinear functions between 37 atmospheric predictor variables and local rainfall. The relative importance of each predictor is analyzed to determine the most important inputs in the network. Randomized ANNs are produced to determine the statistical significance of the relative importance of each predictor variable. Lower tropospheric moisture and winds are shown to be the most important variables at all sites. Results show inter-site variability in u- and v-wind importance depending on the unique geographic situation of the site. Lower tropospheric moisture and winds are physically linked to variability in sea surface temperatures (SSTs) and the strength and position of the North Atlantic High Pressure cell (NAHP). The changes forced by anthropogenic climate change in regional SSTs and the NAHP will impact rainfall variability in Puerto Rico.

  3. Atmospheric controls on Puerto Rico precipitation using artificial neural networks

    Science.gov (United States)

    Ramseyer, Craig A.; Mote, Thomas L.

    2016-10-01

    The growing need for local climate change scenarios has given rise to a wide range of empirical climate downscaling techniques. One of the most critical decisions in these methodologies is the selection of appropriate predictor variables for the downscaled surface predictand. A systematic approach to selecting predictor variables should be employed to ensure that the most important variables are utilized for the study site where the climate change scenarios are being developed. Tropical study areas have been far less examined than mid- and high-latitudes in the climate downscaling literature. As a result, studies analyzing optimal predictor variables for tropics are limited. The objectives of this study include developing artificial neural networks for six sites around Puerto Rico to develop nonlinear functions between 37 atmospheric predictor variables and local rainfall. The relative importance of each predictor is analyzed to determine the most important inputs in the network. Randomized ANNs are produced to determine the statistical significance of the relative importance of each predictor variable. Lower tropospheric moisture and winds are shown to be the most important variables at all sites. Results show inter-site variability in u- and v-wind importance depending on the unique geographic situation of the site. Lower tropospheric moisture and winds are physically linked to variability in sea surface temperatures (SSTs) and the strength and position of the North Atlantic High Pressure cell (NAHP). The changes forced by anthropogenic climate change in regional SSTs and the NAHP will impact rainfall variability in Puerto Rico.

  4. A comparison of artificial neural networks used for river forecasting

    Directory of Open Access Journals (Sweden)

    C. W. Dawson

    1999-01-01

    Full Text Available This paper compares the performance of two artificial neural network (ANN models – the multi layer perceptron (MLP and the radial basis function network (RBF – with a stepwise multiple linear regression model (SWMLR and zero order forecasts (ZOF of river flow. All models were trained using 15 minute rainfall-runoff data for the River Mole, a flood-prone tributary of the River Thames, UK. The models were then used to forecast river flows with a 6 hour lead time and 15 minute resolution, given only antecedent rainfall and discharge measurements. Two seasons (winter and spring were selected for model testing using a cross-validation technique and a range of diagnostic statistics. Overall, the MLP was more skillful than the RBF, SWMLR and ZOF models. However, the RBF flow forecasts were only marginally better than those of the simpler SWMLR and ZOF models. The results compare favourably with a review of previous studies and further endorse claims that ANNs are well suited to rainfall-runoff modelling and (potentially real-time flood forecasting.

  5. SIMULATION AND PREDICTION OF DEBRIS FLOW USING ARTIFICIAL NEURAL NETWORK

    Institute of Scientific and Technical Information of China (English)

    WANG Xie-kang; HUANG Er; CUI Peng

    2003-01-01

    Debris flow is one of the most destructive phenomena of natural hazards. Recently, major natural haz-ard, claiming human lives and assets, is due to debris flow in the world. Several practical methods for forecasting de-bris flow have been proposed, however, the accuracy of these methods is not high enough for practical use because of the stochastic and non-linear characteristics of debris flow. Artificial neural network has proven to be feasible and use-ful in developing models for nonlinear systems. On the other hand, predicting the future behavior based on a time se-ries of collected historical data is also an important tool in many scientific applications. In this study we present a three-layer feed-forward neural network model to forecast surge of debris flow according to the time series data collect-ed in the Jiangjia Ravine, situated in north part of Yunnan Province of China. The simulation and prediction of debris flow using the proposed approach shows this model is feasible, however, further studies are needed.

  6. Reliability and risk analysis using artificial neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Robinson, D.G. [Sandia National Labs., Albuquerque, NM (United States)

    1995-12-31

    This paper discusses preliminary research at Sandia National Laboratories into the application of artificial neural networks for reliability and risk analysis. The goal of this effort is to develop a reliability based methodology that captures the complex relationship between uncertainty in material properties and manufacturing processes and the resulting uncertainty in life prediction estimates. The inputs to the neural network model are probability density functions describing system characteristics and the output is a statistical description of system performance. The most recent application of this methodology involves the comparison of various low-residue, lead-free soldering processes with the desire to minimize the associated waste streams with no reduction in product reliability. Model inputs include statistical descriptions of various material properties such as the coefficients of thermal expansion of solder and substrate. Consideration is also given to stochastic variation in the operational environment to which the electronic components might be exposed. Model output includes a probabilistic characterization of the fatigue life of the surface mounted component.

  7. Modelling the SOFC behaviours by artificial neural network

    Energy Technology Data Exchange (ETDEWEB)

    Milewski, Jaroslaw; Swirski, Konrad [Institute of Heat Engineering, Warsaw University of Technology, 25 Nowowiejska Street, 00-665 Warsaw (Poland)

    2009-07-15

    The Artificial Neural Network (ANN) can be applied to simulate an object's behaviour without an algorithmic solution merely by utilizing available experimental data. The ANN is used for modelling singular cell behaviour. The optimal network architecture is shown and commented. The error backpropagation algorithm was used for an ANN training procedure. The ANN based SOFC model has the following input parameters: current density, temperature, fuel volume flow density (ml min{sup -1} cm{sup -2}), and oxidant volume flow density. Based on these input parameters, cell voltage is predicted by the model. Obtained results show that the ANN can be successfully used for modelling the singular solid oxide fuel cell. The self-learning process of the ANN provides an opportunity to adapt the model to new situations (e.g. certain types of impurities at inlet streams etc.). Based on the results from this study it can be concluded that, by using the ANN, an SOFC can be modelled with relatively high accuracy. In contrast to traditional models, the ANN is able to predict cell voltage without knowledge of numerous physical, chemical, and electrochemical factors. (author)

  8. Online performance assessment of heat exchanger using artificial neural networks

    Directory of Open Access Journals (Sweden)

    C. Ahilan, S. Kumanan, N. Sivakumaran

    2011-09-01

    Full Text Available Heat exchanger is a device in which heat is transferred from one medium to another across a solid surface. The performance of heat exchanger deteriorates with time due to fouling on the heat transfer surface. It is necessary to assess periodically the heat exchanger performance, in order to maintain at high efficiency level. Industries follow adopted practices to monitor but it is limited to some degree. Online monitoring has an advantage to understand and improve the heat exchanger performance. In this paper, online performance monitoring system for shell and tube heat exchanger is developed using artificial neural networks (ANNs. Experiments are conducted based on full factorial design of experiments to develop a model using the parameters such as temperatures and flow rates. ANN model for overall heat transfer coefficient of a design/ clean heat exchanger system is developed using a feed forward back propagation neural network and trained. The developed model is validated and tested by comparing the results with the experimental results. This model is used to assess the performance of heat exchanger with the real/fouled system. The performance degradation is expressed using fouling factor (FF, which is derived from the overall heat transfer coefficient of design system and real system. It supports the system to improve the performance by asset utilization, energy efficient and cost reduction interms of production loss.

  9. Use of artificial neural network for spatial rainfall analysis

    Indian Academy of Sciences (India)

    Tsangaratos Paraskevas; Rozos Dimitrios; Benardos Andreas

    2014-04-01

    In the present study, the precipitation data measured at 23 rain gauge stations over the Achaia County, Greece, were used to estimate the spatial distribution of the mean annual precipitation values over a specific catchment area. The objective of this work was achieved by programming an Artificial Neural Network (ANN) that uses the feed-forward back-propagation algorithm as an alternative interpolating technique. A Geographic Information System (GIS) was utilized to process the data derived by the ANN and to create a continuous surface that represented the spatial mean annual precipitation distribution.The ANN introduced an optimization procedure that was implemented during training, adjusting the hidden number of neurons and the convergence of the ANN in order to select the best network architecture. The performance of the ANN was evaluated using three standard statistical evaluation criteria applied to the study area and showed good performance. The outcomes were also compared with the results obtained from a previous study in the area of research which used a linear regression analysis for the estimation of the mean annual precipitation values giving more accurate results. The information and knowledge gained from the present study could improve the accuracy of analysis concerning hydrology and hydrogeological models, ground water studies, flood related applications and climate analysis studies.

  10. Query Based Approach Towards Spam Attacks Using Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Gaurav Kumar Tak

    2010-10-01

    Full Text Available Currently, spam and scams are passive attack over the inbox which can initiated to steal some confidential information, to spread Worms, Viruses, Trojans, cookies and Sometimes they are used for phishing attacks. Spam mails are the major issue over mail boxes as well as over the internet. Spam mails can be the cause of phishing attack, hacking of banking accounts, attacks on confidential data. Spamming is growing at a rapid rate since sending a flood of mails is easy and very cheap. Spam mails disturb the mind-peace, waste time and consume various resources e.g., memory space and network bandwidth, so filtering of spam mails is a big issue in cyber security. This paper presents an novel approach of spam filtering which is based on some query generated approach on the knowledge base and also use some artificial neural network methods to detect the spam mails based on their behavior. analysis of the mail header, cross validation. Proposed methodology includes the 7 several steps which are well defined and achieve the higher accuracy. It works well with all kinds of spam mails (text based spam as well as image spam. Our tested data and experiments results shows promising results, and spam’s are detected out at least 98.17 % with 0.12% false positive.

  11. Surrogate Modeling of Deformable Joint Contact using Artificial Neural Networks

    Science.gov (United States)

    Eskinazi, Ilan; Fregly, Benjamin J.

    2016-01-01

    Deformable joint contact models can be used to estimate loading conditions for cartilage-cartilage, implant-implant, human-orthotic, and foot-ground interactions. However, contact evaluations are often so expensive computationally that they can be prohibitive for simulations or optimizations requiring thousands or even millions of contact evaluations. To overcome this limitation, we developed a novel surrogate contact modeling method based on artificial neural networks (ANNs). The method uses special sampling techniques to gather input-output data points from an original (slow) contact model in multiple domains of input space, where each domain represents a different physical situation likely to be encountered. For each contact force and torque output by the original contact model, a multi-layer feed-forward ANN is defined, trained, and incorporated into a surrogate contact model. As an evaluation problem, we created an ANN-based surrogate contact model of an artificial tibiofemoral joint using over 75,000 evaluations of a fine-grid elastic foundation (EF) contact model. The surrogate contact model computed contact forces and torques about 1000 times faster than a less accurate coarse grid EF contact model. Furthermore, the surrogate contact model was seven times more accurate than the coarse grid EF contact model within the input domain of a walking motion. For larger input domains, the surrogate contact model showed the expected trend of increasing error with increasing domain size. In addition, the surrogate contact model was able to identify out-of-contact situations with high accuracy. Computational contact models created using our proposed ANN approach may remove an important computational bottleneck from musculoskeletal simulations or optimizations incorporating deformable joint contact models. PMID:26220591

  12. Mobile Robot Path Planning Using Polyclonal-Based Artificial Immune Network

    Directory of Open Access Journals (Sweden)

    Lixia Deng

    2013-01-01

    Full Text Available Polyclonal based artificial immune network (PC-AIN is utilized for mobile robot path planning. Artificial immune network (AIN has been widely used in optimizing the navigation path with the strong searching ability and learning ability. However, artificial immune network exists as a problem of immature convergence which some or all individuals tend to the same extreme value in the solution space. Thus, polyclonal-based artificial immune network algorithm is proposed to solve the problem of immature convergence in complex unknown static environment. Immunity polyclonal algorithm (IPCA increases the diversity of antibodies which tend to the same extreme value and finally selects the antibody with highest concentration. Meanwhile, immunity polyclonal algorithm effectively solves the problem of local minima caused by artificial potential field during the structure of parameter in artificial immune network. Extensive experiments show that the proposed method not only solves immature convergence problem of artificial immune network but also overcomes local minima problem of artificial potential field. So, mobile robot can avoid obstacles, escape traps, and reach the goal with optimum path and faster convergence speed.

  13. NONLINEAR MODELING AND CONTROLLING OF ARTIFICIAL MUSCLE SYSTEM USING NEURAL NETWORKS

    Institute of Scientific and Technical Information of China (English)

    Tian Sheping; Ding Guoqing; Yan Detian; Lin Liangming

    2004-01-01

    The pneumatic artificial muscles are widely used in the fields of medical robots,etc.Neural networks are applied to modeling and controlling of artificial muscle system.A single-joint artificial muscle test system is designed.The recursive prediction error (RPE) algorithm which yields faster convergence than back propagation (BP) algorithm is applied to train the neural networks.The realization of RPE algorithm is given.The difference of modeling of artificial muscles using neural networks with different input nodes and different hidden layer nodes is discussed.On this basis the nonlinear control scheme using neural networks for artificial muscle system has been introduced.The experimental results show that the nonlinear control scheme yields faster response and higher control accuracy than the traditional linear control scheme.

  14. The application of neural networks with artificial intelligence technique in the modeling of industrial processes

    International Nuclear Information System (INIS)

    Neural networks are a relatively new artificial intelligence technique that emulates the behavior of biological neural systems in digital software or hardware. These networks can 'learn', automatically, complex relationships among data. This feature makes the technique very useful in modeling processes for which mathematical modeling is difficult or impossible. The work described here outlines some examples of the application of neural networks with artificial intelligence technique in the modeling of industrial processes.

  15. A multi-objective approach to evolving artificial neural networks for coronary heart disease classification

    OpenAIRE

    Shenfield, Alex; Rostami, Shahin

    2015-01-01

    The optimisation of the accuracy of classifiers in pattern recognition is a complex problem that is often poorly understood. Whilst numerous techniques exist for the optimisation of weights in artificial neural networks (e.g. the Widrow-Hoff least mean squares algorithm and back propagation techniques), there do not exist any hard and fast rules for choosing the structure of an artificial neural network - in particular for choosing both the number of the hidden layers used in the network and ...

  16. Neutron spectrometry and dosimetry based on a new approach called Genetic Artificial Neural Networks

    International Nuclear Information System (INIS)

    Artificial Neural Networks and Genetic Algorithms are two relatively young research areas that were subject to a steadily growing interest during the past years. The structure of a neural network is a significant contributing factor to its performance and the structure is generally heuristically chosen. The use of evolutionary algorithms as search techniques has allowed different properties of neural networks to be evolved. This paper focuses on the intersection on neural networks and evolutionary computation, namely on how evolutionary algorithms can be used to assist neural network design and training, as a novel approach. In this research, a new evolvable artificial neural network modelling approach is presented, which utilizes an optimization process based on the combination of genetic algorithms and artificial neural networks, and is applied in the design of a neural network, oriented to solve the neutron spectrometry and simultaneous dosimetry problems, using only the count rates measured with a Bonner spheres spectrometer system as entrance data. (author)

  17. Electrical Load Forecasting in Power Distribution Network by Using Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Ali Nahari

    2013-11-01

    Full Text Available Today, one of most important concerns in electrical power markets and distribution network is supplying the customer demands. In order to manage the market it is necessary to forecast the usage of electrical power in distribution network. The pattern of electrical power usage depends on many different parameters such as the week days, seasons, weather condition and etc. Today, researchers by using an artificial intelligence based on the natural intelligence are trying to forecast the costumers’ usage of electrical power. In this Paper it is tried to forecast the electrical power usage according to weather data by using artificial neural network in Bushehr distribution electrical power network and also is tried to find out the pattern of electrical power usage with the dataset which is prepared by real data. The method which has been used here is useful in all kind of power forecasting such as short term, middle term and long term. It can be helpful to manage the distributed generators production schedule and also correction of electrical power usage.

  18. An improved BP artificial neural network algorithm for urban traffic flow intelligent prediction

    Institute of Scientific and Technical Information of China (English)

    XIONG Shi-yong; ZHANG Yi

    2009-01-01

    The traffic flow is interrelated to traffic congestion, the big traffic flow directly results in traffic congestion of some section. In this paper, on the basis of the research of overseas traffic accident, considering the characteristic of Chinese traffic, artificial neural network was used to predict traffic accident, and an improved BP artificial neural network model according with Chinese the situation of a country was proposed. The urban traffic flow prediction was simulated under the particular situation, the simulation result shows that the improved BP artificial neural network can fit the urban traffic flow prediction very well and have high performance.

  19. Digital soil mapping using reference area and artificial neural networks

    Directory of Open Access Journals (Sweden)

    Gustavo Pais de Arruda

    2016-06-01

    Full Text Available ABSTRACT Digital soil mapping is an alternative for the recognition of soil classes in areas where pedological surveys are not available. The main aim of this study was to obtain a digital soil map using artificial neural networks (ANN and environmental variables that express soil-landscape relationships. This study was carried out in an area of 11,072 ha located in the Barra Bonita municipality, state of São Paulo, Brazil. A soil survey was obtained from a reference area of approximately 500 ha located in the center of the area studied. With the mapping units identified together with the environmental variables elevation, slope, slope plan, slope profile, convergence index, geology and geomorphic surfaces, a supervised classification by ANN was implemented. The neural network simulator used was the Java NNS with the learning algorithm "back propagation." Reference points were collected for evaluating the performance of the digital map produced. The occurrence of soils in the landscape obtained in the reference area was observed in the following digital classification: medium-textured soils at the highest positions of the landscape, originating from sandstone, and clayey loam soils in the end thirds of the hillsides due to the greater presence of basalt. The variables elevation and slope were the most important factors for discriminating soil class through the ANN. An accuracy level of 82% between the reference points and the digital classification was observed. The methodology proposed allowed for a preliminary soil classification of an area not previously mapped using mapping units obtained in a reference area.

  20. Prediction of Soil Deformation in Tunnelling Using Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Jinxing Lai

    2016-01-01

    Full Text Available In the past few decades, as a new tool for analysis of the tough geotechnical problems, artificial neural networks (ANNs have been successfully applied to address a number of engineering problems, including deformation due to tunnelling in various types of rock mass. Unlike the classical regression methods in which a certain form for the approximation function must be presumed, ANNs do not require the complex constitutive models. Additionally, it is traced that the ANN prediction system is one of the most effective ways to predict the rock mass deformation. Furthermore, it could be envisaged that ANNs would be more feasible for the dynamic prediction of displacements in tunnelling in the future, especially if ANN models are combined with other research methods. In this paper, we summarized the state-of-the-art and future research challenges of ANNs on the tunnel deformation prediction. And the application cases as well as the improvement of ANN models were also presented. The presented ANN models can serve as a benchmark for effective prediction of the tunnel deformation with characters of nonlinearity, high parallelism, fault tolerance, learning, and generalization capability.

  1. Hierarchical Artificial Bee Colony Algorithm for RFID Network Planning Optimization

    Directory of Open Access Journals (Sweden)

    Lianbo Ma

    2014-01-01

    Full Text Available This paper presents a novel optimization algorithm, namely, hierarchical artificial bee colony optimization, called HABC, to tackle the radio frequency identification network planning (RNP problem. In the proposed multilevel model, the higher-level species can be aggregated by the subpopulations from lower level. In the bottom level, each subpopulation employing the canonical ABC method searches the part-dimensional optimum in parallel, which can be constructed into a complete solution for the upper level. At the same time, the comprehensive learning method with crossover and mutation operators is applied to enhance the global search ability between species. Experiments are conducted on a set of 10 benchmark optimization problems. The results demonstrate that the proposed HABC obtains remarkable performance on most chosen benchmark functions when compared to several successful swarm intelligence and evolutionary algorithms. Then HABC is used for solving the real-world RNP problem on two instances with different scales. Simulation results show that the proposed algorithm is superior for solving RNP, in terms of optimization accuracy and computation robustness.

  2. Using artificial neural networks for open-loop tomography

    CERN Document Server

    Osborn, James; Guzman, Dani; Butterley, Timothy; Myers, Richard; Guesalaga, Andres; Laine, Jesus

    2011-01-01

    Modern adaptive optics (AO) systems for large telescopes require tomographic techniques to reconstruct the phase aberrations induced by the turbulent atmosphere along a line of sight to a target which is angularly separated from the guide sources that are used to sample the atmosphere. Multi-object adaptive optics (MOAO) is one such technique. Here, we present a method which uses an artificial neural network (ANN) to reconstruct the target phase given off-axis references sources. We compare our ANN method with a standard least squares type matrix multiplication method and to the learn and apply method developed for the CANARY MOAO instrument. The ANN is trained with a large range of possible turbulent layer positions and therefore does not require any input of the optical turbulence profile. It is therefore less susceptible to changing conditions than some existing methods. We also exploit the non-linear response of the ANN to make it more robust to noisy centroid measurements than other linear techniques.

  3. Hierarchical artificial bee colony algorithm for RFID network planning optimization.

    Science.gov (United States)

    Ma, Lianbo; Chen, Hanning; Hu, Kunyuan; Zhu, Yunlong

    2014-01-01

    This paper presents a novel optimization algorithm, namely, hierarchical artificial bee colony optimization, called HABC, to tackle the radio frequency identification network planning (RNP) problem. In the proposed multilevel model, the higher-level species can be aggregated by the subpopulations from lower level. In the bottom level, each subpopulation employing the canonical ABC method searches the part-dimensional optimum in parallel, which can be constructed into a complete solution for the upper level. At the same time, the comprehensive learning method with crossover and mutation operators is applied to enhance the global search ability between species. Experiments are conducted on a set of 10 benchmark optimization problems. The results demonstrate that the proposed HABC obtains remarkable performance on most chosen benchmark functions when compared to several successful swarm intelligence and evolutionary algorithms. Then HABC is used for solving the real-world RNP problem on two instances with different scales. Simulation results show that the proposed algorithm is superior for solving RNP, in terms of optimization accuracy and computation robustness. PMID:24592200

  4. Predicting concrete corrosion of sewers using artificial neural network.

    Science.gov (United States)

    Jiang, Guangming; Keller, Jurg; Bond, Philip L; Yuan, Zhiguo

    2016-04-01

    Corrosion is often a major failure mechanism for concrete sewers and under such circumstances the sewer service life is largely determined by the progression of microbially induced concrete corrosion. The modelling of sewer processes has become possible due to the improved understanding of in-sewer transformation. Recent systematic studies about the correlation between the corrosion processes and sewer environment factors should be utilized to improve the prediction capability of service life by sewer models. This paper presents an artificial neural network (ANN)-based approach for modelling the concrete corrosion processes in sewers. The approach included predicting the time for the corrosion to initiate and then predicting the corrosion rate after the initiation period. The ANN model was trained and validated with long-term (4.5 years) corrosion data obtained in laboratory corrosion chambers, and further verified with field measurements in real sewers across Australia. The trained model estimated the corrosion initiation time and corrosion rates very close to those measured in Australian sewers. The ANN model performed better than a multiple regression model also developed on the same dataset. Additionally, the ANN model can serve as a prediction framework for sewer service life, which can be progressively improved and expanded by including corrosion rates measured in different sewer conditions. Furthermore, the proposed methodology holds promise to facilitate the construction of analytical models associated with corrosion processes of concrete sewers. PMID:26841228

  5. Prediction of Soil Deformation in Tunnelling Using Artificial Neural Networks.

    Science.gov (United States)

    Lai, Jinxing; Qiu, Junling; Feng, Zhihua; Chen, Jianxun; Fan, Haobo

    2016-01-01

    In the past few decades, as a new tool for analysis of the tough geotechnical problems, artificial neural networks (ANNs) have been successfully applied to address a number of engineering problems, including deformation due to tunnelling in various types of rock mass. Unlike the classical regression methods in which a certain form for the approximation function must be presumed, ANNs do not require the complex constitutive models. Additionally, it is traced that the ANN prediction system is one of the most effective ways to predict the rock mass deformation. Furthermore, it could be envisaged that ANNs would be more feasible for the dynamic prediction of displacements in tunnelling in the future, especially if ANN models are combined with other research methods. In this paper, we summarized the state-of-the-art and future research challenges of ANNs on the tunnel deformation prediction. And the application cases as well as the improvement of ANN models were also presented. The presented ANN models can serve as a benchmark for effective prediction of the tunnel deformation with characters of nonlinearity, high parallelism, fault tolerance, learning, and generalization capability. PMID:26819587

  6. Spatiotemporal modeling of monthly soil temperature using artificial neural networks

    Science.gov (United States)

    Wu, Wei; Tang, Xiao-Ping; Guo, Nai-Jia; Yang, Chao; Liu, Hong-Bin; Shang, Yue-Feng

    2013-08-01

    Soil temperature data are critical for understanding land-atmosphere interactions. However, in many cases, they are limited at both spatial and temporal scales. In the current study, an attempt was made to predict monthly mean soil temperature at a depth of 10 cm using artificial neural networks (ANNs) over a large region with complex terrain. Gridded independent variables, including latitude, longitude, elevation, topographic wetness index, and normalized difference vegetation index, were derived from a digital elevation model and remote sensing images with a resolution of 1 km. The good performance and robustness of the proposed ANNs were demonstrated by comparisons with multiple linear regressions. On average, the developed ANNs presented a relative improvement of about 44 % in root mean square error, 70 % in mean absolute percentage error, and 18 % in coefficient of determination over classical linear models. The proposed ANN models were then applied to predict soil temperatures at unsampled locations across the study area. Spatiotemporal variability of soil temperature was investigated based on the obtained database. Future work will be needed to test the applicability of ANNs for estimating soil temperature at finer scales.

  7. 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. PMID:24767037

  8. Advances in spectral analysis using artificial neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Martinez, M.; Vigneron, V.

    1995-12-31

    Artificial Neural networks (ANNs) have a powerful representational capacity and ability to handle with any multi-input multi-output mapping problem, e.g. in clustering, pattern recognition and identification areas, particularly when combined with some a priori knowledge and statistical point of view. They can be useful in spectrometry for the uranium enrichment methods by examples, where numerous approaches like models fitting or experts analysis are limited. These depends on the radiation measured: the methods most widely used developed over the past 20 years were based on the counting of the 185.7-keV peak with a sodium iodide scintillation detector or the 163.4-keV peak of {sup 235} U. But these methods depend critically of the source-detector geometry. A means of improving the above conventional methods is to reduce the region of interest: it is possible by focusing at the region called K{alpha}X where the three elementary components are present. The measurement of these components in mixtures leads to the isotope ratio {sup 235} U / ({sup 235} U + {sup 236} U + {sup 238} U). In this paper we explore statistical orientations and their consequences on `neural` parameters. We show this decisions are induced by a log-linear model, a special case of a GLIM (Generalized LInear Model) and correspond to a Maximum Likelihood Estimation problem. (authors). 15 refs., 7 figs., 2 tabs.

  9. Advances in spectral analysis using artificial neural networks

    International Nuclear Information System (INIS)

    Artificial Neural networks (ANNs) have a powerful representational capacity and ability to handle with any multi-input multi-output mapping problem, e.g. in clustering, pattern recognition and identification areas, particularly when combined with some a priori knowledge and statistical point of view. They can be useful in spectrometry for the uranium enrichment methods by examples, where numerous approaches like models fitting or experts analysis are limited. These depends on the radiation measured: the methods most widely used developed over the past 20 years were based on the counting of the 185.7-keV peak with a sodium iodide scintillation detector or the 163.4-keV peak of 235 U. But these methods depend critically of the source-detector geometry. A means of improving the above conventional methods is to reduce the region of interest: it is possible by focusing at the region called KαX where the three elementary components are present. The measurement of these components in mixtures leads to the isotope ratio 235 U / (235 U + 236 U + 238 U). In this paper we explore statistical orientations and their consequences on 'neural' parameters. We show this decisions are induced by a log-linear model, a special case of a GLIM (Generalized LInear Model) and correspond to a Maximum Likelihood Estimation problem. (authors). 15 refs., 7 figs., 2 tabs

  10. Modelling urban air quality using artificial neural network

    Energy Technology Data Exchange (ETDEWEB)

    Nagendra, S.M. Shiva; Khare, Mukesh [Indian Institute of Technology Delhi, Department of Civil Engineering, New Delhi (India)

    2005-05-01

    This paper describes the development of artificial neural network-based vehicular exhaust emission models for predicting 8-h average carbon monoxide concentrations at two air quality control regions (AQCRs) in the city of Delhi, India, viz. a typical traffic intersection (AQCR1) and a typical arterial road (AQCR2). Maximum of ten meteorological and six traffic characteristic variables have been used in the models' formulation. Three scenarios were considered - considering both meteorological and traffic characteristics input parameters; only meteorological inputs; and only traffic characteristics input data. The performance of all the developed models was evaluated on the basis of index of agreement (d) and other statistical parameters, viz. the mean and the deviations of the observed and predicted concentrations, mean bias error, mean square error, systematic and unsystematic root mean square error, coefficient of determination and linear best fit constant and gradient (Willmott in B Am Meteorol Soc 63:1309, 1982). The forecast performance of the developed models, with meteorological and traffic characteristics (d=0.78 for AQCR1 and d=0.69 for AQCR2) and with only meteorological inputs (d=0.77 for AQCR1 and d=0.67 for AQCR2), were comparable with the measured data. (orig.)

  11. An artificial neural network for proton identification in HERMES data

    Institute of Scientific and Technical Information of China (English)

    WANG Si-Guang; MAO Ya-Jun; YE Hong-Xue

    2009-01-01

    The HERMES time-of-flight (TOF) system is used for proton identification, but must be carefully calibrated for systematic biases in the equipment. This paper presents an artificial neural network (ANN) trained to recognize protons from Λ0 decay using only raw event data such as time delay, momentum, and trajectory. To avoid the systematic errors associated with Monte Carlo models, we collect a sample of raw experimental data from the year 2000. We presume that when for a positive hadron (assigned one proton mass) and a negative hadron (assigned one π- mass) the reconstructed invariant mass lies within the Λ0 resonance, the positive hadron is more likely to be a proton. Such events are assigned an output value of one during the training process; all others were assigned the output value zero.The trained ANN is capable of identifying protons in independent experimental data, with an efficiencyequivalent to the traditional TOF calibration. By modifying the threshold for proton identification, a researchercan trade off between selection efficiency and background rejection power. This simple and convenient methodis applicable to similar detection problems in other experiments.

  12. VOICE RECOGNITION USING ARTIFICIAL NEURAL NETWORKS AND GAUSSIAN MIXTURE MODELS

    Directory of Open Access Journals (Sweden)

    AARON NICHIE

    2013-05-01

    Full Text Available The ability of recognition systems to correctly recognize speakers based on their speech waveform distribution depends largely on how the recognition system can train the model parameters so as to provide the best class of discrimination. This paper presents the results of an effort to recognize the voice of individual speakers based on their continuous speech waveform distribution using the combined frameworks of artificial neural networks (ANN and statistical Gaussian mixture models (GMM. A feed-forward multilayer ANN architecture with 30 hidden neurons was implemented for discriminative classification and training and the statistical GMM model computed scores that were transferred to best match the speech features. The decision system determines the recognized speakers using correlation coefficient analysis to measure the goodness of match of speech feature frames of the detected speaker from the ANN and GMM frameworks. To validate performance of the system, experiments were conducted using speech utterances from 30 different speakers (20 males and 10 females. System performance showed average recognition rates of 77% for 5-word utterances and 43% when the lengths of the utterances were increased to 20-word utterances for cases of trained speech utterances. With unknown utterances, recognition rate of 18% achieved for 20-word utterances.

  13. Applications of artificial neural networks for microbial water quality modeling

    Energy Technology Data Exchange (ETDEWEB)

    Brion, G.M.; Lingireddy, S. [Univ. of Kentucky, Dept. of Civil Engineering, Lexington, Kentucky (United States)]. E-mail: gbrion@engr.uky.edu

    2002-06-15

    There has been a significant shift in the recent past towards protecting chemical and microbial quality of source waters rather than developing advanced methods to treat heavily polluted water. The key to successful best management practices in protecting the source waters is to identify sources of non-point pollution and their collective impact on the quality of water at the intake. This article presents a few successful applications where artificial neural networks (ANN) have proven to be the useful mathematical tools in correlating the nonlinear relationships between routinely measured parameters (such as rainfall, turbidity, fecal coliforms etc.) and quality of source waters and/or nature of fecal sources. These applications include, prediction of peak concentrations of Giardia and Cryptosporidium, sorting of fecal sources (e.g. agricultural animals vs. urban animals), predicting relative ages of the runoff sources, identifying the potential for sewage contamination. The ability of ANNs to work with complex, inter-related multiparameter databases, and provide superior predictive power in non-linear relationships has been the key for their successful application to microbial water quality studies. (author)

  14. AN APPLICATION OF SPEAKER RECOGNITION USING ARTIFICIAL NEURAL NETWORKS

    Directory of Open Access Journals (Sweden)

    Murat CANER

    2006-02-01

    Full Text Available In this study an artificial neural network (ANN is implemented, which has been used frequently as an implementation model in recent years, to recognize speaker identification. Generally, recognition is consist of three stages that, processing of signal, obtaining attributes and comparing them. Speech samples are transformed into digital data according to voice card of PC. In the analysis of voice stage, recurrent periods and white noise of voice data are trimmed by hamming window method and voice attribute part of the digital data is obtained. For obtaining attribute of voice data LPC (linear predictive coding and DFT (discrete fourier transform methods are used. Of those 28 coefficents, that is used for speaker recognition, 16 were obtained by the analysis of DFT and 12 were obtained by the analysis of LPC. The parameters that represent speaker voice, is used for training and test of ANN. Multilayer perceptron model is used as an architecture of ANN and backpropagation algorithm is used for training method. Voices of "a" is taken from 7 different person and their attributes are found. ANN is trained with these features to find the speaker who is the owner of the sample voice. And then using the test data that is not used for training part, recognition achievement of ANN is tested. As a result, good results were obtained with low failure rate.

  15. Improved Diagnostics Using Polarization Imaging and Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Jianhua Xuan

    2007-01-01

    Full Text Available In recent years, there has been an increasing interest in studying the propagation of polarized light in biological cells and tissues. This paper presents a novel approach to cell or tissue imaging using a full Stokes imaging system with advanced polarization image analysis algorithms for improved diagnostics. The key component of the Stokes imaging system is the electrically tunable retarder, enabling high-speed operation of the system to acquire four intensity images sequentially. From the acquired intensity images, four Stokes vector images can be computed to obtain complete polarization information. Polarization image analysis algorithms are then developed to analyze Stokes polarization images for cell or tissue classification. Specifically, wavelet transforms are first applied to the Stokes components for initial feature analysis and extraction. Artificial neural networks (ANNs are then used to extract diagnostic features for improved classification and prediction. In this study, phantom experiments have been conducted using a prototyped Stokes polarization imaging device. In particular, several types of phantoms, consisting of polystyrene latex spheres in various diameters, were prepared to simulate different conditions of epidermal layer of skin. The experimental results from phantom studies and a plant cell study show that the classification performance using Stokes images is significantly improved over that using the intensity image only.

  16. Signal processing using artificial neural network for BOTDA sensor system.

    Science.gov (United States)

    Azad, Abul Kalam; Wang, Liang; Guo, Nan; Tam, Hwa-Yaw; Lu, Chao

    2016-03-21

    We experimentally demonstrate the use of artificial neural network (ANN) to process sensing signals obtained from Brillouin optical time domain analyzer (BOTDA). The distributed temperature information is extracted directly from the local Brillouin gain spectra (BGSs) along the fiber under test without the process of determination of Brillouin frequency shift (BFS) and hence conversion from BFS to temperature. Unlike our previous work for short sensing distance where ANN is trained by measured BGSs, here we employ ideal BGSs with different linewidths to train the ANN in order to take the linewidth variation due to different conditions from the training and testing phases into account, making it feasible for long distance sensing. Moreover, the performance of ANN is compared with other two techniques, Lorentzian curve fitting and cross-correlation method, and our results show that ANN has higher accuracy and larger tolerance to measurement error, especially at large frequency scanning step. We also show that the temperature extraction from BOTDA measurements employing ANN is significantly faster than the other two approaches. Hence ANN can be an excellent alternative tool to process BGSs measured by BOTDA and obtain temperature distribution along the fiber, especially when large frequency scanning step is adopted to significantly reduce the measurement time but without sacrifice of sensing accuracy. PMID:27136863

  17. Artificial Neural Network Approach for Mapping Contrasting Tillage Practices

    Directory of Open Access Journals (Sweden)

    Terry Howell

    2010-02-01

    Full Text Available Tillage information is crucial for environmental modeling as it directly affects evapotranspiration, infiltration, runoff, carbon sequestration, and soil losses due to wind and water erosion from agricultural fields. However, collecting this information can be time consuming and costly. Remote sensing approaches are promising for rapid collection of tillage information on individual fields over large areas. Numerous regression-based models are available to derive tillage information from remote sensing data. However, these models require information about the complex nature of underlying watershed characteristics and processes. Unlike regression-based models, Artificial Neural Network (ANN provides an efficient alternative to map complex nonlinear relationships between an input and output datasets without requiring a detailed knowledge of underlying physical relationships. Limited or no information currently exist quantifying ability of ANN models to identify contrasting tillage practices from remote sensing data. In this study, a set of Landsat TM-based ANN models was developed to identify contrasting tillage practices in the Texas High Plains. Observed tillage data from Moore and Ochiltree Counties were used to develop and evaluate the models, respectively. The overall classification accuracy for the 15 models developed with the Moore County dataset varied from 74% to 91%. Statistical evaluation of these models against the Ochiltree County dataset produced results with an overall classification accuracy varied from 66% to 80%. The ANN models based on TM band 5 or indices of TM Band 5 may provide consistent and accurate tillage information when applied to the Texas High Plains.

  18. An artificial neural network for proton identification in HERMES data

    International Nuclear Information System (INIS)

    The HERMES time-of-flight (TOF) system is used for proton identification, but must be carefully calibrated for systematic biases in the equipment. This paper presents an artificial neural network (ANN) trained to recognize protons from Λ0 decay using only raw event data such as time delay, momentum, and trajectory. To avoid the systematic errors associated with Monte Carlo models, we collect a sample of raw experimental data from the year 2000. We presume that when for a positive hadron (assigned one proton mass) and a negative hadron (assigned one π- mass) the reconstructed invariant mass lies within the Λ0 resonance, the positive hadron is more likely to be a proton. Such events are assigned an output value of one during the training process; all others were assigned the output value zero. The trained ANN is capable of identifying protons in independent experimental data, with an efficiency equivalent to the traditional TOF calibration. By modifying the threshold for proton identification, a researcher can trade off between selection efficiency and background rejection power. This simple and convenient method is applicable to similar detection problems in other experiments.(authors)

  19. 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.

  20. Urban Ozone Concentration Forecasting with Artificial Neural Network in Corsica

    Directory of Open Access Journals (Sweden)

    Tamas Wani

    2014-03-01

    Full Text Available Atmospheric pollutants concentration forecasting is an important issue in air quality monitoring. Qualitair Corse, the organization responsible for monitoring air quality in Corsica (France, needs to develop a short-term prediction model to lead its mission of information towards the public. Various deterministic models exist for local forecasting, but need important computing resources, a good knowledge of atmospheric processes and can be inaccurate because of local climatical or geographical particularities, as observed in Corsica, a mountainous island located in the Mediterranean Sea. As a result, we focus in this study on statistical models, and particularly Artificial Neural Networks (ANNs that have shown good results in the prediction of ozone concentration one hour ahead with data measured locally. The purpose of this study is to build a predictor realizing predictions of ozone 24 hours ahead in Corsica in order to be able to anticipate pollution peaks formation and to take appropriate preventive measures. Specific meteorological conditions are known to lead to particular pollution event in Corsica (e.g. Saharan dust events. Therefore, an ANN model will be used with pollutant and meteorological data for operational forecasting. Index of agreement of this model was calculated with a one year test dataset and reached 0.88.

  1. Predicting concrete corrosion of sewers using artificial neural network.

    Science.gov (United States)

    Jiang, Guangming; Keller, Jurg; Bond, Philip L; Yuan, Zhiguo

    2016-04-01

    Corrosion is often a major failure mechanism for concrete sewers and under such circumstances the sewer service life is largely determined by the progression of microbially induced concrete corrosion. The modelling of sewer processes has become possible due to the improved understanding of in-sewer transformation. Recent systematic studies about the correlation between the corrosion processes and sewer environment factors should be utilized to improve the prediction capability of service life by sewer models. This paper presents an artificial neural network (ANN)-based approach for modelling the concrete corrosion processes in sewers. The approach included predicting the time for the corrosion to initiate and then predicting the corrosion rate after the initiation period. The ANN model was trained and validated with long-term (4.5 years) corrosion data obtained in laboratory corrosion chambers, and further verified with field measurements in real sewers across Australia. The trained model estimated the corrosion initiation time and corrosion rates very close to those measured in Australian sewers. The ANN model performed better than a multiple regression model also developed on the same dataset. Additionally, the ANN model can serve as a prediction framework for sewer service life, which can be progressively improved and expanded by including corrosion rates measured in different sewer conditions. Furthermore, the proposed methodology holds promise to facilitate the construction of analytical models associated with corrosion processes of concrete sewers.

  2. Multiobjective analysis of a public wellfield using artificial neural networks

    Science.gov (United States)

    Coppola, E.A.; Szidarovszky, F.; Davis, D.; Spayd, S.; Poulton, M.M.; Roman, E.

    2007-01-01

    As competition for increasingly scarce ground water resources grows, many decision makers may come to rely upon rigorous multiobjective techniques to help identify appropriate and defensible policies, particularly when disparate stakeholder groups are involved. In this study, decision analysis was conducted on a public water supply wellfield to balance water supply needs with well vulnerability to contamination from a nearby ground water contaminant plume. With few alternative water sources, decision makers must balance the conflicting objectives of maximizing water supply volume from noncontaminated wells while minimizing their vulnerability to contamination from the plume. Artificial neural networks (ANNs) were developed with simulation data from a numerical ground water flow model developed for the study area. The ANN-derived state transition equations were embedded into a multiobjective optimization model, from which the Pareto frontier or trade-off curve between water supply and wellfield vulnerability was identified. Relative preference values and power factors were assigned to the three stakeholders, namely the company whose waste contaminated the aquifer, the community supplied by the wells, and the water utility company that owns and operates the wells. A compromise pumping policy that effectively balances the two conflicting objectives in accordance with the preferences of the three stakeholder groups was then identified using various distance-based methods. ?? 2006 National Ground Water Association.

  3. Probabilistic Dynamic Deployment of Wireless Sensor Networks by Artificial Bee Colony Algorithm

    OpenAIRE

    Dervis Karaboga; Celal Ozturk; Beyza Gorkemli

    2011-01-01

    As the usage and development of wireless sensor networks are increasing, the problems related to these networks are being realized. Dynamic deployment is one of the main topics that directly affect the performance of the wireless sensor networks. In this paper, the artificial bee colony algorithm is applied to the dynamic deployment of stationary and mobile sensor networks to achieve better performance by trying to increase the coverage area of the network. A probabilistic detection model is ...

  4. HL-2A tokamak disruption forecasting based on an artificial neural network

    Institute of Scientific and Technical Information of China (English)

    Wang Hao; Wang Ai-Ke; Yang Qing-Wei; Ding Xuan-Tong; Dong Jia-Qi; Sanuki H; Itoh K

    2007-01-01

    Artificial neural networks are trained to forecast the plasma disruption in HL-2A tokamak. Optimized network architecture is obtained. Saliency analysis is made to assess the relative importance of different diagnostic signals as network input. The trained networks can successfully detect the disruptive pulses of HL-2A tokamak. The results obtained show the possibiliry of developing a neural network predictor that intervenes well in edvance for avoiding plasma disruption or mitigating its effects.

  5. 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

  6. Application of artificial neural networks in analysis of CHF experimental data in round tubes

    International Nuclear Information System (INIS)

    Artificial neural networks (ANNs) are applied successfully to analyze the critical heat flux (CHF) experimental data from some round tubes in this paper. A set of software adopting artificial neural network method for predicting CHF in round tube and a set of CHF database are gotten. Comparing with common CHF correlations and CHF look-up table, ANN method has stronger ability of allow-wrong and nice robustness. The CHF predicting software adopting artificial neural network technology can improve the predicting accuracy in a wider parameter range, and is easier to update and to use. The artificial neural network method used in this paper can be applied to some similar physical problems. (authors)

  7. Enhancing Artificial Bee Colony Algorithm with Self-Adaptive Searching Strategy and Artificial Immune Network Operators for Global Optimization

    Directory of Open Access Journals (Sweden)

    Tinggui Chen

    2014-01-01

    Full Text Available Artificial bee colony (ABC algorithm, inspired by the intelligent foraging behavior of honey bees, was proposed by Karaboga. It has been shown to be superior to some conventional intelligent algorithms such as genetic algorithm (GA, artificial colony optimization (ACO, and particle swarm optimization (PSO. However, the ABC still has some limitations. For example, ABC can easily get trapped in the local optimum when handing in functions that have a narrow curving valley, a high eccentric ellipse, or complex multimodal functions. As a result, we proposed an enhanced ABC algorithm called EABC by introducing self-adaptive searching strategy and artificial immune network operators to improve the exploitation and exploration. The simulation results tested on a suite of unimodal or multimodal benchmark functions illustrate that the EABC algorithm outperforms ACO, PSO, and the basic ABC in most of the experiments.

  8. Enhancing artificial bee colony algorithm with self-adaptive searching strategy and artificial immune network operators for global optimization.

    Science.gov (United States)

    Chen, Tinggui; Xiao, Renbin

    2014-01-01

    Artificial bee colony (ABC) algorithm, inspired by the intelligent foraging behavior of honey bees, was proposed by Karaboga. It has been shown to be superior to some conventional intelligent algorithms such as genetic algorithm (GA), artificial colony optimization (ACO), and particle swarm optimization (PSO). However, the ABC still has some limitations. For example, ABC can easily get trapped in the local optimum when handing in functions that have a narrow curving valley, a high eccentric ellipse, or complex multimodal functions. As a result, we proposed an enhanced ABC algorithm called EABC by introducing self-adaptive searching strategy and artificial immune network operators to improve the exploitation and exploration. The simulation results tested on a suite of unimodal or multimodal benchmark functions illustrate that the EABC algorithm outperforms ACO, PSO, and the basic ABC in most of the experiments. PMID:24772023

  9. Next Challenges in Bringing Artificial Immune Systems to Production in Network Security

    OpenAIRE

    Hilker, Michael

    2008-01-01

    The human immune system protects the human body against various pathogens like e.g. biological viruses and bacteria. Artificial immune systems reuse the architecture, organization, and workflows of the human immune system for various problems in computer science. In the network security, the artificial immune system is used to secure a network and its nodes against intrusions like viruses, worms, and trojans. However, these approaches are far away from production where they are academic proof...

  10. Artificial neural network for identification of a substance from a Mössbauer data bank

    Science.gov (United States)

    Salles, Evandro O. T.; de Souza, P. A.; Garg, V. K.

    1994-12-01

    Mössbauer data and references of the minerals reported in the literature have been stored in a computer. Artificial neutral networks (ANN) were taught with the average values of experimental data of isomer shift quadrupole splitting of known mineral systems (sulphate, sulphide and sulphites, and silicates). Artificial neural networks successfully identified the unknown substance when fed with the new values of isomer shift and quadrupole splitting.

  11. An artificial hormone system for self-organization of networked nodes

    OpenAIRE

    Trumler, Wolfgang; Thiemann, Tobias; Ungerer, Theo

    2006-01-01

    The rising complexity of distributed computer systems give reason to investigate self-organization mechanism to build systems that are self-managing in the sense of Autonomic and Organic Computing. In this paper we propose the Artificial Hormone System (AHS) as a general approach to build self-organizing systems based on networked nodes. The Artificial Hormone System implements a similar information exchange between networked nodes like the human hormone system does between cells. The arti...

  12. Prediction of thermal conductivity of aqueous solution at high pressures by using artificial neural network

    Directory of Open Access Journals (Sweden)

    Amooey Ali Akbar

    2014-01-01

    Full Text Available The objective of this study is to predict thermal conductivity of aqueous solution with artificial neural network (ANN model with three inputs (pressure, temperature and concentration. A feed forward artificial neural network with three neurons in its hidden layer is recommended to predict thermal conductivity and the accuracy of this method evaluated by regression analysis between the predicted and experimental value and it shows desired result.

  13. Simulation and Optimization for Thermally Coupled Distillation Using Artificial Neural Network and Genetic Algorithm

    Institute of Scientific and Technical Information of China (English)

    王延敏; 姚平经

    2003-01-01

    In this paper, a new approach using artificial neural network and genetic algorithm for the optimization of the thermally coupled distillation is presented. Mathematical model can be constructed with artificial neural network based on the simulation results with ASPEN PLUS. Modified genetic algorithm was used to optimize the model. With the proposed model and optimization arithmetic, mathematical model can be calculated, decision variables and target value can be reached automatically and quickly. A practical example is used to demonstrate the algorithm.

  14. Forecasting the Colorado River Discharge Using an Artificial Neural Network (ANN) Approach

    OpenAIRE

    Mehrkesh, Amirhossein; Ahmadi, Maryam

    2014-01-01

    Artificial Neural Network (ANN) based model is a computational approach commonly used for modeling the complex relationships between input and output parameters. Prediction of the flow rate of a river is a requisite for any successful water resource management and river basin planning. In the current survey, the effectiveness of an Artificial Neural Network was examined to predict the Colorado River discharge. In this modeling process, an ANN model was used to relate the discharge of the Colo...

  15. Artificial neural network accurately predicts hepatitis B surface antigen seroclearance.

    Directory of Open Access Journals (Sweden)

    Ming-Hua Zheng

    Full Text Available BACKGROUND & AIMS: Hepatitis B surface antigen (HBsAg seroclearance and seroconversion are regarded as favorable outcomes of chronic hepatitis B (CHB. This study aimed to develop artificial neural networks (ANNs that could accurately predict HBsAg seroclearance or seroconversion on the basis of available serum variables. METHODS: Data from 203 untreated, HBeAg-negative CHB patients with spontaneous HBsAg seroclearance (63 with HBsAg seroconversion, and 203 age- and sex-matched HBeAg-negative controls were analyzed. ANNs and logistic regression models (LRMs were built and tested according to HBsAg seroclearance and seroconversion. Predictive accuracy was assessed with area under the receiver operating characteristic curve (AUROC. RESULTS: Serum quantitative HBsAg (qHBsAg and HBV DNA levels, qHBsAg and HBV DNA reduction were related to HBsAg seroclearance (P<0.001 and were used for ANN/LRM-HBsAg seroclearance building, whereas, qHBsAg reduction was not associated with ANN-HBsAg seroconversion (P = 0.197 and LRM-HBsAg seroconversion was solely based on qHBsAg (P = 0.01. For HBsAg seroclearance, AUROCs of ANN were 0.96, 0.93 and 0.95 for the training, testing and genotype B subgroups respectively. They were significantly higher than those of LRM, qHBsAg and HBV DNA (all P<0.05. Although the performance of ANN-HBsAg seroconversion (AUROC 0.757 was inferior to that for HBsAg seroclearance, it tended to be better than those of LRM, qHBsAg and HBV DNA. CONCLUSIONS: ANN identifies spontaneous HBsAg seroclearance in HBeAg-negative CHB patients with better accuracy, on the basis of easily available serum data. More useful predictors for HBsAg seroconversion are still needed to be explored in the future.

  16. Bacterial DNA Sequence Compression Models Using Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Armando J. Pinho

    2013-08-01

    Full Text Available It is widely accepted that the advances in DNA sequencing techniques have contributed to an unprecedented growth of genomic data. This fact has increased the interest in DNA compression, not only from the information theory and biology points of view, but also from a practical perspective, since such sequences require storage resources. Several compression methods exist, and particularly, those using finite-context models (FCMs have received increasing attention, as they have been proven to effectively compress DNA sequences with low bits-per-base, as well as low encoding/decoding time-per-base. However, the amount of run-time memory required to store high-order finite-context models may become impractical, since a context-order as low as 16 requires a maximum of 17.2 x 109 memory entries. This paper presents a method to reduce such a memory requirement by using a novel application of artificial neural networks (ANN to build such probabilistic models in a compact way and shows how to use them to estimate the probabilities. Such a system was implemented, and its performance compared against state-of-the art compressors, such as XM-DNA (expert model and FCM-Mx (mixture of finite-context models , as well as with general-purpose compressors. Using a combination of order-10 FCM and ANN, similar encoding results to those of FCM, up to order-16, are obtained using only 17 megabytes of memory, whereas the latter, even employing hash-tables, uses several hundreds of megabytes.

  17. Artificial neural network analysis of liquid desiccant dehumidification system

    International Nuclear Information System (INIS)

    The dehumidification process involves simultaneous heat and mass transfer and reliable transfer coefficients are required in order to analyze the system. This has been proved to be difficult and many assumptions are made to simplify the analysis. The present research proposes the use of ANN based model in order to simulate the relationship between inlet and outlet parameters of the dehumidifier. For the analysis, randomly packed dehumidifier with lithium chloride as the liquid desiccant is chosen. A multilayer ANN is used to investigate the performance of dehumidifier. For training ANN models, data is obtained from analytical equations. Eight parameters are used as inputs to the ANN, namely: air and desiccant flow rates, air and desiccant inlet temperatures, air inlet humidity, desiccant inlet concentration, dimensionless temperature ratio, and inlet temperature of the cooling water. The outputs of the ANN are the water condensation rate and the outlet desiccant concentration as well as its temperature. ANN predictions for these parameters are validated well with experimental values available in the literature with R2 value in the range of 0.9251-0.9660. This study shows that liquid desiccant dehumidification system can be alternatively modeled using ANN with a reasonable degree of accuracy. -- Research highlights: → Artificial neural network (ANN) based model is used to simulate the performance of the liquid desiccant dehumidification process. → Three ANNs each with eight inputs and one output have been trained. → Water condensation rate, outlet desiccant concentration and its temperature are predicted. → ANNs predicted parameters are validated well with the experimental results.

  18. Application of Artificial Neural Networks to Complex Groundwater Management Problems

    International Nuclear Information System (INIS)

    As water quantity and quality problems become increasingly severe, accurate prediction and effective management of scarcer water resources will become critical. In this paper, the successful application of artificial neural network (ANN) technology is described for three types of groundwater prediction and management problems. In the first example, an ANN was trained with simulation data from a physically based numerical model to predict head (groundwater elevation) at locations of interest under variable pumping and climate conditions. The ANN achieved a high degree of predictive accuracy, and its derived state-transition equations were embedded into a multiobjective optimization formulation and solved to generate a trade-off curve depicting water supply in relation to contamination risk. In the second and third examples, ANNs were developed with real-world hydrologic and climate data for different hydrogeologic environments. For the second problem, an ANN was developed using data collected for a 5-year, 8-month period to predict heads in a multilayered surficial and limestone aquifer system under variable pumping, state, and climate conditions. Using weekly stress periods, the ANN substantially outperformed a well-calibrated numerical flow model for the 71-day validation period, and provided insights into the effects of climate and pumping on water levels. For the third problem, an ANN was developed with data collected automatically over a 6-week period to predict hourly heads in 11 high-capacity public supply wells tapping a semiconfined bedrock aquifer and subject to large well-interference effects. Using hourly stress periods, the ANN accurately predicted heads for 24-hour periods in all public supply wells. These test cases demonstrate that the ANN technology can solve a variety of complex groundwater management problems and overcome many of the problems and limitations associated with traditional physically based flow models

  19. Artificial neural network based inverse design method for circular sliding slopes

    Institute of Scientific and Technical Information of China (English)

    丁德馨; 张志军

    2004-01-01

    Current design method for circular sliding slopes is not so reasonable that it often results in slope sliding. As a result, artificial neural network (ANN) is used to establish an artificial neural network based inverse design method for circular sliding slopes. A sample set containing 21 successful circular sliding slopes excavated in the past is used to train the network. A test sample of 3 successful circular sliding slopes excavated in the past is used to test the trained network. The test results show that the ANN based inverse design method is valid and can be applied to the design of circular sliding slopes.

  20. Artificial Neural Network Analysis of Sierpinski Gasket Fractal Antenna: A Low Cost Alternative to Experimentation

    Directory of Open Access Journals (Sweden)

    Balwinder S. Dhaliwal

    2013-01-01

    Full Text Available Artificial neural networks due to their general-purpose nature are used to solve problems in diverse fields. Artificial neural networks (ANNs are very useful for fractal antenna analysis as the development of mathematical models of such antennas is very difficult due to complex shapes and geometries. As such empirical approach doing experiments is costly and time consuming, in this paper, application of artificial neural networks analysis is presented taking the Sierpinski gasket fractal antenna as an example. The performance of three different types of networks is evaluated and the best network for this type of applications has been proposed. The comparison of ANN results with experimental results validates that this technique is an alternative to experimental analysis. This low cost method of antenna analysis will be very useful to understand various aspects of fractal antennas.

  1. The Use of Artificial Neural Networks to Assess the Capacity of Transport Measures

    Directory of Open Access Journals (Sweden)

    Duchaczek Artur

    2015-06-01

    Full Text Available In the area of logistics management both managers and engineers rely primarily on proven computational algorithms, for this reason, it is often difficult to convince them to the use of artificial neural networks in solving decision problems. The paper presents the possibilities of using the FANN library in building of a computer application applied in the area of logistics. The possibilities of the component are presented on the example of applications of artificial neural networks to estimate the capacity of transport vehicles based on their dimensions. The example presented in the work was solved with the use of a multi-network Layered Perceptron. The example depicted not only the possibility of using artificial neural networks for solving poorly structured tasks but also practical application of the TFannNetwork component

  2. Next Challenges in Bringing Artificial Immune Systems to Production in Network Security

    CERN Document Server

    Hilker, Michael

    2008-01-01

    The human immune system protects the human body against various pathogens like e.g. biological viruses and bacteria. Artificial immune systems reuse the architecture, organization, and workflows of the human immune system for various problems in computer science. In the network security, the artificial immune system is used to secure a network and its nodes against intrusions like viruses, worms, and trojans. However, these approaches are far away from production where they are academic proof-of-concept implementations or use only a small part to protect against a certain intrusion. This article discusses the required steps to bring artificial immune systems into production in the network security domain. It furthermore figures out the challenges and provides the description and results of the prototype of an artificial immune system, which is SANA called.

  3. Application of artificial neural networks in analysis of CHF experimental data in round tubes

    Institute of Scientific and Technical Information of China (English)

    HUANG Yan-Ping; SHAN Jian-Qiang; CHEN Bing-De; LANG Xue-Mei; JIA Dou-Nan; WANG Xiao-Jun

    2004-01-01

    Artificial neural networks (ANNs) are applied successfully to analyze the critical heat flux (CHF) experimental data from some round tubes in this paper. A set of software adopting artificial neural network method for predicting CHF in round tube and a set of CHF database are gotten. Comparing with common CHF correlations and CHF look-up table, ANN method has stronger ability of allow-wrong and nice robustness. The CHF predicting software adopting artificial neural network technology can improve the predicting accuracy in a wider parameter range,and is easier to update and to use. The artificial neural nefwork method used in this paper can be applied to some similar physical problems.

  4. From Artificial Neural Networks to Emotion Machines with Marvin Minsky

    OpenAIRE

    Jozef Kelemen

    2007-01-01

    In 2007, one among the founders and internationally most recognized leadingpioneers of the field of Artificial Intelligence and Cognitive Science, professor emeritus atMassachusetts Institute of Technology, Marvin Minsky celebrates his 80th anniversary.Exploiting thnis opportunity, the article overviews his contribution to the above mentionedfields (and might be also to some others), and sketches an (obviously incomplet) picture ofthe history of Artificial Intelligence and related disciplines...

  5. From Artificial Neural Networks to Emotion Machines with Marvin Minsky

    Directory of Open Access Journals (Sweden)

    Jozef Kelemen

    2007-12-01

    Full Text Available In 2007, one among the founders and internationally most recognized leadingpioneers of the field of Artificial Intelligence and Cognitive Science, professor emeritus atMassachusetts Institute of Technology, Marvin Minsky celebrates his 80th anniversary.Exploiting thnis opportunity, the article overviews his contribution to the above mentionedfields (and might be also to some others, and sketches an (obviously incomplet picture ofthe history of Artificial Intelligence and related disciplines, and a specific way of doingscience, too.

  6. Probabilistic dynamic deployment of wireless sensor networks by artificial bee colony algorithm.

    Science.gov (United States)

    Ozturk, Celal; Karaboga, Dervis; Gorkemli, Beyza

    2011-01-01

    As the usage and development of wireless sensor networks are increasing, the problems related to these networks are being realized. Dynamic deployment is one of the main topics that directly affect the performance of the wireless sensor networks. In this paper, the artificial bee colony algorithm is applied to the dynamic deployment of stationary and mobile sensor networks to achieve better performance by trying to increase the coverage area of the network. A probabilistic detection model is considered to obtain more realistic results while computing the effectively covered area. Performance of the algorithm is compared with that of the particle swarm optimization algorithm, which is also a swarm based optimization technique and formerly used in wireless sensor network deployment. Results show artificial bee colony algorithm can be preferable in the dynamic deployment of wireless sensor networks. PMID:22163942

  7. Identification of Propionibacteria to the species level using Fourier transform infrared spectroscopy and artificial neural networks.

    Science.gov (United States)

    Dziuba, B

    2013-01-01

    Fourier transform infrared spectroscopy (FTIR) and artificial neural networks (ANN's) were used to identify species of Propionibacteria strains. The aim of the study was to improve the methodology to identify species of Propionibacteria strains, in which the differentiation index D, calculated based on Pearson's correlation and cluster analyses were used to describe the correlation between the Fourier transform infrared spectra and bacteria as molecular systems brought unsatisfactory results. More advanced statistical methods of identification of the FTIR spectra with application of artificial neural networks (ANN's) were used. In this experiment, the FTIR spectra of Propionibacteria strains stored in the library were used to develop artificial neural networks for their identification. Several multilayer perceptrons (MLP) and probabilistic neural networks (PNN) were tested. The practical value of selected artificial neural networks was assessed based on identification results of spectra of 9 reference strains and 28 isolates. To verify results of isolates identification, the PCR based method with the pairs of species-specific primers was used. The use of artificial neural networks in FTIR spectral analyses as the most advanced chemometric method supported correct identification of 93% bacteria of the genus Propionibacterium to the species level.

  8. 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

  9. Application of gray correlation analysis and artificial neural network in rock mass blasting

    Institute of Scientific and Technical Information of China (English)

    ZHU Hong-bing; WU Liang

    2005-01-01

    Studied forecasting and controlling the blasting fragmentation by using artificial neural network for multi-ingredients. At the same time, according to the characteristic of multi-parameters input to network model, the gray correlation theory was employed to find out key factors, which can not only save time of computation and parameters input, but improve the stability of the model.

  10. Application of Artificial Neural Network in Active Vibration Control of Diesel Engine

    Institute of Scientific and Technical Information of China (English)

    SUN Cheng-shun; ZHANG Jian-wu

    2005-01-01

    Artificial Neural Network (ANN) is applied to diesel twostage vibration isolating system and an AVC (Active Vibration Control) system is developed. Both identifier and controller are constructed by three-layer BP neural network. Besides computer simulation, experiment research is carried out on both analog bench and diesel bench. The results of simulation and experiment show a diminished response of vibration.

  11. Improved Quantum Artificial Fish Algorithm Application to Distributed Network Considering Distributed Generation

    Directory of Open Access Journals (Sweden)

    Tingsong Du

    2015-01-01

    Full Text Available An improved quantum artificial fish swarm algorithm (IQAFSA for solving distributed network programming considering distributed generation is proposed in this work. The IQAFSA based on quantum computing which has exponential acceleration for heuristic algorithm uses quantum bits to code artificial fish and quantum revolving gate, preying behavior, and following behavior and variation of quantum artificial fish to update the artificial fish for searching for optimal value. Then, we apply the proposed new algorithm, the quantum artificial fish swarm algorithm (QAFSA, the basic artificial fish swarm algorithm (BAFSA, and the global edition artificial fish swarm algorithm (GAFSA to the simulation experiments for some typical test functions, respectively. The simulation results demonstrate that the proposed algorithm can escape from the local extremum effectively and has higher convergence speed and better accuracy. Finally, applying IQAFSA to distributed network problems and the simulation results for 33-bus radial distribution network system show that IQAFSA can get the minimum power loss after comparing with BAFSA, GAFSA, and QAFSA.

  12. Application of Global Dynamic Reconfiguration in Artificial Neural Network System based on Field Programmable Gate Array

    Institute of Scientific and Technical Information of China (English)

    LI Wei; WANG Wei; MA Yi-mei; WANG Jin-hai

    2008-01-01

    Presented is a global dynamic reconfiguration design of an artificial neural network based on field programmable gate array(FPGA). Discussed are the dynamic reconfiguration principles and methods. Proposed is a global dynamic reconfiguration scheme using Xilinx FPGA and platform flash. Using the revision capabilities of Xilinx XCF32P platform flash, an artificial neural network based on Xilinx XC2V30P Virtex-Ⅱ can be reconfigured dynamically from back propagation(BP) learning algorithms to BP network testing algorithms. The experimental results indicate that the scheme is feasible, and that, using dynamic reconfiguration technology, FPGA resource utilization can be reduced remarkably.

  13. Calibration of a shock wave position sensor using artificial neural networks

    Science.gov (United States)

    Decker, Arthur J.; Weiland, Kenneth E.

    1993-01-01

    This report discusses the calibration of a shock wave position sensor. The position sensor works by using artificial neural networks to map cropped CCD frames of the shadows of the shock wave into the value of the shock wave position. This project was done as a tutorial demonstration of method and feasibility. It used a laboratory shadowgraph, nozzle, and commercial neural network package. The results were quite good, indicating that artificial neural networks can be used efficiently to automate the semi-quantitative applications of flow visualization.

  14. Robust nonlinear autoregressive moving average model parameter estimation using stochastic recurrent artificial neural networks

    DEFF Research Database (Denmark)

    Chon, K H; Hoyer, D; Armoundas, A A;

    1999-01-01

    In this study, we introduce a new approach for estimating linear and nonlinear stochastic autoregressive moving average (ARMA) model parameters, given a corrupt signal, using artificial recurrent neural networks. This new approach is a two-step approach in which the parameters of the deterministic...... part of the stochastic ARMA model are first estimated via a three-layer artificial neural network (deterministic estimation step) and then reestimated using the prediction error as one of the inputs to the artificial neural networks in an iterative algorithm (stochastic estimation step). The prediction...... error is obtained by subtracting the corrupt signal of the estimated ARMA model obtained via the deterministic estimation step from the system output response. We present computer simulation examples to show the efficacy of the proposed stochastic recurrent neural network approach in obtaining accurate...

  15. Reservoir computing as an alternative to traditional artificial neural networks in rainfall-runoff modelling

    Directory of Open Access Journals (Sweden)

    N. J. de Vos

    2012-05-01

    Full Text Available Despite theoretical benefits of recurrent artificial neural networks over their feedforward counterparts, it is still unclear whether the former offer practical advantages as rainfall-runoff models. The main drawback of recurrent networks is the increased complexity of the training procedure due to their architecture. This work uses recently introduced, conceptually simple reservoir computing models for one-day-ahead forecasts on twelve river basins in the Eastern United States, and compares them to a variety of traditional feedforward and recurrent models. Two modifications on the reservoir computing models are made to increase the hydrologically relevant information content of their internal state. The results show that the reservoir computing networks outperform feedforward networks and are competitive with state-of-the-art recurrent networks, across a range of performance measures. This, along with their simplicity and ease of training, suggests that reservoir computing models can be considered promising alternatives to traditional artificial neural networks in rainfall-runoff modelling.

  16. Use of Artificial Neural Network for Predicting the Mechanical Property of Low Carbon Steel

    OpenAIRE

    Somkuwar, Vandana

    2013-01-01

    For product development manufacturers and designers need information about the existing materials and new material and its properties as early as possible. This paper presents a method of predicting the properties of unknown material using artificial neural network. The developed neural network model is employed for simulations of the relationship between mechanical property and the chemical composition of low carbon steel. Simulating and analyzing result shows that network model can effectiv...

  17. Prediction of ferric iron precipitation in bioleaching process using partial least squares and artificial neural network

    OpenAIRE

    Golmohammadi Hassan; Rashidi Abbas; Safdari Seyed Jaber

    2013-01-01

    A quantitative structure-property relationship (QSPR) study based on partial least squares (PLS) and artificial neural network (ANN) was developed for the prediction of ferric iron precipitation in bioleaching process. The leaching temperature, initial pH, oxidation/reduction potential (ORP), ferrous concentration and particle size of ore were used as inputs to the network. The output of the model was ferric iron precipitation. The optimal condition of the neural network was obtained by...

  18. Study of a laboratory-scale froth flotation process using artificial neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Kalyani, V.K.; Pallavika; Chaudhuri, S.; Charan, T.G.; Haldar, D.D.; Kamal, K.P.; Badhe, Y.P.; Tambe, S.S.; Kulkarni, B.D. [IT Centre, Dhanbad (India). Central Mineral Research Institute

    2008-07-01

    A three-layer feed-forward artificial neural network (ANN) model, trained using the error back propagation algorithm, has been established to simulate the froth flotation process for the beneficiation of coal fines. The network model validates the experimentally observed qualitative and quantitative trends. The optimal model parameters in terms of network weights have been estimated and can be used to compute the parameters of the coal flotation process over wide-ranging experimental conditions.

  19. Rule Extraction from Trained Artificial Neural Network Based on Genetic Algorithm

    Institute of Scientific and Technical Information of China (English)

    WANG Wen-jian; ZHANG Li-xia

    2002-01-01

    This paper discusses how to extract symbolic rules from trained artificial neural network (ANN) in domains involving classification using genetic algorithms (GA). Previous methods based on an exhaustive analysis of network connections and output values have already been demonstrated to be intractable in that the scale-up factor increases with the number of nodes and connections in the network.Some experiments explaining effectiveness of the presented method are given as well.

  20. Prediction Model of Soil Nutrients Loss Based on Artificial Neural Network

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    On the basis of Artificial Neural Network theory, a back propagation neural network with one middle layer is building in this paper, and its algorithms is also given, Using this BP network model, study the case of Malian - River basin. The results by calculating show that the solution based on BP algorithms are consis tent with those based multiple-variables linear regression model. They also indicate that BP model in this paper is reasonable and BP algorithms are feasible.

  1. Can artificial neural networks provide an "expert's" view of medical students performances on computer based simulations?

    OpenAIRE

    Stevens, R. H.; K. Najafi

    1992-01-01

    Artificial neural networks were trained to recognize the test selection patterns of students' successful solutions to seven immunology computer based simulations. When new student's test selections were presented to the trained neural network, their problem solutions were correctly classified as successful or non-successful > 90% of the time. Examination of the neural networks output weights after each test selection revealed a progressive increase for the relevant problem suggesting that a s...

  2. The Application of Artificial Neural Networks to Astronomical Classification

    Science.gov (United States)

    Naim, A.

    1995-12-01

    Galaxies are fundamental to the understanding of the structure and evolution of the universe. They contain stars, gas and dust, and serve as an astrophysical laboratory in which physical processes can be examined. In the context of the large scale structure of the universe galaxies can be viewed as test particles. They are bright and therefore visible at very large distances, and also numerous and so can be used to provide reliable statistics. In previous decades the major obstacle to studying the large scale structure of the universe was the relatively sparse data samples, because obtaining large quantities of galaxian images and spectra requires a lot of observing time, and the accumulation of significant data bases was therefore a slow process. This obstacle is in the process of being removed today, with the advent of large-scale surveys (e.g., the APM galaxy survey, the Sloan Digital Sky Survey and the 2 degree Field survey). The new challenge with which the astronomical community is faced is the management and analysis of the forthcoming extragalactic data bases. On top of the obvious need for better hardware to give large storage volumes and quick access, one needs to devise automated tools for data analysis. The sheer volume of the data renders manual analysis impractical. It would be best if one could somehow transfer the knowledge and expertise accumulated over years of painstaking manual analysis to a machine. This thesis is part of an effort to achieve this goal. I borrowed techniques that have proved useful in other fields (e.g., engineering) and applied them to astronomical datasets. The major tool I used was Artificial Neural Networks (ANNs), which was originally conceived as a simplified computational model for the brain. The scope of methods and algorithms referred to as ANNs is quite wide. In particular, a distinction is made between Supervised Learning algorithms and Unsupervised methods. The former put the emphasis on ``teaching'' a machine to do

  3. Learning emergence: adaptive cellular automata façade trained by artificial neural networks

    OpenAIRE

    Skavara, M. M. E.

    2009-01-01

    This thesis looks into the possibilities of controlling the emergent behaviour of Cellular Automata (CA) to achieve specific architectural goals. More explicitly, the objective is to develop a performing, adaptive building facade, which is fed with the history of its achievements and errors, to provide optimum light conditions in buildings’ interiors. To achieve that, an artificial Neural Network (NN) is implemented. However, can an artificial NN cope with the complexity of suc...

  4. Artificial Synapse Network on Inorganic Proton Conductor for Neuromorphic Systems Applications

    OpenAIRE

    Zhu, Li Qiang; Wan, Chang Jin; Guo, Li Qiang; Shi, Yi; Wan, Qing

    2013-01-01

    The basic units in our brain are neurons and each neuron has more than 1000 synapse connections. Synapse is the basic structure for information transfer in an ever-changing manner, and short-term plasticity allows synapses to perform critical computational functions in neural circuits. Therefore the major challenge for the hardware implementation of neuromorphic computation is to develop artificial synapse. Here, in-plane oxide-based artificial synapse network coupled by proton neurotransmitt...

  5. Influence of natural radioactive aerosols on artificial radioactivity detection in the Spanish surveillance networks

    International Nuclear Information System (INIS)

    The device used for continuous measurements of artificial α and β activity in Spanish radiological surveillance networks is the LB BAI 9850 monitor from the Berthold Company. The temporal variation of radon decay product equilibrium introduces a varying background signal in the artificial radioactivity in these monitors. This unwanted background signal can be significantly reduced by analyzing natural radioactive aerosols and their influence on the monitor

  6. On the relationships between generative encodings, regularity, and learning abilities when evolving plastic artificial neural networks.

    Directory of Open Access Journals (Sweden)

    Paul Tonelli

    Full Text Available A major goal of bio-inspired artificial intelligence is to design artificial neural networks with abilities that resemble those of animal nervous systems. It is commonly believed that two keys for evolving nature-like artificial neural networks are (1 the developmental process that links genes to nervous systems, which enables the evolution of large, regular neural networks, and (2 synaptic plasticity, which allows neural networks to change during their lifetime. So far, these two topics have been mainly studied separately. The present paper shows that they are actually deeply connected. Using a simple operant conditioning task and a classic evolutionary algorithm, we compare three ways to encode plastic neural networks: a direct encoding, a developmental encoding inspired by computational neuroscience models, and a developmental encoding inspired by morphogen gradients (similar to HyperNEAT. Our results suggest that using a developmental encoding could improve the learning abilities of evolved, plastic neural networks. Complementary experiments reveal that this result is likely the consequence of the bias of developmental encodings towards regular structures: (1 in our experimental setup, encodings that tend to produce more regular networks yield networks with better general learning abilities; (2 whatever the encoding is, networks that are the more regular are statistically those that have the best learning abilities.

  7. On the relationships between generative encodings, regularity, and learning abilities when evolving plastic artificial neural networks.

    Science.gov (United States)

    Tonelli, Paul; Mouret, Jean-Baptiste

    2013-01-01

    A major goal of bio-inspired artificial intelligence is to design artificial neural networks with abilities that resemble those of animal nervous systems. It is commonly believed that two keys for evolving nature-like artificial neural networks are (1) the developmental process that links genes to nervous systems, which enables the evolution of large, regular neural networks, and (2) synaptic plasticity, which allows neural networks to change during their lifetime. So far, these two topics have been mainly studied separately. The present paper shows that they are actually deeply connected. Using a simple operant conditioning task and a classic evolutionary algorithm, we compare three ways to encode plastic neural networks: a direct encoding, a developmental encoding inspired by computational neuroscience models, and a developmental encoding inspired by morphogen gradients (similar to HyperNEAT). Our results suggest that using a developmental encoding could improve the learning abilities of evolved, plastic neural networks. Complementary experiments reveal that this result is likely the consequence of the bias of developmental encodings towards regular structures: (1) in our experimental setup, encodings that tend to produce more regular networks yield networks with better general learning abilities; (2) whatever the encoding is, networks that are the more regular are statistically those that have the best learning abilities.

  8. An Examination of Application of Artificial Neural Network in Cognitive Radios

    Science.gov (United States)

    Bello Salau, H.; Onwuka, E. N.; Aibinu, A. M.

    2013-12-01

    Recent advancement in software radio technology has led to the development of smart device known as cognitive radio. This type of radio fuses powerful techniques taken from artificial intelligence, game theory, wideband/multiple antenna techniques, information theory and statistical signal processing to create an outstanding dynamic behavior. This cognitive radio is utilized in achieving diverse set of applications such as spectrum sensing, radio parameter adaptation and signal classification. This paper contributes by reviewing different cognitive radio implementation that uses artificial intelligence such as the hidden markov models, metaheuristic algorithm and artificial neural networks (ANNs). Furthermore, different areas of application of ANNs and their performance metrics based approach are also examined.

  9. An Examination of Application of Artificial Neural Network in Cognitive Radios

    International Nuclear Information System (INIS)

    Recent advancement in software radio technology has led to the development of smart device known as cognitive radio. This type of radio fuses powerful techniques taken from artificial intelligence, game theory, wideband/multiple antenna techniques, information theory and statistical signal processing to create an outstanding dynamic behavior. This cognitive radio is utilized in achieving diverse set of applications such as spectrum sensing, radio parameter adaptation and signal classification. This paper contributes by reviewing different cognitive radio implementation that uses artificial intelligence such as the hidden markov models, metaheuristic algorithm and artificial neural networks (ANNs). Furthermore, different areas of application of ANNs and their performance metrics based approach are also examined

  10. Early detection of system malfunctions in NPPs using artificial neural network based classification

    International Nuclear Information System (INIS)

    Artificial neural networks seem to be a promising tool to perform classification. To do it, the network is trained in a proper way, i.e. both normal and abnormal signals are shown to the network. Using a large enough sample set, after the training period the network is able to distinguish normal and abnormal signals. When a new signal is shown, a network assigns it to either normal or abnormal class, using the information inherently extracted from the sample set. The method is under development but early and promising results indicate the applicability of the tool in real situations. (author)

  11. Artificial Immune Danger Theory Based Model for Network Security Evaluation

    Directory of Open Access Journals (Sweden)

    Feixian Sun

    2011-02-01

    Full Text Available Inspired by the principles of immune danger theory, a danger theory based model for network security risk assessment is presented in this paper. Firstly, the principle of the danger theory is introduced. And then, with the improved concepts and formal definitions of antigen, antibody, danger signal, and detection lymphocyte for network security risk assessment presented, the distributed architecture of the proposed model is described. Following that, the principle of network intrusion detection is expounded. Finally, the method of network security risk assessment is given. Theoretical analysis and simulation results show that the proposed model can evaluate the network attack threats in real time. Thus, it provides an effective risk evaluation solution to network security.

  12. A Comparative Analysis of Community Detection Algorithms on Artificial Networks

    CERN Document Server

    Yang, Zhao; Tessone, Claudio Juan

    2016-01-01

    Many community detection algorithms have been developed to uncover the mesoscopic properties of complex networks. However how good an algorithm is, in terms of accuracy and computing time, remains still open. Testing algorithms on real-world network has certain restrictions which made their insights potentially biased: the networks are usually small, and the underlying communities are not defined objectively. In this study, we employ the Lancichinetti-Fortunato-Radicchi benchmark graph to test eight state-of-the-art algorithms. We quantify the accuracy using complementary measures and algorithms' computing time. Based on simple network properties and the aforementioned results, we provide guidelines that help to choose the most adequate community detection algorithm for a given network. Moreover, these rules allow uncovering limitations in the use of specific algorithms given macroscopic network properties. Our contribution is threefold: firstly, we provide actual techniques to determine which is the most sui...

  13. Data assimilation: Particle filter and artificial neural networks

    International Nuclear Information System (INIS)

    The goal of this work is to present the performance of the Neural Network Multilayer Perceptrons trained to emulate a Particle Filter in the context of data assimilation. Techniques for data assimilation are applied for the Lorenz system, which presents a strong nonlinearity and chaotic nature. The cross validation method was used for training the network. Good results were obtained applying the multilayer perceptrons neural network.

  14. Double Glow Plasma Surface Alloying Process Modeling Using Artificial Neural Networks

    Institute of Scientific and Technical Information of China (English)

    Jiang XU; Xishan XIE; Zhong XU

    2003-01-01

    A model is developed for predicting the correlation between processing parameters and the technical target of double glowby applying artificial neural network (ANN). The input parameters of the neural network (NN) are source voltage, workpiecevoltage, working pressure and distance between source electrode and workpiece. The output of the NN model is three importanttechnical targets, namely the gross element content, the thickness of surface alloying layer and the absorption rate (the ratioof the mass loss of source materials to the increasing mass of workpiece) in the processing of double glow plasma surfacealloying. The processing parameters and technical target are then used as a training set for an artificial neural network. Themodel is based on multiplayer feedforward neural network. A very good performance of the neural network is achieved and thecalculated results are in good agreement with the experimental ones.

  15. Parallel and Mixed Hardware Implementation of Artificial Neuron Network on the FPGA Platform

    Directory of Open Access Journals (Sweden)

    ATIBI Mohamed

    2014-10-01

    Full Text Available Most applications in different fields (automotive, robotics, medical… take advantage of the proven performance by artificial neural networks to solve their most complex problems. The architecture chosen for implementation is the multilayer perceptron that uses retro propagation as a learning algorithm. This article presents modular hardware implementation of multilayer perceptron architecture of artificial neuron network ‘ANN’, in the FPGA platform according to two models (parallel and mixed hardware implementation, and the comparison between these two implementations in terms of hardware resources and execution time. The two implementations are based on the proposed module of a formal neuron with the sigmoid activation function.

  16. Kinematic Analysis of 3-DOF Planer Robot Using Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Jolly Atit Shah

    2012-07-01

    Full Text Available Automatic control of the robotic manipulator involves study of kinematics and dynamics as a major issue. This paper involves the forward and inverse kinematics of 3-DOF robotic manipulator with revolute joints. In this study the Denavit- Hartenberg (D-H model is used to model robot links and joints. Also forward and inverse kinematics solution has been achieved using Artificial Neural Networks for 3-DOF robotic manipulator. It shows that by using artificial neural network the solution we get is faster, acceptable and has zero error.

  17. Artificial intelligence. Application of the Statistical Neural Networks computer program in nuclear medicine

    International Nuclear Information System (INIS)

    Artificial Neural Networks (ANN) may be a tool alternative and complementary to typical statistical analysis. However, in spite of many computer application of various ANN algorithms ready for use, artificial intelligence is relatively rarely applied to data processing. In this paper practical aspects of scientific application of ANN in medicine using the Statistical Neural Networks Computer program, were presented. Several steps of data analysis with the above ANN software package were discussed shortly, from material selection and its dividing into groups to the types of obtained results. The typical problems connected with assessing scintigrams by ANN were also described. (author)

  18. Granite wash completion optimization with the aid of artificial neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Shelley, R.; Scheuerman, P.; McRill, P. E.; Hamilton, R. [Halliburton Energy Services Inc., (United States); Massengill, D. R. [Pioneer Natural Resources, USA (United States)

    1998-12-31

    Granite wash completions in the Red Deer Creek Field in Texas were analyzed using an artificial neural network. Particular attention was focused on quantifiable aspects of a well`s completion and stimulation procedure, including fluid selection, treatment volume, proppant type and volume, pump rates, and perforation distribution. Results showed that the application of artificial neural network technology resulted in a two-fold increase in gas production from granite wash completion in the Red Deer Creek area. The methodology was documented and compared to conventional completion optimization techniques. 8 refs., 3 tabs., 3 figs.

  19. A NEW METHOD FOR STABILITY ANALYSIS OF UNDERGROUND OPENING USING ARTIFICIAL NEURAL NETWORK

    Institute of Scientific and Technical Information of China (English)

    杨英杰; 张清

    1996-01-01

    The stability of underground opening is strongly constrained by a variety of factors.These include initial stress, structure of rock mass, and underground water, etc. A new approach proposed in this paper aims at revealing the degree of significance of them in order to catch the key factors. This new approach utilizes the mapping potentiality of artificial neural network and works out the sophisticated interactions among those factors from sample set. As a demonstration, an example is given for the application of this method to an underground opening. All results of this paper prove the efficiency of artificial neural network in stability analysis of underground opening.

  20. A Comparative Analysis of Community Detection Algorithms on Artificial Networks

    Science.gov (United States)

    Yang, Zhao; Algesheimer, René; Tessone, Claudio J.

    2016-08-01

    Many community detection algorithms have been developed to uncover the mesoscopic properties of complex networks. However how good an algorithm is, in terms of accuracy and computing time, remains still open. Testing algorithms on real-world network has certain restrictions which made their insights potentially biased: the networks are usually small, and the underlying communities are not defined objectively. In this study, we employ the Lancichinetti-Fortunato-Radicchi benchmark graph to test eight state-of-the-art algorithms. We quantify the accuracy using complementary measures and algorithms’ computing time. Based on simple network properties and the aforementioned results, we provide guidelines that help to choose the most adequate community detection algorithm for a given network. Moreover, these rules allow uncovering limitations in the use of specific algorithms given macroscopic network properties. Our contribution is threefold: firstly, we provide actual techniques to determine which is the most suited algorithm in most circumstances based on observable properties of the network under consideration. Secondly, we use the mixing parameter as an easily measurable indicator of finding the ranges of reliability of the different algorithms. Finally, we study the dependency with network size focusing on both the algorithm’s predicting power and the effective computing time.

  1. Artificial intelligence based event detection in wireless sensor networks

    NARCIS (Netherlands)

    Bahrepour, Majid

    2013-01-01

    Wireless sensor networks (WSNs) are composed of large number of small, inexpensive devices, called sensor nodes, which are equipped with sensing, processing, and communication capabilities. While traditional applications of wireless sensor networks focused on periodic monitoring, the focus of more r

  2. A Comparative Analysis of Community Detection Algorithms on Artificial Networks

    Science.gov (United States)

    Yang, Zhao; Algesheimer, René; Tessone, Claudio J.

    2016-01-01

    Many community detection algorithms have been developed to uncover the mesoscopic properties of complex networks. However how good an algorithm is, in terms of accuracy and computing time, remains still open. Testing algorithms on real-world network has certain restrictions which made their insights potentially biased: the networks are usually small, and the underlying communities are not defined objectively. In this study, we employ the Lancichinetti-Fortunato-Radicchi benchmark graph to test eight state-of-the-art algorithms. We quantify the accuracy using complementary measures and algorithms’ computing time. Based on simple network properties and the aforementioned results, we provide guidelines that help to choose the most adequate community detection algorithm for a given network. Moreover, these rules allow uncovering limitations in the use of specific algorithms given macroscopic network properties. Our contribution is threefold: firstly, we provide actual techniques to determine which is the most suited algorithm in most circumstances based on observable properties of the network under consideration. Secondly, we use the mixing parameter as an easily measurable indicator of finding the ranges of reliability of the different algorithms. Finally, we study the dependency with network size focusing on both the algorithm’s predicting power and the effective computing time. PMID:27476470

  3. Handwritten Farsi Character Recognition using Artificial Neural Network

    CERN Document Server

    Ahangar, Reza Gharoie

    2009-01-01

    Neural Networks are being used for character recognition from last many years but most of the work was confined to English character recognition. Till date, a very little work has been reported for Handwritten Farsi Character recognition. In this paper, we have made an attempt to recognize handwritten Farsi characters by using a multilayer perceptron with one hidden layer. The error backpropagation algorithm has been used to train the MLP network. In addition, an analysis has been carried out to determine the number of hidden nodes to achieve high performance of backpropagation network in the recognition of handwritten Farsi characters. The system has been trained using several different forms of handwriting provided by both male and female participants of different age groups. Finally, this rigorous training results an automatic HCR system using MLP network. In this work, the experiments were carried out on two hundred fifty samples of five writers. The results showed that the MLP networks trained by the err...

  4. Development of Improved Artificial Neural Network Model for Stock Market Prediction

    Directory of Open Access Journals (Sweden)

    PRATAP KISHORE PADHIARY

    2011-02-01

    Full Text Available In recent year’s prediction of stock market returns is a hottest field of research in finance. Artificial Neural Network (ANN is a technique that is heavily researched and widely used in applications for engineering and scientific fields for various purposes ranging from control systems to artificial intelligence. This paper surveys key issues in financial forecasting and propose an ANN methodologywhich could be better for long term (one month, two month as well as short term (one day prediction of stock price of any leading stock market indices. Survey of existing literature reveals that adaptivelearning rate will give more accurate result than fixed learning rate parameter for ANN models. Many researchers noted that slight parameter changed causes major variations in the behavior of the network. So there is no theory which could be guideline for finding best network topology. The proposed trigonometric functional link artificial neural network (FLANN model employs standard least meansquare (LMS algorithm with search-then-converge scheduling which could effectively calculate learning rate parameter that changes with time and may require less experiments to train the model. The objective of this paper is to introduce a functional link single layer artificial neural network (FLANN for long term as well as short term stock market prediction.

  5. Natural and artificial intelligence misconceptions about brains and neural networks

    CERN Document Server

    de Callataÿ, A

    1992-01-01

    How does the mind work? How is data stored in the brain? How does the mental world connect with the physical world? The hybrid system developed in this book shows a radically new view on the brain. Briefly, in this model memory remains permanent by changing the homeostasis rebuilding the neuronal organelles. These transformations are approximately abstracted as all-or-none operations. Thus the computer-like neural systems become plausible biological models. This illustrated book shows how artificial animals with such brains learn invariant methods of behavior control from their repeated action

  6. Evaluation on Stability of Stope Structure Based on Nonlinear Dynamics of Coupling Artificial Neural Network

    Institute of Scientific and Technical Information of China (English)

    2002-01-01

    The nonlinear dynamical behaviors of artificial neural network (ANN) and their application to science and engineering were summarized. The mechanism of two kinds of dynamical processes, i.e. weight dynamics and activation dynamics in neural networks, and the stability of computing in structural analysis and design were stated briefly. It was successfully applied to nonlinear neural network to evaluate the stability of underground stope structure in a gold mine. With the application of BP network, it is proven that the neuro-computing is a practical and advanced tool for solving large-scale underground rock engineering problems.

  7. Vein matching using artificial neural network in vein authentication systems

    Science.gov (United States)

    Noori Hoshyar, Azadeh; Sulaiman, Riza

    2011-10-01

    Personal identification technology as security systems is developing rapidly. Traditional authentication modes like key; password; card are not safe enough because they could be stolen or easily forgotten. Biometric as developed technology has been applied to a wide range of systems. According to different researchers, vein biometric is a good candidate among other biometric traits such as fingerprint, hand geometry, voice, DNA and etc for authentication systems. Vein authentication systems can be designed by different methodologies. All the methodologies consist of matching stage which is too important for final verification of the system. Neural Network is an effective methodology for matching and recognizing individuals in authentication systems. Therefore, this paper explains and implements the Neural Network methodology for finger vein authentication system. Neural Network is trained in Matlab to match the vein features of authentication system. The Network simulation shows the quality of matching as 95% which is a good performance for authentication system matching.

  8. RRAM-based hardware implementations of artificial neural networks: progress update and challenges ahead

    Science.gov (United States)

    Prezioso, M.; Merrikh-Bayat, F.; Chakrabarti, B.; Strukov, D.

    2016-02-01

    Artificial neural networks have been receiving increasing attention due to their superior performance in many information processing tasks. Typically, scaling up the size of the network results in better performance and richer functionality. However, large neural networks are challenging to implement in software and customized hardware are generally required for their practical implementations. In this work, we will discuss our group's recent efforts on the development of such custom hardware circuits, based on hybrid CMOS/memristor circuits, in particular of CMOL variety. We will start by reviewing the basics of memristive devices and of CMOL circuits. We will then discuss our recent progress towards demonstration of hybrid circuits, focusing on the experimental and theoretical results for artificial neural networks based on crossbarintegrated metal oxide memristors. We will conclude presentation with the discussion of the remaining challenges and the most pressing research needs.

  9. Modeling and prediction of Turkey's electricity consumption using Artificial Neural Networks

    Energy Technology Data Exchange (ETDEWEB)

    Kavaklioglu, Kadir; Ozturk, Harun Kemal; Canyurt, Olcay Ersel [Pamukkale University, Mechanical Engineering Department, Denizli (Turkey); Ceylan, Halim [Pamukkale University, Civil Engineering Department, Denizli (Turkey)

    2009-11-15

    Artificial Neural Networks are proposed to model and predict electricity consumption of Turkey. Multi layer perceptron with backpropagation training algorithm is used as the neural network topology. Tangent-sigmoid and pure-linear transfer functions are selected in the hidden and output layer processing elements, respectively. These input-output network models are a result of relationships that exist among electricity consumption and several other socioeconomic variables. Electricity consumption is modeled as a function of economic indicators such as population, gross national product, imports and exports. It is also modeled using export-import ratio and time input only. Performance comparison among different models is made based on absolute and percentage mean square error. Electricity consumption of Turkey is predicted until 2027 using data from 1975 to 2006 along with other economic indicators. The results show that electricity consumption can be modeled using Artificial Neural Networks, and the models can be used to predict future electricity consumption. (author)

  10. Prediction of operational parameters effect on coal flotation using artificial neural network

    Institute of Scientific and Technical Information of China (English)

    E. Jorjani; Sh. Mesroghli; S. Chehreh Chelgani

    2008-01-01

    Artificial neural network procedures were used to predict the combustible value (i.e. 100-Ash) and combustible recovery of coal flotation concentrate in different operational conditions. The pulp density, pH, rotation rate, coal particle size, dosage of collector, frother and conditioner were used as inputs to the network. Feed-forward artificial neural networks with 5-30-2-1 and 7-10-3-1 arrangements were capable to estimate the combustible value and combustible recovery of coal flotation concentrate respectively as the outputs. Quite satisfactory correlations of 1 and 0.91 in training and testing stages for combustible value and of 1 and 0.95 in training and testing stages for combustible recovery prediction were achieved. The proposed neural network models can be used to determine the most advantageous operational conditions for the expected concentrate assay and recovery in the coal flotation process.

  11. Practicability study on the suitability of artificial, neural networks for the approximation of unknown steering torques

    Science.gov (United States)

    Van Ende, K. T. R.; Schaare, D.; Kaste, J.; Küçükay, F.; Henze, R.; Kallmeyer, F. K.

    2016-10-01

    For steer-by-wire systems, the steering feedback must be generated artificially due to the system characteristics. Classical control concepts require operating-point driven optimisations as well as increased calibration efforts in order to adequately simulate the steering torque in all driving states. Artificial neural networks (ANNs) are an innovative control concept; they are capable of learning arbitrary non-linear correlations without complex knowledge of physical dependencies. The present study investigates the suitability of neural networks for approximating unknown steering torques. To ensure robust processing of arbitrary data, network training with a sufficient volume of training data is required, that represents the relation between the input and target values in a wide range. The data were recorded in the course of various test drives. In this research, a variety of network topologies were trained, analysed and evaluated. Though the fundamental suitability of ANNs for the present control task was demonstrated.

  12. APPLICATION OF ARTIFICIAL NEURAL NETWORKS FOR PREDICTION OF AIR POLLUTION LEVELS IN ENVIRONMENTAL MONITORING

    Directory of Open Access Journals (Sweden)

    Małgorzata Pawul

    2016-09-01

    Full Text Available Recently, a lot of attention was paid to the improvement of methods which are used to air quality forecasting. Artificial neural networks can be applied to model these problems. Their advantage is that they can solve the problem in the conditions of incomplete information, without the knowledge of the analytical relationship between the input and output data. In this paper we applied artificial neural networks to predict the PM 10 concentrations as factors determining the occurrence of smog phenomena. To create these networks we used meteorological data and concentrations of PM 10. The data were recorded in 2014 and 2015 at three measuring stations operating in Krakow under the State Environmental Monitoring. The best results were obtained by three-layer perceptron with back-propagation algorithm. The neural networks received a good fit in all cases.

  13. Modeling of mass transfer of Phospholipids in separation process with supercritical CO2 fluid by RBF artificial neural networks

    Science.gov (United States)

    An artificial Radial Basis Function (RBF) neural network model was developed for the prediction of mass transfer of the phospholipids from canola meal in supercritical CO2 fluid. The RBF kind of artificial neural networks (ANN) with orthogonal least squares (OLS) learning algorithm were used for mod...

  14. Quantitative analysis of cefalexin based on artificial neural networks combined with modified genetic algorithm using short near-infrared spectroscopy

    Science.gov (United States)

    Huan, Yanfu; Feng, Guodong; Wang, Bin; Ren, Yulin; Fei, Qiang

    2013-05-01

    In this paper, a novel chemometric method was developed for rapid, accurate, and quantitative analysis of cefalexin in samples. The experiments were carried out by using the short near-infrared spectroscopy coupled with artificial neural networks. In order to enhancing the predictive ability of artificial neural networks model, a modified genetic algorithm was used to select fixed number of wavelength.

  15. Src64 controls a novel actin network required for proper ring canal formation in the Drosophila male germline.

    Science.gov (United States)

    Eikenes, Åsmund Husabø; Malerød, Lene; Lie-Jensen, Anette; Sem Wegner, Catherine; Brech, Andreas; Liestøl, Knut; Stenmark, Harald; Haglund, Kaisa

    2015-12-01

    In many organisms, germ cells develop as cysts in which cells are interconnected via ring canals (RCs) as a result of incomplete cytokinesis. However, the molecular mechanisms of incomplete cytokinesis remain poorly understood. Here, we address the role of tyrosine phosphorylation of RCs in the Drosophila male germline. We uncover a hierarchy of tyrosine phosphorylation within germline cysts that positively correlates with RC age. The kinase Src64 is responsible for mediating RC tyrosine phosphorylation, and loss of Src64 causes a reduction in RC diameter within germline cysts. Mechanistically, we show that Src64 controls an actin network around the RCs that depends on Abl and the Rac/SCAR/Arp2/3 pathway. The actin network around RCs is required for correct RC diameter in cysts of developing germ cells. We also identify that Src64 is required for proper germ cell differentiation in the Drosophila male germline independent of its role in RC regulation. In summary, we report that Src64 controls actin dynamics to mediate proper RC formation during incomplete cytokinesis during germline cyst development in vivo.

  16. Artificial neural network based pulse shape analysis in cryogenic detectors for rare event searches

    International Nuclear Information System (INIS)

    We present a method based on an Artificial Neural Network for a pulse shape analysis in cryogenic detectors. To train the neural network a huge amount of pulses with known properties are necessary. Therefore, a data-driven simulation used to generate these sets is explained. Furthermore, these simulations allow detailed studies, especially of the cut efficiency and the signal purity of the developed cut. First results are presented and compared with the performance of alternative algorithms.

  17. Time series forecasting by evolving artificial neural networks using genetic algorithms and estimation of distribution algorithms

    OpenAIRE

    Peralta, Juan; Gutiérrez, Germán; Sanchis, Araceli

    2010-01-01

    Accurate time series forecasting are important for displaying the manner in which the past continues to affect the future and for planning our day to-day activities. In recent years, a large literature has evolved on the use of evolving artificial neural networks (EANNs) in many forecasting applications. Evolving neural networks are particularly appealing because of their ability to model an unspecified nonlinear relationship between time series variables. This paper evaluates two methods to ...

  18. Application of artificial neural network in market segmentation: A review on recent trends

    OpenAIRE

    Chattopadhyay, Manojit; Dan, Pranab K; Majumdar, Sitanath; Chakraborty, Partha Sarathi

    2012-01-01

    Despite the significance of Artificial Neural Network (ANN) algorithm to market segmentation, there is a need of a comprehensive literature review and a classification system for it towards identification of future trend of market segmentation research. The present work is the first identifiable academic literature review of the application of neural network based techniques to segmentation. Our study has provided an academic database of literature between the periods of 2000-2010 and propose...

  19. Viscosity of carbon nanotube suspension using artificial neural networks with principal component analysis

    Science.gov (United States)

    Yousefi, Fakhri; Karimi, Hajir; Mohammadiyan, Somayeh

    2016-11-01

    This paper applies the model including back-propagation network (BPN) and principal component analysis (PCA) to estimate the effective viscosity of carbon nanotubes suspension. The effective viscosities of multiwall carbon nanotubes suspension are examined as a function of the temperature, nanoparticle volume fraction, effective length of nanoparticle and the viscosity of base fluids using artificial neural network. The obtained results by BPN-PCA model have good agreement with the experimental data.

  20. Predicting Students' Academic Performance Using Artificial Neural Networks: A Case Study

    OpenAIRE

    Ghaleb A. El-Refae; Qeethara Kadhim Al-Shayea

    2010-01-01

    Predicting students’ academic performance is critical for universities because strategic programs can be planned in improving or maintaining students’ performance. The goal of this study is to predict the factors affecting the university students' performance using Artificial Neural Networks (ANN) model. Various factors that may likely influence the performance of a student were identified. Generalized Regression Neural Network (GRNN) is used to predict the university students' performance. I...

  1. Modeling Slump of Ready Mix Concrete Using Genetically Evolved Artificial Neural Networks

    OpenAIRE

    Vinay Chandwani; Vinay Agrawal; Ravindra Nagar

    2014-01-01

    Artificial neural networks (ANNs) have been the preferred choice for modeling the complex and nonlinear material behavior where conventional mathematical approaches do not yield the desired accuracy and predictability. Despite their popularity as a universal function approximator and wide range of applications, no specific rules for deciding the architecture of neural networks catering to a specific modeling task have been formulated. The research paper presents a methodology for automated de...

  2. Prediction of the Performance of the Fabrics in Garment Manufacturing by Artificial Neural Network

    Institute of Scientific and Technical Information of China (English)

    LIU Kan; ZHANG Wei-yuan

    2004-01-01

    An artificial neural network is used to predict the performance of fabrics in clothing manufacturing. The predictions are based on fabric mechanical properties measured on the FAST system. The influences of the different ANN's construct on the convergence speed and the prediction accuracy are investigated. The result indicates that the BP neural network is an efficiency technique and has a wide prospect in the application to garment processing.

  3. Artificial neural network based pulse shape analysis in cryogenic detectors for rare event searches

    Energy Technology Data Exchange (ETDEWEB)

    Zoeller, Andreas [Physik Department E15, Technische Universitaet Muenchen, 85748 Garching (Germany); Collaboration: CRESST-Collaboration

    2015-07-01

    We present a method based on an Artificial Neural Network for a pulse shape analysis in cryogenic detectors. To train the neural network a huge amount of pulses with known properties are necessary. Therefore, a data-driven simulation used to generate these sets is explained. Furthermore, these simulations allow detailed studies, especially of the cut efficiency and the signal purity of the developed cut. First results are presented and compared with the performance of alternative algorithms.

  4. Ethanol production from steam exploded rapeseed straw and the process simulation using artificial neural networks

    DEFF Research Database (Denmark)

    Talebnia, Farid; Mighani, Moein; Rahimnejad, Mostafa;

    2015-01-01

    and 67% of maximum theoretical value. Next, data of the experimental runs were exploited for modeling the processes by artificial neural networks (ANNs) and performance of the developed models was evaluated. The ANN-based models showed a great potential for time-course prediction of the studied processes....... Efficiency of the joint network for simulating the whole process was also determined and promising results were obtained....

  5. Prediction of jominy hardness profiles of steels using artificial neural networks

    Science.gov (United States)

    Vermeulen, W. G.; van der Wolk, P. J.; de Weijer, A. P.; van der Zwaag, S.

    1996-02-01

    Jominy hardness profiles of steels were predicted from chemical composition and austenitizing temperature using an artificial neural network. The neural network was trained using some 4000 examples, covering a wide range of steel compositions. The performance of the neural network is examined as a function of the network architecture, the number of alloying elements, and the number of data sets used for training. A well-trained network predicts the Jominy hardness profile with an average error of about 2 HRC. Special attention was devoted to the effect of boron on hardenability. A network trained using data only from boron steels produced results similar to those of a network trained using all data available. The accuracy of the predictions of the model is compared with that of an analytical model for hardenability and with that of a partial least- squares model using the same set of data.

  6. Automatic event detection based on artificial neural networks

    Science.gov (United States)

    Doubravová, Jana; Wiszniowski, Jan; Horálek, Josef

    2015-04-01

    The proposed algorithm was developed to be used for Webnet, a local seismic network in West Bohemia. The Webnet network was built to monitor West Bohemia/Vogtland swarm area. During the earthquake swarms there is a large number of events which must be evaluated automatically to get a quick estimate of the current earthquake activity. Our focus is to get good automatic results prior to precise manual processing. With automatic data processing we may also reach a lower completeness magnitude. The first step of automatic seismic data processing is the detection of events. To get a good detection performance we require low number of false detections as well as high number of correctly detected events. We used a single layer recurrent neural network (SLRNN) trained by manual detections from swarms in West Bohemia in the past years. As inputs of the SLRNN we use STA/LTA of half-octave filter bank fed by vertical and horizontal components of seismograms. All stations were trained together to obtain the same network with the same neuron weights. We tried several architectures - different number of neurons - and different starting points for training. Networks giving the best results for training set must not be the optimal ones for unknown waveforms. Therefore we test each network on test set from different swarm (but still with similar characteristics, i.e. location, focal mechanisms, magnitude range). We also apply a coincidence verification for each event. It means that we can lower the number of false detections by rejecting events on one station only and force to declare an event on all stations in the network by coincidence on two or more stations. In further work we would like to retrain the network for each station individually so each station will have its own coefficients (neural weights) set. We would also like to apply this method to data from Reykjanet network located in Reykjanes peninsula, Iceland. As soon as we have a reliable detection, we can proceed to

  7. Prediction of Gas Holdup in Bubble Columns Using Artificial Neural Network

    Institute of Scientific and Technical Information of China (English)

    吴元欣; 罗湘华; 陈启明; 李定或; 李世荣; M.H.Al-Dahhan; M.P.Dudukovic

    2003-01-01

    A new correlation for the prediction of gas hod up in bubble columns was proposed based on an extensive experimental database set up from the literature published over last 30 years .The updated estimation method relying on artificial neural network,dimensional analysis and phenomenological approaches was used and the model prediction agreed with the experimental data with average relative error less than 10%.

  8. Motion Planning Of an Autonomous Mobile Robot Using Artificial Neural Network

    OpenAIRE

    Tripathi, G. N.; Rihani, V.

    2012-01-01

    The paper presents the electronic design and motion planning of a robot based on decision making regarding its straight motion and precise turn using Artificial Neural Network (ANN). The ANN helps in learning of robot so that it performs motion autonomously. The weights calculated are implemented in microcontroller. The performance has been tested to be excellent.

  9. Getting emotional with evolutionary simulations: the origin of affective processing in artificial neural networks

    NARCIS (Netherlands)

    B.T. Heerebout

    2011-01-01

    The main purpose of the present thesis was to investigate the evolutionary roots of basic affective processes and their underlying neural mechanisms. To this end, simulations were performed with agents that evolved artificial neural networks. Our general working hypothesis was that positive and nega

  10. STUDY ON THERMODYNAMIC MODEL OF A COMPRESSOR WITH ARTIFICIAL NEURAL NETWORKS

    Institute of Scientific and Technical Information of China (English)

    1999-01-01

    A new compressor thermodynamic model is set up. Artificial neural networks(ANN) which have self-adjusting functions are adopted to calculate volumetric efficiency and electrical efficiency of a compressor. The new compressor model composed of the theoretical model and ANN reaches more precise results than traditional ones. Furthermore, the new compressor model is of better flexibility in a large scale.

  11. Prediction of Superconductivity for Oxides Based on Structural Parameters and Artificial Neural Network Method

    Institute of Scientific and Technical Information of China (English)

    2000-01-01

    Superconductive properties for oxides were predicted by artificial neural network (ANN) method with structural and chemical parameters as inputs. The predicted properties include superconductivity for oxides, distributed ranges of the superconductive transition temperature (Tc) for complex oxides, and Tc values for cuprate superconductors. The calculated results indicated that the adjusted ANN can be used to predict superconductive properties for unknown oxides.

  12. Condition Monitoring for DC-link Capacitors Based on Artificial Neural Network Algorithm

    DEFF Research Database (Denmark)

    Soliman, Hammam Abdelaal Hammam; Wang, Huai; Gadalla, Brwene Salah Abdelkarim;

    2015-01-01

    hardware will reduce the cost, and therefore could be more promising for industry applications. A condition monitoring method based on Artificial Neural Network (ANN) algorithm is therefore proposed in this paper. The implementation of the ANN to the DC-link capacitor condition monitoring in a back...

  13. Artificial Neural Networks for Reducing Computational Effort in Active Truncated Model Testing of Mooring Lines

    DEFF Research Database (Denmark)

    Christiansen, Niels Hørbye; Voie, Per Erlend Torbergsen; Høgsberg, Jan Becker

    2015-01-01

    simultaneously, this method is very demanding in terms of numerical efficiency and computational power. Therefore, this method has not yet proved to be feasible. It has recently been shown how a hybrid method combining classical numerical models and artificial neural networks (ANN) can provide a dramatic...

  14. Wind Turbine Fault Detection based on Artificial Neural Network Analysis of SCADA Data

    DEFF Research Database (Denmark)

    Herp, Jürgen; S. Nadimi, Esmaeil

    2015-01-01

    Slowly developing faults in wind turbine can, when not detected and fixed on time, cause severe damage and downtime. We are proposing a fault detection method based on Artificial Neural Networks (ANN) and the recordings from Supervisory Control and Data Acquisition (SCADA) systems installed in wind...

  15. Artificial Neural Network Algorithm for Condition Monitoring of DC-link Capacitors Based on Capacitance Estimation

    DEFF Research Database (Denmark)

    Soliman, Hammam Abdelaal Hammam; Wang, Huai; Gadalla, Brwene Salah Abdelkarim;

    2015-01-01

    challenges. A capacitance estimation method based on Artificial Neural Network (ANN) algorithm is therefore proposed in this paper. The implemented ANN estimated the capacitance of the DC-link capacitor in a back-toback converter. Analysis of the error of the capacitance estimation is also given...

  16. 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...

  17. RBF-Type Artificial Neural Network Model Applied in Alloy Design of Steels

    Institute of Scientific and Technical Information of China (English)

    YOU Wei; LIU Ya-xiu; BAI Bing-zhe; FANG Hong-sheng

    2008-01-01

    RBF model, a new type of artificial neural network model was developed to design the content of carbon in low-alloy engineering steels. The errors of the ANN model are. MSE 0. 052 1, MSRE 17. 85%, and VOF 1. 932 9. The results obtained are satisfactory. The method is a powerful aid for designing new steels.

  18. Artificial neural networks to forecast biomass of Pacific sardine and its environment

    DEFF Research Database (Denmark)

    Cisneros Mata, M.A.; Brey, T.; Jarre, Astrid;

    1996-01-01

    We tested the forecasting performance of artificial neural networks (ANNs) using several time series of environmental and biotic data pertaining to the California Current (CC) neritic ecosystem. ANNs performed well predicting CC monthly 10-m depth temperature up to nine years in advance, using te...

  19. Application of Artificial Neural Network to Predicting Hardenability of Gear Steel

    Institute of Scientific and Technical Information of China (English)

    GAO Xiu-hua; QI Ke-min; DENG Tian-yong; QIU Chun-lin; ZHOU Ping; DU Xian-bin

    2006-01-01

    The prediction of the hardenability and chemical composition of gear steel was studied using artificial neural networks. A software was used to quantitatively forecast the hardenability by its chemical composition or the chemical composition by its hardenability. The prediction result is more precise than that obtained from the traditional method based on the simple mathematical regression model.

  20. Statistical Classification for Cognitive Diagnostic Assessment: An Artificial Neural Network Approach

    Science.gov (United States)

    Cui, Ying; Gierl, Mark; Guo, Qi

    2016-01-01

    The purpose of the current investigation was to describe how the artificial neural networks (ANNs) can be used to interpret student performance on cognitive diagnostic assessments (CDAs) and evaluate the performances of ANNs using simulation results. CDAs are designed to measure student performance on problem-solving tasks and provide useful…

  1. Detection of Respiratory Abnormalities Using Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Mahdi J. Baemani

    2008-01-01

    Full Text Available Problem Statement: Lung disease is a major threat to the human health regarding the industrial life, air pollution, smoking, and infections. Lung function tests are often performed using spirometry. Approach: The present study aims at detecting obstructive and restrictive pulmonary abnormalities. Lung function tests are often performed using spirometry. In this study, the data were obtained from 250 volunteers with standard recording protocol in order to detect and classify pulmonary diseases into normal, obstructive and restrictive. Firstly, spirometric data was statistically analyzed concerning its significance for neural networks. Then, such parameters were presented as input to MLP and recurrent networks. Results: These two networks detected normal and abnormal disorders as well as obstructive and restrictive patterns, respectively. Moreover, the output data was confirmed by measuring accuracy and sensitivity. Conclusion: The results show that the proposed method could be useful for detecting the function of respiratory system.

  2. ABNORMALITY DETECTION IN ECG USING ARTIFICIAL NEURAL NETWORKS

    Directory of Open Access Journals (Sweden)

    Shahanaz Ayub

    2010-01-01

    Full Text Available Electrocardiogram represents electrical activity of the heart. Arrhythmias are among the most common ECG abnormalities. Millions of ECGs are taken for the diagnosis of various classes of patients, where ECG can provide a lot of information regarding the abnormality in the concerned patient, ECGs are analysed by the physicians and interpreted depending upon their experience.The interpretation may vary by physician to physician. Hence this work is all about the automation and consistency in the analysis of the ECG signals so that they must be diagnosed and interpreted accurately irrespective of the physicians. This would help to start an early treatment for the problems and many lives could be saved. Many works have been done previously but this paper presents a new concept by application of MATLAB based tools in the same weighted neural network algorithms. This will help to reduce the hardware requirements, make network more reliable and thus a hope to make it feasible. To do so various networks were designed using the MATLAB based tools (licensed version and parameters. Two classes of networks were designed, but with different training algorithms, namely Perceptron and Back propagation. They were provided training inputs from the data obtained from the standard MIT-BIH Arrhythmia database. After training different forms of networks, they were tested by providing unknown inputs as patient data and the results in the whole process from training to testing were recorded in the form of tables. The results for the normal beats were best in the case of Cascade-Forward Back propagation network algorithm. The percentage of correct classification is 100%.The results are compared with the previous work which concludes that the method proposed in this paper gives best results.

  3. Flow of emotional messages in artificial social networks

    CERN Document Server

    Chmiel, Anna

    2010-01-01

    Models of message flows in an artificial group of users communicating via the Internet are introduced and investigated using numerical simulations. We assumed that messages possess an emotional character with a positive valence and that the willingness to send the next affective message to a given person increases with the number of messages received from this person. As a result, the weights of links between group members evolve over time. Memory effects are introduced, taking into account that the preferential selection of message receivers depends on the communication intensity during the recent period only. We also model the phenomenon of secondary social sharing when the reception of an emotional e-mail triggers the distribution of several emotional e-mails to other people.

  4. Monitoring and classifying animal behavior using ZigBee-based mobile ad hoc wireless sensor networks and artificial neural networks

    DEFF Research Database (Denmark)

    S. Nadimi, Esmaeil; Nyholm Jørgensen, Rasmus; Blanes-Vidal, Victoria;

    2012-01-01

    perceptron (MLP)-based artificial neural network (ANN). The best performance of the ANN in terms of the mean squared error (MSE) and the convergence speed was achieved when it was initialized and trained using the Nguyen–Widrow and Levenberg–Marquardt back-propagation algorithms, respectively. The success...... into their corresponding behavioral modes. However, network unreliability and high-energy consumption have limited the applicability of those systems. In this study, a 2.4-GHz ZigBee-based mobile ad hoc wireless sensor network (MANET) that is able to overcome those problems is presented. The designed MANET showed high...

  5. LOCATING HUBS IN TRANSPORT NETWORKS: AN ARTIFICIAL INTELLIGENCE APPROACH

    Directory of Open Access Journals (Sweden)

    Dušan Teodorović

    2014-09-01

    Full Text Available Hub facilities serve as switching and transshipment points in transportation and communication networks as well as in logistic systems. Hub networks have an influence on flows on the hub-to-hub links and ensure benefit from economies of scale in inter-hub transportation. The key factors for designing a successful hub-and-spoke network are to determine the optimal number of hubs, to properly locate hubs, and to allocate the non-hubs to the hubs. This paper presents the model to determine the locations of the p-hub facilities in the network and to allocate the non-hubs to the hubs. The problem is solved by the Bee Colony Optimization (BCO algorithm, and the results are compared with the optimal solutions obtained by CPLEX. The BCO algorithm belongs to the class of stochastic swarm optimization methods. The proposed algorithm is inspired by the foraging habits of bees in the nature. The BCO algorithm was able to obtain the optimal value of objective functions in all test problems. The CPU times required to find the best solutions by the BCO are acceptable.

  6. Forecasting solar proton event with artificial neural network

    Science.gov (United States)

    Gong, J.; Wang, J.; Xue, B.; Liu, S.; Zou, Z.

    Solar proton event (SPE), relatively rare but popular in solar maximum, can bring hazard situation to spacecraft. As a special event, SPE always accompanies flare, which is also called proton flare. To produce such an eruptive event, large amount energy must be accumulated within the active region. So we can investigate the character of the active region and its evolving trend, together with other such as cm radio emission and soft X-ray background to evaluate the potential of SEP in chosen area. In order to summarize the omen of SPEs in the active regions behind the observed parameters, we employed AI technology. Full connecting neural network was chosen to fulfil this job. After constructing the network, we train it with 13 parameters that was able to exhibit the character of active regions and their evolution trend. More than 80 sets of event parameter were defined to teach the neural network to identify whether an active region was potential of SPE. Then we test this model with a data base consisting SPE and non-SPE cases that was not used to train the neural network. The result showed that 75% of the choice by the model was right.

  7. Determination of Liquefaction Potential using Artificial Neural Networks

    DEFF Research Database (Denmark)

    Farrokhzad, F; Choobbasti, A.J; Barari, Amin

    2011-01-01

    The authors propose an alternative general regression model based on neural networks, which enables analysis of summary data obtained by liquefaction analysis according to usual methods. For that purpose, the data from some thirty boreholes made during field investigations in Babol, in the Iranian...

  8. Artificial Neural Network Based State Estimators Integrated into Kalmtool

    DEFF Research Database (Denmark)

    Bayramoglu, Enis; Ravn, Ole; Poulsen, Niels Kjølstad

    2012-01-01

    In this paper we present a toolbox enabling easy evaluation and comparison of dierent ltering algorithms. The toolbox is called Kalmtool and is a set of MATLAB tools for state estimation of nonlinear systems. The toolbox now contains functions for Articial Neural Network Based State Estimation...

  9. Customer Credit Risk Assessment using Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Nasser Mohammadi

    2016-03-01

    Full Text Available Since the granting of banking facilities in recent years has faced problems such as customer credit risk and affects the profitability directly, customer credit risk assessment has become imperative for banks and it is used to distinguish good applicants from those who will probably default on repayments. In credit risk assessment, a score is assigned to each customer then by comparing it with the cut-off point score which distinguishes two classes of the applicants, customers are classified into two credit statuses either a good or bad applicant. Regarding good performance and their ability of classification, generalization and learning patterns, Multilayer Perceptron Neural Network model trained using various Back-Propagation (BP algorithms considered in designing an evaluation model in this study. The BP algorithms, Levenberg-Marquardt (LM, Gradient descent, Conjugate gradient, Resilient, BFGS Quasinewton, and One-step secant were utilized. Each of these six networks runs and trains for different numbers of neurons within their hidden layer. Mean squared error (MSE is used as a criterion to specify optimum number of neurons in the hidden layer. The results showed that LM algorithm converges faster to the network and achieves better performance than the other algorithms. At last, by comparing classification performance of neural network with a number of classification algorithms such as Logistic Regression and Decision Tree, the neural network model outperformed the others in customer credit risk assessment. In credit models, because the cost that Type II error rate imposes to the model is too high, therefore, Receiver Operating Characteristic curve is used to find appropriate cut-off point for a model that in addition to high Accuracy, has lower Type II error rate.

  10. On the identification of quark and gluon jets using artificial neural network method

    International Nuclear Information System (INIS)

    The identification of quark and gluon jets produced in e+e- collisions using the artificial neural network method is addressed. The structure and the learning algorithm of the BP (Back Propagation) neural network model is studied. Three characteristic parameters--the average multiplicity and the average transverse momentum of jets and the average value of the angles opposite to the quark or gluon jets are taken as training parameters and are inputted to the BP network for repeated training. The learning process is ended when the output error of the neural network is less than a preset precision (σ=0.005). The same training routine is repeated in each of the 8 energy bins ranging from 2.5-22.5 GeV, respectively. The finally updated weights and thresholds of the BP neural network are tested using the quark and gluon jet samples,getting from the non-symmetric three-jet events produced by the Monte Carlo generator JETSET 7.4. Then the pattern recognition of the mixed sample getting from the combination of the quark and gluon jet samples is carried out through applying the trained BP neural network. It turns out that the purities of the identified quark and gluon jets are around 75%-85%, showing that the artificial neural network is effective and practical in jet analysis. It is hopeful to use the further improved BP neural network to study the experimental data of high energy e+e- collisions. (author)

  11. Predicting temperature profiles in producing oil wells using artificial neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Farshad, F.F.; Garber, J.D.; Lorde, J.N. [Louisiana Univ., Lafayette, LA (United States)

    2000-10-19

    A novel approach using artificial neural networks (ANNs) for predicting temperature profiles evaluated 27 wells in the Gulf of Mexico. Two artificial neural network models were developed that predict the temperature of the flowing fluid at any depth in flowing oil wells. Back propagation was used in training the networks. The networks were tested using measured temperature profiles from the 27 oil wells. Both neural network models successfully mapped the general temperature-profile trends of naturally flowing oil wells. The highest accuracy was achieved with a mean absolute relative percentage error of 6.0 per cent. The accuracy of the proposed neural network models to predict the temperature profile is compared to that of existing correlations. Many correlations to predict temperature profiles of the wellbore fluid, for single-phase or multiphase flow, in producing oil wells have been developed using theoretical principles such as energy, mass and momentum balances coupled with regression analysis. The Neural Network 2 model exhibited significantly lower mean absolute relative percentage error than other correlations. Furthermore, in order to test the accuracy of the neural network models to that of Kirkpatrick's correlation, a mathematical model was developed for Kirkpatrick's flowing temperature gradient chart. (Author)

  12. Inversion of quasi-3D DC resistivity imaging data using artificial neural networks

    Indian Academy of Sciences (India)

    Ahmad Neyamadpour; W A T Wan Abdullah; Samsudin Taib

    2010-02-01

    The objective of this paper is to investigate the applicability of artificial neural networks in inverting quasi-3D DC resistivity imaging data. An electrical resistivity imaging survey was carried out along seven parallel lines using a dipole–dipole array to confirm the validation of the results of an inversion using an artificial neural network technique. The model used to produce synthetic data to train the artificial neural network was a homogeneous medium of 100 m resistivity with an embedded anomalous body of 1000 m resistivity. The network was trained using 21 datasets (comprising 12159 data points) and tested on another 11 synthetic datasets (comprising 6369 data points) and on real field data. Another 24 test datasets (comprising 13896 data points) consisting of different resistivities for the background and the anomalous bodies were used in order to test the interpolation and extrapolation of network properties. Different learning paradigms were tried in the training process of the neural network, with the resilient propagation paradigm being the most efficient. The number of nodes, hidden layers, and efficient values for learning rate and momentum coefficient have been studied. Although a significant correlation between results of the neural network and the conventional robust inversion technique was found, the ANN results show more details of the subsurface structure, and the RMS misfits for the results of the neural network are less than seen with conventional methods. The interpreted results show that the trained network was able to invert quasi-3D electrical resistivity imaging data obtained by dipole–dipole configuration both rapidly and accurately.

  13. Application of artificial neural network for medical image recognition and diagnostic decision making

    International Nuclear Information System (INIS)

    An artificial neural network has been applied for pattern recognition and used as a tool in an expert system. The purpose of this study is to examine the potential usefulness of the neural network approach in medical applications for image recognition and decision making. The authors designed multilayer feedforward neural networks with a back-propagation algorithm for our study. Using first-pass radionuclide ventriculograms, we attempted to identify the right and left ventricles of the heart and the lungs by training the neural network from patterns of time-activity curves. In a preliminary study, the neural network enabled identification of the lungs and heart chambers once the network was trained sufficiently by means of repeated entries of data from the same case

  14. Modelling and Prediction of Photovoltaic Power Output Using Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Aminmohammad Saberian

    2014-01-01

    Full Text Available This paper presents a solar power modelling method using artificial neural networks (ANNs. Two neural network structures, namely, general regression neural network (GRNN feedforward back propagation (FFBP, have been used to model a photovoltaic panel output power and approximate the generated power. Both neural networks have four inputs and one output. The inputs are maximum temperature, minimum temperature, mean temperature, and irradiance; the output is the power. The data used in this paper started from January 1, 2006, until December 31, 2010. The five years of data were split into two parts: 2006–2008 and 2009-2010; the first part was used for training and the second part was used for testing the neural networks. A mathematical equation is used to estimate the generated power. At the end, both of these networks have shown good modelling performance; however, FFBP has shown a better performance comparing with GRNN.

  15. Can artificial neural networks provide an "expert's" view of medical students performances on computer based simulations?

    Science.gov (United States)

    Stevens, R H; Najafi, K

    1992-01-01

    Artificial neural networks were trained to recognize the test selection patterns of students' successful solutions to seven immunology computer based simulations. When new student's test selections were presented to the trained neural network, their problem solutions were correctly classified as successful or non-successful > 90% of the time. Examination of the neural networks output weights after each test selection revealed a progressive increase for the relevant problem suggesting that a successful solution was represented by the neural network as the accumulation of relevant tests. Unsuccessful problem solutions revealed two patterns of students performances. The first pattern was characterized by low neural network output weights for all seven problems reflecting extensive searching and lack of recognition of relevant information. In the second pattern, the output weights from the neural network were biased towards one of the remaining six incorrect problems suggesting that the student mis-represented the current problem as an instance of a previous problem.

  16. An Artificial Neural Network Approach to the Solution of Molecular Chemical Equilibrium

    CERN Document Server

    Ramos, A A

    2005-01-01

    A novel approach is presented for the solution of instantaneous chemical equilibrium problems. The chemical equilibrium can be considered, due to its intrinsically local character, as a mapping of the three-dimensional parameter space spanned by the temperature, hydrogen density and electron density into many one-dimensional spaces representing the number density of each species. We take advantage of the ability of artificial neural networks to approximate non-linear functions and construct neural networks for the fast and efficient solution of the chemical equilibrium problem in typical stellar atmosphere physical conditions. The neural network approach has the advantage of providing an analytic function, which can be rapidly evaluated. The networks are trained with a learning set (that covers the entire parameter space) until a relative error below 1% is reached. It has been verified that the networks are not overtrained by using an additional verification set. The networks are then applied to a snapshot of...

  17. Parameter estimation of brain tumors using intraoperative thermal imaging based on artificial tactile sensing in conjunction with artificial neural network

    Science.gov (United States)

    Sadeghi-Goughari, M.; Mojra, A.; Sadeghi, S.

    2016-02-01

    Intraoperative Thermal Imaging (ITI) is a new minimally invasive diagnosis technique that can potentially locate margins of brain tumor in order to achieve maximum tumor resection with least morbidity. This study introduces a new approach to ITI based on artificial tactile sensing (ATS) technology in conjunction with artificial neural networks (ANN) and feasibility and applicability of this method in diagnosis and localization of brain tumors is investigated. In order to analyze validity and reliability of the proposed method, two simulations were performed. (i) An in vitro experimental setup was designed and fabricated using a resistance heater embedded in agar tissue phantom in order to simulate heat generation by a tumor in the brain tissue; and (ii) A case report patient with parafalcine meningioma was presented to simulate ITI in the neurosurgical procedure. In the case report, both brain and tumor geometries were constructed from MRI data and tumor temperature and depth of location were estimated. For experimental tests, a novel assisted surgery robot was developed to palpate the tissue phantom surface to measure temperature variations and ANN was trained to estimate the simulated tumor’s power and depth. Results affirm that ITI based ATS is a non-invasive method which can be useful to detect, localize and characterize brain tumors.

  18. Parameter estimation of brain tumors using intraoperative thermal imaging based on artificial tactile sensing in conjunction with artificial neural network

    International Nuclear Information System (INIS)

    Intraoperative Thermal Imaging (ITI) is a new minimally invasive diagnosis technique that can potentially locate margins of brain tumor in order to achieve maximum tumor resection with least morbidity. This study introduces a new approach to ITI based on artificial tactile sensing (ATS) technology in conjunction with artificial neural networks (ANN) and feasibility and applicability of this method in diagnosis and localization of brain tumors is investigated. In order to analyze validity and reliability of the proposed method, two simulations were performed. (i) An in vitro experimental setup was designed and fabricated using a resistance heater embedded in agar tissue phantom in order to simulate heat generation by a tumor in the brain tissue; and (ii) A case report patient with parafalcine meningioma was presented to simulate ITI in the neurosurgical procedure. In the case report, both brain and tumor geometries were constructed from MRI data and tumor temperature and depth of location were estimated. For experimental tests, a novel assisted surgery robot was developed to palpate the tissue phantom surface to measure temperature variations and ANN was trained to estimate the simulated tumor’s power and depth. Results affirm that ITI based ATS is a non-invasive method which can be useful to detect, localize and characterize brain tumors. (paper)

  19. Application of artificial neural networks in the CT study of solitary pulmonary nodule

    International Nuclear Information System (INIS)

    Objective: To establish a new-type discriminative method in differentiating benign from malignant solitary pulmonary nodule (SPN) on high-resolution CT/thin-section CT by using artificial neural networks theory in the CT diagnostic study of SPN. Methods: Two hundred SPNs pathologically proved by operation or biopsy (primary pulmonary carcinoma 135 eases, benign nodules 65 cases) were collected, 3 clinical characteristics (age, sex, with or without bloody sputum) and 9 high-resolution CT/thin-section CT characteristics (location, long and short diameter, contour, spiculation, halo sign, air-space, relation with the adjacent blood vessels and pleura) were analyzed. 140 cases were randomly selected to form the training samples, on which artificial neural networks model (BP networks) was built and compared with Logistic model from Statistical Package for the Social Science (SPSS) software. Results: The total consistent rate of BP neural networks (98.0%, 196/200) was higher than that of Logistic model (86.0%, 172/200) (P<0.001). Areas under ROC curve were 0.996±0.004 and 0.936±0.017, respectively, and the difference between the two was significant (P<0.001). Conclusion: Using high-resolution CT and thin-section CT in combination with artificial neural networks theory is feasible, and it is expected to become a useful and reliable clinical tool in differentiating benign from malignant SPN. (authors)

  20. PREDICTION OF DEMAND FOR PRIMARY BOND OFFERINGS USING ARTIFICIAL NEURAL NETWORKS

    Directory of Open Access Journals (Sweden)

    Michal Tkac

    2014-12-01

    Full Text Available Purpose: Primary bond markets represent an interesting investment opportunity not only for banks, insurance companies, and other institutional investors, but also for individuals looking for capital gains. Since offered securities vary in terms of their rating, industrial classification, coupon, or maturity, demand of buyers for particular offerings often overcomes issued volume and price of given bond on secondary market consequently rises. Investors might be regarded as consumers purchasing required service according to their specific preferences at desired price. This paper aims at analysis of demand for bonds on primary market using artificial neural networks.Design/methodology: We design a multilayered feedforward neural network trained by Levenberg-Marquardt algorithm in order to estimate demand for individual bonds based on parameters of particular offerings. Outcomes obtained by artificial neural network are compared with conventional econometric methods.Findings: Our results indicate that artificial neural network significantly outperformed standard econometric techniques and on examined sample of primary bond offerings achieved considerably better performance in terms of prediction accuracy and mean squared error.Originality: We show that proposed neural network is able to successfully predict demand for primary obligation offerings based on their specifications. Moreover, we identify relevant parameters of issues which are able to considerably affect total demand for given security.  Our findings might not only help investors to detect marketable securities, but also enable issuing entities to increase demand for their bonds in order to decrease their offering price. 

  1. Application of artificial neural networks to the design optimization of aerospace structural components

    Science.gov (United States)

    Berke, Laszlo; Patnaik, Surya N.; Murthy, Pappu L. N.

    1993-01-01

    The application of artificial neural networks to capture structural design expertise is demonstrated. The principal advantage of a trained neural network is that it requires trivial computational effort to produce an acceptable new design. For the class of problems addressed, the development of a conventional expert system would be extremely difficult. In the present effort, a structural optimization code with multiple nonlinear programming algorithms and an artificial neural network code NETS were used. A set of optimum designs for a ring and two aircraft wings for static and dynamic constraints were generated by using the optimization codes. The optimum design data were processed to obtain input and output pairs, which were used to develop a trained artificial neural network with the code NETS. Optimum designs for new design conditions were predicted by using the trained network. Neural net prediction of optimum designs was found to be satisfactory for most of the output design parameters. However, results from the present study indicate that caution must be exercised to ensure that all design variables are within selected error bounds.

  2. Estimating tree bole volume using artificial neural network models for four species in Turkey.

    Science.gov (United States)

    Ozçelik, Ramazan; Diamantopoulou, Maria J; Brooks, John R; Wiant, Harry V

    2010-01-01

    Tree bole volumes of 89 Scots pine (Pinus sylvestris L.), 96 Brutian pine (Pinus brutia Ten.), 107 Cilicica fir (Abies cilicica Carr.) and 67 Cedar of Lebanon (Cedrus libani A. Rich.) trees were estimated using Artificial Neural Network (ANN) models. Neural networks offer a number of advantages including the ability to implicitly detect complex nonlinear relationships between input and output variables, which is very helpful in tree volume modeling. Two different neural network architectures were used and produced the Back propagation (BPANN) and the Cascade Correlation (CCANN) Artificial Neural Network models. In addition, tree bole volume estimates were compared to other established tree bole volume estimation techniques including the centroid method, taper equations, and existing standard volume tables. An overview of the features of ANNs and traditional methods is presented and the advantages and limitations of each one of them are discussed. For validation purposes, actual volumes were determined by aggregating the volumes of measured short sections (average 1 meter) of the tree bole using Smalian's formula. The results reported in this research suggest that the selected cascade correlation artificial neural network (CCANN) models are reliable for estimating the tree bole volume of the four examined tree species since they gave unbiased results and were superior to almost all methods in terms of error (%) expressed as the mean of the percentage errors. PMID:19880241

  3. Artificial Neural Network Based Approach for short load forecasting

    Directory of Open Access Journals (Sweden)

    Mr. Rajesh Deshmukh

    2011-12-01

    Full Text Available Accurate models for electric power load forecasting are essential to the operation and planning of a power utility company. Load forecasting helps electric utility to make important decisions on trading of power, load switching, and infrastructure development. Load forecasts are extremely important for power utilizes ISOs, financial institutions, and other stakeholder of power sector. Short term load forecasting is a essential part of electric power system planning and operation forecasting made for unit commitment and security assessment, which have a direct impact on operational casts and system security. Conventional ANN based load forecasting method deal with 24 hour ahead load forecasting by using forecasted temp. This can lead to high forecasting errors in case of rapid temperature changes. This paper present a neural network based approach for short term load forecasting considering data for training, validation and testing of neural network.

  4. Artificial Neural Networks Based Modeling and Control of Continuous Stirred Tank Reactor

    Directory of Open Access Journals (Sweden)

    R. S.M.N. Malar

    2009-01-01

    Full Text Available Continuous Stirred Tank Reactor (CSTR is one of the common reactors in chemical plant. Problem statement: Developing a model incorporating the nonlinear dynamics of the system warrants lot of computation. An efficient control of the product concentration can be achieved only through accurate model. Approach: In this study, attempts were made to alleviate the above mentioned problem using “Artificial Intelligence” (AI techniques. One of the AI techniques namely Artificial Neural Networks (ANN was used to model the CSTR incorporating its non-linear characteristics. Two nonlinear models based control strategies namely internal model control and direct inverse control were designed using the neural networks and applied to the control of isothermal CSTR. Results: The simulation results for the above control schemes with set point tracking were presented. Conclusion: Results indicated that neural networks can learn accurate models and give good non-linear control when model equations are not known.

  5. Fault diagnosis in nuclear power plants using an artificial neural network technique

    International Nuclear Information System (INIS)

    Application of artificial intelligence (AI) computational techniques, such as expert systems, fuzzy logic, and neural networks in diverse areas has taken place extensively. In the nuclear industry, the intended goal for these AI techniques is to improve power plant operational safety and reliability. As a computerized operator support tool, the artificial neural network (ANN) approach is an emerging technology that currently attracts a large amount of interest. The ability of ANNs to extract the input/output relation of a complicated process and the superior execution speed of a trained ANN motivated this study. The goal was to develop neural networks for sensor and process faults diagnosis with the potential of implementing as a component of a real-time operator support system LYDIA, early sensor and process fault detection and diagnosis

  6. Biologically inspired intelligent decision making: a commentary on the use of artificial neural networks in bioinformatics.

    Science.gov (United States)

    Manning, Timmy; Sleator, Roy D; Walsh, Paul

    2014-01-01

    Artificial neural networks (ANNs) are a class of powerful machine learning models for classification and function approximation which have analogs in nature. An ANN learns to map stimuli to responses through repeated evaluation of exemplars of the mapping. This learning approach results in networks which are recognized for their noise tolerance and ability to generalize meaningful responses for novel stimuli. It is these properties of ANNs which make them appealing for applications to bioinformatics problems where interpretation of data may not always be obvious, and where the domain knowledge required for deductive techniques is incomplete or can cause a combinatorial explosion of rules. In this paper, we provide an introduction to artificial neural network theory and review some interesting recent applications to bioinformatics problems. PMID:24335433

  7. Estimating Of Etchant Copper Concentration In The Electrolytic Cell Using Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Muzher M. Ibrahem

    2013-05-01

    Full Text Available      In  this paper, Artificial Neural Networks (ANN, which are known for their ability to model nonlinear systems, provide accurate approximations of system behavior and are typically much more computationally efficient than phenomenological models  are used to predict the etchant copper concentration in the electrolytic cell in terms of electric potential, operating time, temperature of the electrolytic cell , ratio of surface area of poles per unit volume of solution  and the distance between poles. In this paper 350 sets of data are used to trained and test the network.. The best results were achieved using a model based on a feedforword Artificial Neural Network (ANN with one hidden layer and fifteen neurons in the hidden layer gives a very close prediction of the copper concentration in the electrolytic cell.

  8. Automatic Fault Classification of Rolling Element Bearing using Wavelet Packet Decomposition and Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Manish Yadav

    2011-09-01

    Full Text Available In this work an automatic fault classification system is developed for bearing fault classification of three phase induction motor. The system uses the wavelet packet decomposition using ‘db8’ motherwavelet function for feature extraction from the vibration signal, recorded for various bearing fault conditions. The selection of best node of wavelet packet tree is performed by using best tree algorithmalong with minimum Shannon entropy criteria. The ten statistical features such as peak value, root mean square value (RMS, kurtosis, skewness etc. are extracted from the wavelet packet coefficient of optimal node. The extracted feature then was used to train and test neural network fault classification. The artificial neural network system was designed to classify the rolling element bearing condition: healthy bearing (HB rolling element fault (REF, inner race fault (IRF and Outer race fault (ORF for fault classification. The over all fault classification rate is 98.33% of the artificial neural network fault classifier.

  9. Security framework for networked storage system based on artificial immune system

    Science.gov (United States)

    Huang, Jianzhong; Xie, Changsheng; Zhang, Chengfeng; Zhan, Ling

    2007-11-01

    This paper proposed a theoretical framework for the networked storage system addressing the storage security. The immune system is an adaptive learning system, which can recognize, classify and eliminate 'non-self' such as foreign pathogens. Thus, we introduced the artificial immune technique to the storage security research, and proposed a full theoretical framework for storage security system. Under this framework, it is possible to carry out the quantitative evaluation for the storage security system using modeling language of artificial immune system (AIS), and the evaluation can offer security consideration for the deployment of networked storage system. Meanwhile, it is potential to obtain the active defense technique suitable for networked storage system via exploring the principle of AIS and achieve a highly secure storage system with immune characteristic.

  10. Hardware-based artificial neural networks for size, weight, and power constrained platforms

    Science.gov (United States)

    Wysocki, B. T.; McDonald, N. R.; Thiem, C. D.

    2014-05-01

    A fully parallel, silicon-based artificial neural network (CM1K) built on zero instruction set computer (ZISC) technology was used for change detection and object identification in video data. Fundamental pattern recognition capabilities were demonstrated with reduced neuron numbers utilizing only a few, or in some cases one, neuron per category. This simplified approach was used to validate the utility of few neuron networks for use in applications that necessitate severe size, weight, and power (SWaP) restrictions. The limited resource requirements and massively parallel nature of hardware-based artificial neural networks (ANNs) make them superior to many software approaches in resource limited systems, such as micro-UAVs, mobile sensor platforms, and pocket-sized robots.

  11. Condition monitoring of planetary gearbox by hardware implementation of artificial neural networks

    DEFF Research Database (Denmark)

    Dabrowski, Dariusz

    2016-01-01

    -stationary conditions and are exposed to extreme events. Also bucket-wheel excavators are equipped with high-power gearboxes that are exposed to shocks. Continuous monitoring of their condition is crucial in view of early failures, and to ensure safety of exploitation. Artificial neural networks allow for a quick...... environmental conditions. In this paper, a hardware implementation of an artificial neural network designed for condition monitoring of a planetary gearbox is presented. The implementation was done on a Field Programmable Gate Array (FPGA). It is characterized by much higher efficiency and stability than...... and effective association of the symptoms with the condition of the machine. Extensive research shows that neural networks can be successfully used to recognize gearboxes' failures; they allow for detection of new failures which were not known at the time of training and can be applied for identification...

  12. Artificial Neural Networks in the prediction of insolvency. A paradigm shift to traditional business practices recipes

    Directory of Open Access Journals (Sweden)

    Marcia M. Lastre Valdes

    2014-06-01

    Full Text Available In this paper a review and analysis of the major theories and models that address the prediction of corporate bankruptcy and insolvency is made. Neural networks are a tool of most recent appearance, although in recent years have received considerable attention from the academic and professional world, and have started to be implemented in different models testing organizations insolvency based on neural computation. The purpose of this paper is to yield evidence of the usefulness of Artificial Neural Networks in the problem of bankruptcy prediction insolence or so compare its predictive ability with the methods commonly used in that context. The findings suggest that high predictive capabilities can be achieved  using artificial neural networks, with qualitative and quantitative variables.

  13. Optimization with artificial neural network systems - A mapping principle and a comparison to gradient based methods

    Science.gov (United States)

    Leong, Harrison Monfook

    1988-01-01

    General formulae for mapping optimization problems into systems of ordinary differential equations associated with artificial neural networks are presented. A comparison is made to optimization using gradient-search methods. The performance measure is the settling time from an initial state to a target state. A simple analytical example illustrates a situation where dynamical systems representing artificial neural network methods would settle faster than those representing gradient-search. Settling time was investigated for a more complicated optimization problem using computer simulations. The problem was a simplified version of a problem in medical imaging: determining loci of cerebral activity from electromagnetic measurements at the scalp. The simulations showed that gradient based systems typically settled 50 to 100 times faster than systems based on current neural network optimization methods.

  14. Weather Radar Estimations Feeding an Artificial Neural Network Model

    OpenAIRE

    Gustavo Cerda Villafaña; Sergio Ledesma; Dawei Han

    2008-01-01

    The application of ANNs (Artifi cial Neural Networks) has been studied by many researchers in modelling rainfall runoff processes. However, the work so far has been focused on the rainfall data from traditional raingauges. Weather radar is a modern technology which could provide high resolution rainfall in time and space. In this study, a comparison in rainfall runoff modelling between the raingauge and weather radar has been carried out. The data were collected from Brue catchment in Southwe...

  15. Spin wave absorber generated by artificial surface anisotropy for spin wave device network

    Science.gov (United States)

    Kanazawa, Naoki; Goto, Taichi; Sekiguchi, Koji; Granovsky, Alexander B.; Takagi, Hiroyuki; Nakamura, Yuichi; Inoue, Mitsuteru

    2016-09-01

    Spin waves (SWs) have the potential to reduce the electric energy loss in signal processing networks. The SWs called magnetostatic forward volume waves (MSFVWs) are advantageous for networking due to their isotropic dispersion in the plane of a device. To control the MSFVW flow in a processing network based on yttrium iron garnet, we developed a SW absorber using artificial structures. The mechanical surface polishing method presented in this work can well control extrinsic damping without changing the SW dispersion of the host material. Furthermore, enhancement of the ferromagnetic resonance linewidth over 3 Oe was demonstrated.

  16. Escherichia coli O157:H7 restriction pattern recognition by artificial neural network.

    OpenAIRE

    Carson, C. A.; Keller, J. M.; McAdoo, K K; Wang, D.; HIGGINS, B; Bailey, C W; Thorne, J G; Payne, B J; Skala, M; Hahn, A. W.

    1995-01-01

    An artificial neural network model for the recognition of Escherichia coli O157:H7 restriction patterns was designed. In the training phase, images of two classes of E. coli isolates (O157:H7 and non-O157:H7) were digitized and transmitted to the neural network. The system was then tested for recognition of images not included in the training set. Promising results were achieved with the designed network configuration, providing a basis for further study. This application of a new generation ...

  17. Simple artificial neural networks that match probability and exploit and explore when confronting a multiarmed bandit.

    Science.gov (United States)

    Dawson, Michael R W; Dupuis, Brian; Spetch, Marcia L; Kelly, Debbie M

    2009-08-01

    The matching law (Herrnstein 1961) states that response rates become proportional to reinforcement rates; this is related to the empirical phenomenon called probability matching (Vulkan 2000). Here, we show that a simple artificial neural network generates responses consistent with probability matching. This behavior was then used to create an operant procedure for network learning. We use the multiarmed bandit (Gittins 1989), a classic problem of choice behavior, to illustrate that operant training balances exploiting the bandit arm expected to pay off most frequently with exploring other arms. Perceptrons provide a medium for relating results from neural networks, genetic algorithms, animal learning, contingency theory, reinforcement learning, and theories of choice. PMID:19596631

  18. A Hardware Implementation of Artificial Neural Network Using Field Programmable Gate Arrays

    OpenAIRE

    Won, E.

    2007-01-01

    An artificial neural network algorithm is implemented using a field programmable gate array hardware. One hidden layer is used in the feed-forward neural network structure in order to discriminate one class of patterns from the other class in real time. With five 8-bit input patterns, six hidden nodes, and one 8-bit output, the implemented hardware neural network makes decision on a set of input patterns in 11 clocks and the result is identical to what to expect from off-line computation. Thi...

  19. Prediction of mechanical property of E4303 electrode using artificial neural network

    Institute of Scientific and Technical Information of China (English)

    徐越兰; 黄俊; 王克鸿

    2004-01-01

    Based on the method of artificial neural network, a new approach has been devised to predict the mechanical property of E4303 electrode. The outlined predication model for determining the mechanical property of electrode was built upon the production data. The research leverages a back propagation algorithm as the neural network's learning rule. The result indicates that there are positive correlations between the predicted results and the practical production data. Hence, using the neural network, predication of electrode property can be realized. For the first time, this research provides a more scientific method for designing electrode.

  20. Analysis on evaluation ability of nonlinear safety assessment model of coal mines based on artificial neural network

    Institute of Scientific and Technical Information of China (English)

    SHI Shi-liang; LIU Hai-bo; LIU Ai-hua

    2004-01-01

    Based on the integration analysis of goods and shortcomings of various methods used in safety assessment of coal mines, combining nonlinear feature of mine safety sub-system, this paper establishes the neural network assessment model of mine safety, analyzes the ability of artificial neural network to evaluate mine safety state, and lays the theoretical foundation of artificial neural network using in the systematic optimization of mine safety assessment and getting reasonable accurate safety assessment result.

  1. Influence of different auxiliary agents of biomechanical preparation in the filling of "artificially prepared" lateral canals Influência de diferentes agentes auxiliares do preparo biomecânico na obturação de canais laterais artificiais

    Directory of Open Access Journals (Sweden)

    Daniela Ribeiro da Silva

    2005-06-01

    Full Text Available The purpose of the present study was to evaluate the influence of some auxiliary agents of biomechanical preparation of the root canal on the filling of artificial lateral canals in extracted human teeth. A total of eighty single-rooted teeth were employed, which were submitted to preparation of three artificial lateral canals in one of the proximal aspects at the cervical, middle and apical thirds, besides one in the buccal aspect. The main canals were prepared by Profile 0.4 rotary instruments through the crown-down technique and irrigated with the irrigants investigated, as follows: Group A - 1% sodium hypochlorite and final irrigation with trisodium EDTA for 5 minutes; Group B - Endogel (2% chlorhexidine gel; Group C - Endo PTC and Dakin's solution and final irrigation with Tergentol- Furacin; and Group D - File Eze. The root canals were obturated by the Tagger's hybrid technique and then radiographed for assessment of the penetration rate of the filling materials in the lateral canals. Analysis of the results demonstrated no statistically significant difference (pObjetivou-se avaliar a influência de alguns agentes auxiliares do preparo biomecânico do canal radicular, na obturação de canais laterais artificiais em dentes humanos extraídos. Foram utilizados oitenta dentes unirradiculados nos quais, previamente, foram confeccionados três canais laterais artificiais em uma das paredes proximais, nos terços cervical, médio e apical e um canal na parede vestibular. Os canais principais foram preparados com instrumentação rotatória, instrumentos Profile 0.4, pela técnica rotatória coroa- ápice e irrigados com a substância irrigadora estudada, sendo no grupo A - hipoclorito de sódio a 1% e irrigação final com EDTA trissódico por 5 minutos; grupo B - Endogel (gel de clorexidina a 2%; grupo C - Endo PTC e solução de Dakin e irrigação final com tergentol-furacin segundo a técnica de Paiva e Antoniazzi e no grupo D - File Eze

  2. Architecture and biological applications of artificial neural networks: a tuberculosis perspective.

    Science.gov (United States)

    Darsey, Jerry A; Griffin, William O; Joginipelli, Sravanthi; Melapu, Venkata Kiran

    2015-01-01

    Advancement of science and technology has prompted researchers to develop new intelligent systems that can solve a variety of problems such as pattern recognition, prediction, and optimization. The ability of the human brain to learn in a fashion that tolerates noise and error has attracted many researchers and provided the starting point for the development of artificial neural networks: the intelligent systems. Intelligent systems can acclimatize to the environment or data and can maximize the chances of success or improve the efficiency of a search. Due to massive parallelism with large numbers of interconnected processers and their ability to learn from the data, neural networks can solve a variety of challenging computational problems. Neural networks have the ability to derive meaning from complicated and imprecise data; they are used in detecting patterns, and trends that are too complex for humans, or other computer systems. Solutions to the toughest problems will not be found through one narrow specialization; therefore we need to combine interdisciplinary approaches to discover the solutions to a variety of problems. Many researchers in different disciplines such as medicine, bioinformatics, molecular biology, and pharmacology have successfully applied artificial neural networks. This chapter helps the reader in understanding the basics of artificial neural networks, their applications, and methodology; it also outlines the network learning process and architecture. We present a brief outline of the application of neural networks to medical diagnosis, drug discovery, gene identification, and protein structure prediction. We conclude with a summary of the results from our study on tuberculosis data using neural networks, in diagnosing active tuberculosis, and predicting chronic vs. infiltrative forms of tuberculosis. PMID:25502388

  3. Architecture and biological applications of artificial neural networks: a tuberculosis perspective.

    Science.gov (United States)

    Darsey, Jerry A; Griffin, William O; Joginipelli, Sravanthi; Melapu, Venkata Kiran

    2015-01-01

    Advancement of science and technology has prompted researchers to develop new intelligent systems that can solve a variety of problems such as pattern recognition, prediction, and optimization. The ability of the human brain to learn in a fashion that tolerates noise and error has attracted many researchers and provided the starting point for the development of artificial neural networks: the intelligent systems. Intelligent systems can acclimatize to the environment or data and can maximize the chances of success or improve the efficiency of a search. Due to massive parallelism with large numbers of interconnected processers and their ability to learn from the data, neural networks can solve a variety of challenging computational problems. Neural networks have the ability to derive meaning from complicated and imprecise data; they are used in detecting patterns, and trends that are too complex for humans, or other computer systems. Solutions to the toughest problems will not be found through one narrow specialization; therefore we need to combine interdisciplinary approaches to discover the solutions to a variety of problems. Many researchers in different disciplines such as medicine, bioinformatics, molecular biology, and pharmacology have successfully applied artificial neural networks. This chapter helps the reader in understanding the basics of artificial neural networks, their applications, and methodology; it also outlines the network learning process and architecture. We present a brief outline of the application of neural networks to medical diagnosis, drug discovery, gene identification, and protein structure prediction. We conclude with a summary of the results from our study on tuberculosis data using neural networks, in diagnosing active tuberculosis, and predicting chronic vs. infiltrative forms of tuberculosis.

  4. Evaluation of artificial neural network techniques for flow forecasting in the River Yangtze, China

    Directory of Open Access Journals (Sweden)

    C. W. Dawson

    2002-01-01

    Full Text Available While engineers have been quantifying rainfall-runoff processes since the mid-19th century, it is only in the last decade that artificial neural network models have been applied to the same task. This paper evaluates two neural networks in this context: the popular multilayer perceptron (MLP, and the radial basis function network (RBF. Using six-hourly rainfall-runoff data for the River Yangtze at Yichang (upstream of the Three Gorges Dam for the period 1991 to 1993, it is shown that both neural network types can simulate river flows beyond the range of the training set. In addition, an evaluation of alternative RBF transfer functions demonstrates that the popular Gaussian function, often used in RBF networks, is not necessarily the ‘best’ function to use for river flow forecasting. Comparisons are also made between these neural networks and conventional statistical techniques; stepwise multiple linear regression, auto regressive moving average models and a zero order forecasting approach. Keywords: Artificial neural network, multilayer perception, radial basis function, flood forecasting

  5. Modeling Broadband Microwave Structures by Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    V. Otevrel

    2004-06-01

    Full Text Available The paper describes the exploitation of feed-forward neural networksand recurrent neural networks for replacing full-wave numerical modelsof microwave structures in complex microwave design tools. Building aneural model, attention is turned to the modeling accuracy and to theefficiency of building a model. Dealing with the accuracy, we describea method of increasing it by successive completing a training set.Neural models are mutually compared in order to highlight theiradvantages and disadvantages. As a reference model for comparisons,approximations based on standard cubic splines are used. Neural modelsare used to replace both the time-domain numeric models and thefrequency-domain ones.

  6. An in vitro model to investigate filling of lateral canals.

    Science.gov (United States)

    Venturi, Mauro; Di Lenarda, Roberto; Prati, Carlo; Breschi, Lorenzo

    2005-12-01

    Aims of this work were to examine lateral canals in extracted teeth, to propose a new technique to produce artificial lateral canals, and to compare two obturation techniques. Cleared roots were examined to record measure and shape of lateral canals. Artificial lateral canals were prepared on human demineralized teeth before final clearing. Specimens were divided in two groups: canals of group 1 were filled with Schilder's technique, canals of group 2 were filled with vertical compaction with apical backfilling. Stereomicroscopic analysis of lateral canal filling revealed lower filling rates in apical canals compared to coronal ones and higher filling rates with "vertical compaction with apical backfilling" compared to Schilder's group. The tested procedure appears to be a reliable technique to obtain standardized lateral canals and to compare filling procedures.

  7. Using an Artificial Neural Network Approach for Supplier Evaluation Process and a Sectoral Application

    Directory of Open Access Journals (Sweden)

    A. Yeşim Yayla

    2011-02-01

    Full Text Available In this study, a-three layered feed-forward backpropagation Artificial Neural Network (ANN model is developed for the supplier firms in ceramic sector on the bases of user effectiveness for using concurrent engineering method. The developed model is also questioned for its usability in the supplier evaluation process. The network's independent variables of the developed model are considered as input variables of the network and dependent variables are used as output variables. The values of these variables are determined with factor analysis. For obtaining the date set to be used in the analysis, a questionnaire form with 34 questions explaining the network's input and output variables are prepared and sent out to 52 firms active in related sector. For obtaining more accurate results from the network, the questions having factor load below 0,6 are eliminated from the analysis. With the elimination of the questions from the analysis, the answers given for 22 questions explaining 8 input variables are used for the evaluation the network's inputs, the answers given for 3 questions explaining output variables are used for the evaluation the network's outputs. The data set of the network's are divided into four equal groups with k-fold method in order to get four different alternative network structures. As a conclusion, the forecasted firm scores giving the minimum error from the network test simulation and real firm scores are found to be very close to each other, thus, it is concluded that the developed artificial neural network model can be used effectively in the supplier evaluation process.

  8. Forecasting the Tehran Stock Market by Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Reza Aghababaeyan

    2011-09-01

    Full Text Available One of the most important problems in modern finance is finding efficient ways to summarize and visualize the stock market data to give individuals or institutions useful information about the market behavior for investment decisions. The enormous amount of valuable data generated by the stock market has attracted researchers to explore this problem domain using different methodologies. Potential significant benefits of solving these problems motivated extensive research for years. In this paper, computational data mining methodology was used to predict seven major stock market indexes. Two learning algorithms including Linear Regression and Neural Network Standard feed-forward back prop (FFB were tested and compared. The models were trained from four years of historical data from March 2007 to February 2011 in order to predict the major stock prices indexes in the Iran (Tehran Stock Exchange. The performance of these prediction models was evaluated using two widely used statistical metrics. We can show that using Neural Network Standard feed-forward back prop (FFB algorithm resulted in better prediction accuracy. In addition, traditional knowledge shows that a longer training period with more training data could help to build a more accurate prediction model. However, as the stock market in Iran has been highly fluctuating in the past two years, this paper shows that data collected from a closer and shorter period could help to reduce the prediction error for such highly speculated fast changing environment.

  9. PREDICTION OF FIGHT OR FLIGHT RESPONSE USING ARTIFICIAL NEURAL NETWORKS

    Directory of Open Access Journals (Sweden)

    Abhijit Suresh

    2014-01-01

    Full Text Available The modern society has posed several threats to the public. Public security is declining with increasing anti-social behaviour. Cases of rape and terrorist attacks have become increasingly common and there is a strong demand for a security system to control such modalities. Anti-social behaviour is a key issue of public concern. Public perceptions, however, have been improving recently. The vital response to physical and emotional danger is called fight or flight response. It is a basic survival mechanism occurring in response to a specific stimulus, such as pain or the threat of danger. Predicting the flight and fight response is an important aspect to identify possible areas susceptible to such events and provide emergency assistance to the victims involved. This study analyses various physiological changes associated with fight or flight response and proposes an approach to predict measures that determines whether an individual is under fear caused due the perceived threat. The proposed approach uses feed forward neural networks with back propagation algorithm. With the physiological changes such as blood pressure, heart rate and respiratory rate as inputs, the optimal configuration of neural network was configured and the proposed system is able to predict the measure of fight or flight response with minimal error. By monitoring and identifying the fear measure it is possible to prevent or reduce the damage to the society by activities such as rape and terrorist attacks.

  10. EXPERIMENT BASED FAULT DIAGNOSIS ON BOTTLE FILLING PLANT WITH LVQ ARTIFICIAL NEURAL NETWORK ALGORITHM

    Directory of Open Access Journals (Sweden)

    Mustafa DEMETGÜL

    2008-01-01

    Full Text Available In this study, an artificial neural network is developed to find an error rapidly on pneumatic system. Also the ANN prevents the system versus the failure. The error on the experimental bottle filling plant can be defined without any interference using analog values taken from pressure sensors and linear potentiometers. The sensors and potentiometers are placed on different places of the plant. Neural network diagnosis faults on plant, where no bottle, cap closing cylinder B is not working, bottle cap closing cylinder C is not working, air pressure is not sufficient, water is not filling and low air pressure faults. The fault is diagnosed by artificial neural network with LVQ. It is possible to find an failure by using normal programming or PLC. The reason offing Artificial Neural Network is to give a information where the fault is. However, ANN can be used for different systems. The aim is to find the fault by using ANN simultaneously. In this situation, the error taken place on the pneumatic system is collected by a data acquisition card. It is observed that the algorithm is very capable program for many industrial plants which have mechatronic systems.

  11. An Efficient Technique to Implement Similarity Measures in Text Document Clustering using Artificial Neural Networks Algorithm

    Directory of Open Access Journals (Sweden)

    K. Selvi

    2014-12-01

    Full Text Available Pattern recognition, envisaging supervised and unsupervised method, optimization, associative memory and control process are some of the diversified troubles that can be resolved by artificial neural networks. Problem identified: Of late, discovering the required information in massive quantity of data is the challenging tasks. The model of similarity evaluation is the central element in accomplishing a perceptive of variables and perception that encourage behavior and mediate concern. This study proposes Artificial Neural Networks algorithms to resolve similarity measures. In order to apply singular value decomposition the frequency of word pair is established in the given document. (1 Tokenization: The splitting up of a stream of text into words, phrases, signs, or other significant parts is called tokenization. (2 Stop words: Preceding or succeeding to processing natural language data, the words that are segregated is called stop words. (3 Porter stemming: The main utilization of this algorithm is as part of a phrase normalization development that is characteristically completed while setting up in rank recovery technique. (4 WordNet: The compilation of lexical data base for the English language is called as WordNet Based on Artificial Neural Networks, the core part of this study work extends n-gram proposed algorithm. All the phonemes, syllables, letters, words or base pair corresponds in accordance to the application. Future work extends the application of this same similarity measures in various other neural network algorithms to accomplish improved results.

  12. Fault Diagnosis of Mixed-Signal Analog Circuit using Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Ashwani Kumar Narula

    2015-06-01

    Full Text Available This paper presents parametric fault diagnosis in mixed-signal analog circuit using artificial neural networks. Single parametric faults are considered in this study. A benchmark R2R digital to analog converter circuit has been used as an example circuit for experimental validations. The input test pattern required for testing are reduced to optimum value using sensitivity analysis of the circuit under test. The effect of component tolerances has also been taken care of by performing the Monte-Carlo analysis. In this study parametric fault models are defined for the R2R network of the digital to analog converter. The input test patterns are applied to the circuit under test and the output responses are measured for each fault model covering all the Monte-Carlo runs. The classification of the parametric faults is done using artificial neural networks. The fault diagnosis system is developed in LabVIEW environment in the form of a virtual instrument. The artificial neural network is designed using MATLAB and finally embedded in the virtual instrument. The fault diagnosis is validated with simulated data and with the actual data acquired from the circuit hardware.

  13. Model of Cholera Forecasting Using Artificial Neural Network in Chabahar City, Iran

    Directory of Open Access Journals (Sweden)

    Pezeshki

    2016-02-01

    Full Text Available Background Cholera as an endemic disease remains a health issue in Iran despite decrease in incidence. Since forecasting epidemic diseases provides appropriate preventive actions in disease spread, different forecasting methods including artificial neural networks have been developed to study parameters involved in incidence and spread of epidemic diseases such as cholera. Objectives In this study, cholera in rural area of Chabahar, Iran was investigated to achieve a proper forecasting model. Materials and Methods Data of cholera was gathered from 465 villages, of which 104 reported cholera during ten years period of study. Logistic regression modeling and correlate bivariate were used to determine risk factors and achieve possible predictive model one-hidden-layer perception neural network with backpropagation training algorithm and the sigmoid activation function was trained and tested between the two groups of infected and non-infected villages after preprocessing. For determining validity of prediction, the ROC diagram was used. The study variables included climate conditions and geographical parameters. Results After determining significant variables of cholera incidence, the described artificial neural network model was capable of forecasting cholera event among villages of test group with accuracy up to 80%. The highest accuracy was achieved when model was trained with variables that were significant in statistical analysis describing that the two methods confirm the result of each other. Conclusions Application of artificial neural networking assists forecasting cholera for adopting protective measures. For a more accurate prediction, comprehensive information is required including data on hygienic, social and demographic parameters.

  14. Optimizing particle size for targeting diseased microvasculature: from experiments to artificial neural networks

    Directory of Open Access Journals (Sweden)

    Schrefler BA

    2011-07-01

    Full Text Available Daniela P Boso1, Sei-Young Lee2, Mauro Ferrari3, Bernhard A Schrefler1, Paolo Decuzzi31Department of Structural and Transportation Engineering, University of Padova, Padova, Italy; 2Global Production Technology Center, Samsung Electronics Co Ltd, Republic of Korea; 3The Methodist Hospital Research Institute, Department of Nanomedicine and Biomedical Engineering, Houston, TX, USABackground: Nanoparticles with different sizes, shapes, and surface properties are being developed for the early diagnosis, imaging, and treatment of a range of diseases. Identifying the optimal configuration that maximizes nanoparticle accumulation at the diseased site is of vital importance. In this work, using a parallel plate flow chamber apparatus, it is demonstrated that an optimal particle diameter (dopt exists for which the number (ns of nanoparticles adhering to the vessel walls is maximized. Such a diameter depends on the wall shear rate (S. Artificial neural networks are proposed as a tool to predict ns as a function of S and particle diameter (d, from which to eventually derive dopt. Artificial neural networks are trained using data from flow chamber experiments. Two networks are used, ie, ANN231 and ANN2321, exhibiting an accurate prediction for ns and its complex functional dependence on d and S. This demonstrates that artificial neural networks can be used effectively to minimize the number of experiments needed without compromising the accuracy of the study. A similar procedure could potentially be used equally effectively for in vivo analysis.Keywords: nanoparticle, optimal configuration, vascular adhesion, laminar flow, wall shear rate, artificial neural networks

  15. Evaluation and scoring of radiotherapy treatment plans using an artificial neural network

    International Nuclear Information System (INIS)

    Purpose: The objective of this work was to demonstrate the feasibility of using an artificial neural network to predict the clinical evaluation of radiotherapy treatment plans. Methods and Materials: Approximately 150 treatment plans were developed for 16 patients who received external-beam radiotherapy for soft-tissue sarcomas of the lower extremity. Plans were assigned a figure of merit by a radiation oncologist using a five-point rating scale. Plan scoring was performed by a single physician to ensure consistency in rating. Dose-volume information extracted from a training set of 511 treatment plans on 14 patients was correlated to the physician-generated figure of merit using an artificial neural network. The neural network was tested with a test set of 19 treatment plans on two patients whose plans were not used in the training of the neural net. Results: Physician scoring of treatment plans was consistent to within one point on the rating scale 88% of the time. The neural net reproduced the physician scores in the training set to within one point approximately 90% of the time. It reproduced the physician scores in the test set to within one point approximately 83% of the time. Conclusions: An artificial neural network can be trained to generate a score for a treatment plan that can be correlated to a clinically-based figure of merit. The accuracy of the neural net in scoring plans compares well with the reproducibility of the clinical scoring. The system of radiotherapy treatment plan evaluation using an artificial neural network demonstrates promise as a method for generating a clinically relevant figure of merit

  16. Artificial Hormone Reaction Networks: Towards Higher Evolvability in Evolutionary Multi-Modular Robotics

    CERN Document Server

    Hamann, Heiko; Schmickl, Thomas; Crailsheim, Karl

    2010-01-01

    The semi-automatic or automatic synthesis of robot controller software is both desirable and challenging. Synthesis of rather simple behaviors such as collision avoidance by applying artificial evolution has been shown multiple times. However, the difficulty of this synthesis increases heavily with increasing complexity of the task that should be performed by the robot. We try to tackle this problem of complexity with Artificial Homeostatic Hormone Systems (AHHS), which provide both intrinsic, homeostatic processes and (transient) intrinsic, variant behavior. By using AHHS the need for pre-defined controller topologies or information about the field of application is minimized. We investigate how the principle design of the controller and the hormone network size affects the overall performance of the artificial evolution (i.e., evolvability). This is done by comparing two variants of AHHS that show different effects when mutated. We evolve a controller for a robot built from five autonomous, cooperating modu...

  17. Growing adaptive machines combining development and learning in artificial neural networks

    CERN Document Server

    Bredeche, Nicolas; Doursat, René

    2014-01-01

    The pursuit of artificial intelligence has been a highly active domain of research for decades, yielding exciting scientific insights and productive new technologies. In terms of generating intelligence, however, this pursuit has yielded only limited success. This book explores the hypothesis that adaptive growth is a means of moving forward. By emulating the biological process of development, we can incorporate desirable characteristics of natural neural systems into engineered designs, and thus move closer towards the creation of brain-like systems. The particular focus is on how to design artificial neural networks for engineering tasks. The book consists of contributions from 18 researchers, ranging from detailed reviews of recent domains by senior scientists, to exciting new contributions representing the state of the art in machine learning research. The book begins with broad overviews of artificial neurogenesis and bio-inspired machine learning, suitable both as an introduction to the domains and as a...

  18. 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°.

  19. A New Procedure for Damage Assessment of Prestressed Concrete Beams Using Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    K. Sumangala

    2011-01-01

    Full Text Available A damage assessment procedure has been developed using artificial neural network (ANN for prestressed concrete beams. The methodology had been formulated using the results obtained from an experimental study conducted in the laboratory. Prestressed concrete (PSC rectangular beams were cast, and pitting corrosion was introduced in the prestressing wires and was allowed to be snapped using accelerated corrosion process. Both static and dynamic tests were conducted to study the behaviour of perfect and damaged beams. The measured output from both static and dynamic tests was taken as input to train the neural network. Back propagation network was chosen for this purpose, which was written using the programming package MATLAB. The trained network was tested using separate test data obtained from the tests. A damage assessment procedure was developed using the trained network, it was validated using the data available in literature, and the outcome is presented in this paper.

  20. Fault tolerance of artificial neural networks with applications in critical systems

    Science.gov (United States)

    Protzel, Peter W.; Palumbo, Daniel L.; Arras, Michael K.

    1992-01-01

    This paper investigates the fault tolerance characteristics of time continuous recurrent artificial neural networks (ANN) that can be used to solve optimization problems. The principle of operations and performance of these networks are first illustrated by using well-known model problems like the traveling salesman problem and the assignment problem. The ANNs are then subjected to 13 simultaneous 'stuck at 1' or 'stuck at 0' faults for network sizes of up to 900 'neurons'. The effects of these faults is demonstrated and the cause for the observed fault tolerance is discussed. An application is presented in which a network performs a critical task for a real-time distributed processing system by generating new task allocations during the reconfiguration of the system. The performance degradation of the ANN under the presence of faults is investigated by large-scale simulations, and the potential benefits of delegating a critical task to a fault tolerant network are discussed.

  1. Genetic Algorithms vs. Artificial Neural Networks in Economic Forecasting Process

    Directory of Open Access Journals (Sweden)

    Nicolae Morariu

    2008-01-01

    Full Text Available This paper aims to describe the implementa-tion of a neural network and a genetic algorithm system in order to forecast certain economic indicators of a free market economy. In a free market economy forecasting process precedes the economic planning (a management function, providing important information for the result of the last process. Forecasting represents a starting point in setting of target for a firm, an organization or even a branch of the economy. Thus, the forecasting method used can influence in a significant mode the evolution of an entity. In the following we will describe the forecasting of an economic indicator using two intelligent systems. The difference between the results obtained by this two systems are described in chapter IV.

  2. Properties of artificial networks evolved to contend with natural spectra.

    Science.gov (United States)

    Morgenstern, Yaniv; Rostami, Mohammad; Purves, Dale

    2014-07-22

    Understanding why spectra that are physically the same appear different in different contexts (color contrast), whereas spectra that are physically different appear similar (color constancy) presents a major challenge in vision research. Here, we show that the responses of biologically inspired neural networks evolved on the basis of accumulated experience with spectral stimuli automatically generate contrast and constancy. The results imply that these phenomena are signatures of a strategy that biological vision uses to circumvent the inverse optics problem as it pertains to light spectra, and that double-opponent neurons in early-level vision evolve to serve this purpose. This strategy provides a way of understanding the peculiar relationship between the objective world and subjective color experience, as well as rationalizing the relevant visual circuitry without invoking feature detection or image representation.

  3. Artificial Neural Network-Based Fault Distance Locator for Double-Circuit Transmission Lines

    Directory of Open Access Journals (Sweden)

    Anamika Jain

    2013-01-01

    Full Text Available This paper analyses two different approaches of fault distance location in a double circuit transmission lines, using artificial neural networks. The single and modular artificial neural networks were developed for determining the fault distance location under varying types of faults in both the circuits. The proposed method uses the voltages and currents signals available at only the local end of the line. The model of the example power system is developed using Matlab/Simulink software. Effects of variations in power system parameters, for example, fault inception angle, CT saturation, source strength, its X/R ratios, fault resistance, fault type and distance to fault have been investigated extensively on the performance of the neural network based protection scheme (for all ten faults in both the circuits. Additionally, the effects of network changes: namely, double circuit operation and single circuit operation, have also been considered. Thus, the present work considers the entire range of possible operating conditions, which has not been reported earlier. The comparative results of single and modular neural network indicate that the modular approach gives correct fault location with better accuracy. It is adaptive to variation in power system parameters, network changes and works successfully under a variety of operating conditions.

  4. A comparative study for the estimation of geodetic point velocity by artificial neural networks

    Indian Academy of Sciences (India)

    M Yilmaz; M Gullu

    2014-06-01

    Space geodesy era provides velocity information which results in the positioning of geodetic points by considering the time evolution. The geodetic point positions on the Earth’s surface change over time due to plate tectonics, and these changes have to be accounted for geodetic purposes. The velocity field of geodetic network is determined from GPS sessions. Velocities of the new structured geodetic points within the geodetic network are estimated from this velocity field by the interpolation methods. In this study, the utility of Artificial Neural Networks (ANN) widely applied in diverse fields of science is investigated in order to estimate the geodetic point velocities. Back Propagation Artificial Neural Network (BPANN) and Radial Basis Function Neural Network (RBFNN) are used to estimate the geodetic point velocities. In order to evaluate the performance of ANNs, the velocities are also interpolated by Kriging (KRIG) method. The results are compared in terms of the root mean square error (RMSE) over five different geodetic networks. It was concluded that the estimation of geodetic point velocity by BPANN is more effective and accurate than by KRIG when the points to be estimated are more than the points known.

  5. Reduced-Order Modeling for Flutter/LCO Using Recurrent Artificial Neural Network

    Science.gov (United States)

    Yao, Weigang; Liou, Meng-Sing

    2012-01-01

    The present study demonstrates the efficacy of a recurrent artificial neural network to provide a high fidelity time-dependent nonlinear reduced-order model (ROM) for flutter/limit-cycle oscillation (LCO) modeling. An artificial neural network is a relatively straightforward nonlinear method for modeling an input-output relationship from a set of known data, for which we use the radial basis function (RBF) with its parameters determined through a training process. The resulting RBF neural network, however, is only static and is not yet adequate for an application to problems of dynamic nature. The recurrent neural network method [1] is applied to construct a reduced order model resulting from a series of high-fidelity time-dependent data of aero-elastic simulations. Once the RBF neural network ROM is constructed properly, an accurate approximate solution can be obtained at a fraction of the cost of a full-order computation. The method derived during the study has been validated for predicting nonlinear aerodynamic forces in transonic flow and is capable of accurate flutter/LCO simulations. The obtained results indicate that the present recurrent RBF neural network is accurate and efficient for nonlinear aero-elastic system analysis

  6. The evaluation of removal of calcium hydroxide paste from an artificial standardized groove in the apical root canal using different irrigation methodologies

    NARCIS (Netherlands)

    van der Sluis, L. W. M.; Wu, M. K.; Wesselink, P. R.

    2007-01-01

    Aim: To evaluate the capacity to remove a calcium hydroxide (Ca(OH)(2)) paste from the root canal and to evaluate the efficacy of Ca(OH)(2) removal during passive ultrasonic irrigation using either sodium hypochlorite (NaOCl) or water as an irrigant. Methodology: Sixteen mandibular premolars were us

  7. canal24

    Data.gov (United States)

    California Department of Resources — Canal system center lines in the Central Valley of California and adjacent areas captured from 1:24,000-scale USGS topographic maps. Updates and modifications made...

  8. Determination of type and concentration of DNA nitrogenous bases by Raman spectroscopy using artificial neural networks

    Science.gov (United States)

    Laptinskiy, Kirill A.; Burikov, Sergey A.; Sarmanova, Olga E.; Dolenko, Sergey A.; Dolenko, Tatiana A.

    2016-04-01

    In this article the results of solution of two-parametrical inverse problems of laser Raman spectroscopy of identification and determination of concentration of DNA nitrogenous bases in two-component solutions are presented. Elaboration of methods of control of reactions with DNA strands in remote real-time mode is necessary for solution of one of the basic problems of creation of biocomputers - increase of reliability of molecular DNA-computations. The comparative analysis of two used methods of solution of stated problems has demonstrated convincing advantages of technique of artificial neural networks. Use of artificial neural networks allowed to reach the accuracy of determination of concentration of each base in two-component solutions 0.2-0.3 g/l.

  9. Modeling and Simulation of Road Traffic Noise Using Artificial Neural Network and Regression.

    Science.gov (United States)

    Honarmand, M; Mousavi, S M

    2014-04-01

    Modeling and simulation of noise pollution has been done in a large city, where the population is over 2 millions. Two models of artificial neural network and regression were developed to predict in-city road traffic noise pollution with using the data of noise measurements and vehicle counts at three points of the city for a period of 12 hours. The MATLAB and DATAFIT softwares were used for simulation. The predicted results of noise level were compared with the measured noise levels in three stations. The values of normalized bias, sum of squared errors, mean of squared errors, root mean of squared errors, and squared correlation coefficient calculated for each model show the results of two models are suitable, and the predictions of artificial neural network are closer to the experimental data.

  10. Classification of intelligence quotient via brainwave sub-band power ratio features and artificial neural network.

    Science.gov (United States)

    Jahidin, A H; Megat Ali, M S A; Taib, M N; Tahir, N Md; Yassin, I M; Lias, S

    2014-04-01

    This paper elaborates on the novel intelligence assessment method using the brainwave sub-band power ratio features. The study focuses only on the left hemisphere brainwave in its relaxed state. Distinct intelligence quotient groups have been established earlier from the score of the Raven Progressive Matrices. Sub-band power ratios are calculated from energy spectral density of theta, alpha and beta frequency bands. Synthetic data have been generated to increase dataset from 50 to 120. The features are used as input to the artificial neural network. Subsequently, the brain behaviour model has been developed using an artificial neural network that is trained with optimized learning rate, momentum constant and hidden nodes. Findings indicate that the distinct intelligence quotient groups can be classified from the brainwave sub-band power ratios with 100% training and 88.89% testing accuracies.

  11. A study on precursors leading to geomagnetic storms using artificial neural network

    Indian Academy of Sciences (India)

    Gaurav Singh; A K Singh

    2016-07-01

    Space weather prediction involves advance forecasting of the magnitude and onset time of major geomagneticstorms on Earth. In this paper, we discuss the development of an artificial neural network-basedmodel to study the precursor leading to intense and moderate geomagnetic storms, following halo coronalmass ejection (CME) and related interplanetary (IP) events. IP inputs were considered within a 5-daytime window after the commencement of storm. The artificial neural network (ANN) model training,testing and validation datasets were constructed based on 110 halo CMEs (both full and partial halo andtheir properties) observed during the ascending phase of the 24th solar cycle between 2009 and 2014. Thegeomagnetic storm occurrence rate from halo CMEs is estimated at a probability of 79%, by this model.

  12. A Facial Expression Classification System Integrating Canny, Principal Component Analysis and Artificial Neural Network

    CERN Document Server

    Thai, Le Hoang; Hai, Tran Son

    2011-01-01

    Facial Expression Classification is an interesting research problem in recent years. There are a lot of methods to solve this problem. In this research, we propose a novel approach using Canny, Principal Component Analysis (PCA) and Artificial Neural Network. Firstly, in preprocessing phase, we use Canny for local region detection of facial images. Then each of local region's features will be presented based on Principal Component Analysis (PCA). Finally, using Artificial Neural Network (ANN)applies for Facial Expression Classification. We apply our proposal method (Canny_PCA_ANN) for recognition of six basic facial expressions on JAFFE database consisting 213 images posed by 10 Japanese female models. The experimental result shows the feasibility of our proposal method.

  13. Multiple Artificial Neural Networks with Interaction Noise for Estimation of Spatial Categorical Variables

    Directory of Open Access Journals (Sweden)

    Xiang Huang

    2016-08-01

    Full Text Available This paper presents a multiple artificial neural networks (MANN method with interaction noise for estimating the occurrence probabilities of different classes at any site in space. The MANN consists of several independent artificial neural networks, the number of which is determined by the neighbors around the target location. In the proposed algorithm, the conditional or pre-posterior (multi-point probabilities are viewed as output nodes, which can be estimated by weighted combinations of input nodes: two-point transition probabilities. The occurrence probability of a certain class at a certain location can be easily computed by the product of output probabilities using Bayes’ theorem. Spatial interaction or redundancy information can be measured in the form of interaction noises. Prediction results show that the method of MANN with interaction noise has a higher classification accuracy than the traditional Markov chain random fields (MCRF model and can successfully preserve small-scale features.

  14. Training a Feed-Forward Neural Network with Artificial Bee Colony based Backpropagation Method

    Directory of Open Access Journals (Sweden)

    Sudarshan Nandy

    2012-09-01

    Full Text Available Back-propagation algorithm is one of the most widely used and popular techniques to optimize the feedforward neural network training. Nature inspired meta-heuristic algorithms also provide derivative-freesolution to optimize complex problem. Artificial bee colony algorithm is a nature inspired meta-heuristicalgorithm, mimicking the foraging or food source searching behaviour of bees in a bee colony and thisalgorithm is implemented in several applications for an improved optimized outcome. The proposedmethod in this paper includes an improved artificial bee colony algorithm based back-propagation neuralnetwork training method for fast and improved convergence rate of the hybrid neural network learningmethod. The result is analysed with the genetic algorithm based back-propagation method, and it isanother hybridized procedure of its kind. Analysis is performed over standard data sets, reflecting the lightof efficiency of proposed method in terms of convergence speed and rate.

  15. Prediction of IV curves for a superconducting thin film using artificial neural networks

    Science.gov (United States)

    Kamran, M.; Haider, S. A.; Akram, T.; Naqvi, S. R.; He, S. K.

    2016-07-01

    We propose a framework using artificial neural networks that predicts the IV characteristics of a superconducting thin film with square array of nano-engineered periodic antidots, called holes. We adopt the conventionally used commercial physical properties measurement system to obtain a dataset comprising transport measurements, and use this dataset to train our artificial neural network. Once trained, the model is capable of predicting the curve for varying temperature and magnetic flux values, which are cross validated by the physical properties measurement system. Consistent with the works in literature, our framework suggests Josephson Junctions like behavior near transition temperature and at stronger magnetic fields. Our study is important since repeated measurements using the conventional method are time consuming and costly; we demonstrate that the proposed method may be effectively used to classify the IV characteristics over a wide range of temperature and magnetic field values.

  16. Modeling the thermotaxis behavior of C.elegans based on the artificial neural network.

    Science.gov (United States)

    Li, Mingxu; Deng, Xin; Wang, Jin; Chen, Qiaosong; Tang, Yun

    2016-07-01

    ASBTRACT This research aims at modeling the thermotaxis behavior of C.elegans which is a kind of nematode with full clarified neuronal connections. Firstly, this work establishes the motion model which can perform the undulatory locomotion with turning behavior. Secondly, the thermotaxis behavior is modeled by nonlinear functions and the nonlinear functions are learned by artificial neural network. Once the artificial neural networks have been well trained, they can perform the desired thermotaxis behavior. Last, several testing simulations are carried out to verify the effectiveness of the model for thermotaxis behavior. This work also analyzes the different performances of the model under different environments. The testing results reveal the essence of the thermotaxis of C.elegans to some extent, and theoretically support the research on the navigation of the crawling robots.

  17. Prediction of Global Solar Radiation in India Using Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Rajiv Gupta

    2016-06-01

    Full Text Available Increasing global warming and decreasing fossil fuel reserves has necessitated the use of renewable energy resources like solar energy in India. To maximize return on a solar farm, it had to be set up at a place with high solar radiation. The solar radiation values are available only for a small number of places and must be interpolated for the rest. This paper utilizes Artificial Neural Network in interpolation, by obtaining a function with input as combinations of 7 geographical and meteorological parameters affecting radiation, and output as global solar radiation. Data considered was of past 9 years for 13 Indian cities. Low error values and high coefficient of determination values thus obtained, verified that the results were accurate in terms of the original solar radiation data known. Thus, artificial neural network can be used to interpolate the solar radiation for the places of interest depending on the availability of the data.

  18. IMAGE SEGMENTATION WITH ARTIFICIAL NEURAL NETWORK FOR NUTRIENT DEFICIENCY IN COTTON CROP

    Directory of Open Access Journals (Sweden)

    Maicon A. Sartin

    2014-01-01

    Full Text Available The leaf analysis in a crop can present the need of a nutrient determined in the plant. The macronutrients deficiency in the cotton crop can be identified by specific type of colors variation by leaves images. Early identification of macronutrients deficiency can help in the growing suitable of the crop and reduce the use of agricultural inputs. This study investigates the image segmentation of the cotton leaves with deficiency of the phosphor. The segmentation is performed by difference of leaf pigmentation, according with the pattern related to macronutrient type in deficit and the cultivate. The image segmentation is made by an artificial neural network and the Otsu method. The results show satisfactory values with an optimized artificial neural network and better than the Otsu method. The results are presented by images and distinct parameters of quality analysis in the segmentation.

  19. Integrating fuzzy Delphi method with artificial neural network for demand forecasting of power engineering company

    Directory of Open Access Journals (Sweden)

    Golam Kabir

    2012-08-01

    Full Text Available An organization has to make the right decisions in time depending on demand information to enhance the commercial competitive advantage in a constantly fluctuating business environment. Therefore, estimating the demand quantity for the next period most likely appears to be crucial. Manufacturing companies consider forecasting a crucial process for effectively guiding several activities, and research has devoted particular attention to this issue. The objective of the paper is to propose a new forecasting mechanism which is modeled by integrating Fuzzy Delhi Method (FDM with Artificial Neural Network (ANN techniques to manage the demand with incomplete information. Artificial neural networks has been applied as it is capable to model complex, nonlinear processes without having to assume the form of the relationship between input and output variables. The effectiveness of the proposed approach to the demand forecasting issue is demonstrated for a 20/25 MVA Distribution Transformer from Energypac Engineering Limited, a leading power engineering company of Bangladesh.

  20. A Red-Light Running Prevention System Based on Artificial Neural Network and Vehicle Trajectory Data

    Directory of Open Access Journals (Sweden)

    Pengfei Li

    2014-01-01

    Full Text Available The high frequency of red-light running and complex driving behaviors at the yellow onset at intersections cannot be explained solely by the dilemma zone and vehicle kinematics. In this paper, the author presented a red-light running prevention system which was based on artificial neural networks (ANNs to approximate the complex driver behaviors during yellow and all-red clearance and serve as the basis of an innovative red-light running prevention system. The artificial neural network and vehicle trajectory are applied to identify the potential red-light runners. The ANN training time was also acceptable and its predicting accurate rate was over 80%. Lastly, a prototype red-light running prevention system with the trained ANN model was described. This new system can be directly retrofitted into the existing traffic signal systems.

  1. A study on precursors leading to geomagnetic storms using artificial neural network

    Science.gov (United States)

    Singh, Gaurav; Singh, A. K.

    2016-06-01

    Space weather prediction involves advance forecasting of the magnitude and onset time of major geomagnetic storms on Earth. In this paper, we discuss the development of an artificial neural network-based model to study the precursor leading to intense and moderate geomagnetic storms, following halo coronal mass ejection (CME) and related interplanetary (IP) events. IP inputs were considered within a 5-day time window after the commencement of storm. The artificial neural network (ANN) model training, testing and validation datasets were constructed based on 110 halo CMEs (both full and partial halo and their properties) observed during the ascending phase of the 24th solar cycle between 2009 and 2014. The geomagnetic storm occurrence rate from halo CMEs is estimated at a probability of 79%, by this model.

  2. Prediction of Atmospheric Pressure at Ground Level using Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Angshuman Ray

    2013-01-01

    Full Text Available Prediction of Atmospheric Pressure is one important and challenging task that needs lot of attention and study for analyzing atmospheric conditions. Advent of digital computers and development of data driven artificial intelligence approaches like Artificial Neural Networks (ANN have helped in numerical prediction of pressure. However, very few works have been done till now in this area. The present study developed an ANN model based on the past observations of several meteorological parameters like temperature, humidity, air pressure and vapour pressure as an input for training the model. The novel architecture of the proposed model contains several multilayer perceptron network (MLP to realize better performance. The model is enriched by analysis of alternative hybrid model of k-means clustering and MLP. The improvement of the performance in the prediction accuracy has been demonstrated by the automatic selection of the appropriate cluster

  3. Predicting Learners Performance Using Artificial Neural Networks in Linear Programming Intelligent Tutoring System

    Directory of Open Access Journals (Sweden)

    Samy S. Abu Naser

    2012-04-01

    Full Text Available In this paper we present a technique that employ Artificial Neural Networks and expert systems to obtain knowledge for the learner model in the Linear Programming Intelligent Tutoring System(LP-ITS to be able to determine the academic performance level of the learners in order to offer him/her the properdifficulty level of linear programming problems to solve. LP-ITS uses Feed forward Back-propagation algorithm to be trained with a group of learners data to predict their academic performance. Furthermore, LP-ITS uses an Expert System to decide the proper difficulty level that is suitable with the predicted academic performance of the learner. Several tests have been carried out to examine adherence to real time data. The accuracy of predicting the performance of the learners is very high and thus states that the Artificial Neural Network is skilled enough to make suitable predictions.

  4. Predicting Learners Performance Using Artificial Neural Networks in Linear Programming Intelligent Tutoring System

    Directory of Open Access Journals (Sweden)

    Samy S. Abu Naser

    2012-03-01

    Full Text Available In this paper we present a technique that employ Artificial Neural Networks and expert systems to obtain knowledge for the learner model in the Linear Programming Intelligent Tutoring System(LP-ITS to be able to determine the academic performance level of the learners in order to offer him/her the proper difficulty level of linear programming problems to solve. LP-ITS uses Feed forward Back-propagation algorithm to be trained with a group of learners data to predict their academic performance. Furthermore, LP-ITS uses an Expert System to decide the proper difficulty level that is suitable with the predicted academic performance of the learner. Several tests have been carried out to examine adherence to real time data. The accuracy of predicting the performance of the learners is very high and thus states that the Artificial Neural Network is skilled enough to make suitable predictions.

  5. Application of Artificial Neural Networks in Aircraft Maintenance, Repair and Overhaul Solutions

    CERN Document Server

    Paul, Soumitra; Jasani, Devashish; Dudhwewala, Rachit; Gowda, Vijay Bore; Nair, T R Gopalakrishnan

    2010-01-01

    This paper reviews application of Artificial Neural Networks in Aircraft Maintenance, Repair and Overhaul (MRO). MRO solutions are designed to facilitate the authoring and delivery of maintenance and repair information to the line maintenance technicians who need to improve aircraft repair turn around time, optimize the efficiency and consistency of fleet maintenance and ensure regulatory compliance. The technical complexity of aircraft systems, especially in avionics, has increased to the point at which it poses a significant troubleshotting and repair challenge for MRO personnel. As per the existing scenario, the MRO systems in place are inefficient. In this paper, we propose the centralization and integration of the MRO database to increase its efficiency. Moreover the implementation of Artificial Neural Networks in this system can rid the system of many of its deficiencies. In order to make the system more efficient we propose to integrate all the modules so as to reduce the efficacy of repair.

  6. Artificial neural network in studying factors of hepatic cancer recurrence after hepatectomy

    Institute of Scientific and Technical Information of China (English)

    HE Jia; HE Xian-min; ZHANG Zhi-jian

    2002-01-01

    Objective: To explore the affecting factors of liver cancer recurrence after hepatectomy. Methods:The BP artificial neural network - Cox regression was introduced to analyze the factors of recurrence in1 457 patients. Results: The affecting factors statistically significant to liver cancer prognosis was selected.There were 18 factors to be selected by uni-factor analysis, and 9 factors to be selected by multi-factor analysis. Conclusion: The 9 factors selected can be used as important indexes to evaluate the recurrence of liver cancer after hepatectomy. The artificial neural network is a better method to analyze the clinical data, which provides scientific and objective data for evaluating prognosis of liver cancer.

  7. Modeling, Optimization and simulation of Rotary Furnace using Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Dr. R, K. Jain,

    2011-04-01

    Full Text Available This paper deals with modeling and simulation of LDO fired rotary furnace using feed forward modeling method of artificial neural network (ANN.The authors conducted experimental investigations onfuel consumption in a rotary furnace in an industry. It was observed that 6% oxygen enrichment of the air preheated up to 4600C simultaneously with reduction of air volume to 75% of its theoretical requirement lowered the specific fuel consumption to 0.260 lit/kg..The compact heat exchanger with 533 fins was used for preheating the air. Accordingly the emission level was also considerably reduced. The feed forward modeling method of artificial neural network contained in MAT LAB software was used for modeling andoptimization of specific fuel consumption. The percentage variation, between actual experimental data and same data when simulated is +1.730%, and other feasible simulated datas is +6.192%,-3.038%,-5.692%,and+0.115%which is fairly acceptable.

  8. Forecasting the Colorado River Discharge Using an Artificial Neural Network (ANN) Approach

    CERN Document Server

    Mehrkesh, Amirhossein

    2014-01-01

    Artificial Neural Network (ANN) based model is a computational approach commonly used for modeling the complex relationships between input and output parameters. Prediction of the flow rate of a river is a requisite for any successful water resource management and river basin planning. In the current survey, the effectiveness of an Artificial Neural Network was examined to predict the Colorado River discharge. In this modeling process, an ANN model was used to relate the discharge of the Colorado River to such parameters as the amount of precipitation, ambient temperature and snowpack level at a specific time of the year. The model was able to precisely study the impact of climatic parameters on the flow rate of the Colorado River.

  9. A study on precursors leading to geomagnetic storms using artificial neural network

    Science.gov (United States)

    Singh, Gaurav; Singh, A. K.

    2016-07-01

    Space weather prediction involves advance forecasting of the magnitude and onset time of major geomagnetic storms on Earth. In this paper, we discuss the development of an artificial neural network-based model to study the precursor leading to intense and moderate geomagnetic storms, following halo coronal mass ejection (CME) and related interplanetary (IP) events. IP inputs were considered within a 5-day time window after the commencement of storm. The artificial neural network (ANN) model training, testing and validation datasets were constructed based on 110 halo CMEs (both full and partial halo and their properties) observed during the ascending phase of the 24th solar cycle between 2009 and 2014. The geomagnetic storm occurrence rate from halo CMEs is estimated at a probability of 79%, by this model.

  10. Improved Space Surveillance Network (SSN) Scheduling using Artificial Intelligence Techniques

    Science.gov (United States)

    Stottler, D.

    There are close to 20,000 cataloged manmade objects in space, the large majority of which are not active, functioning satellites. These are tracked by phased array and mechanical radars and ground and space-based optical telescopes, collectively known as the Space Surveillance Network (SSN). A better SSN schedule of observations could, using exactly the same legacy sensor resources, improve space catalog accuracy through more complementary tracking, provide better responsiveness to real-time changes, better track small debris in low earth orbit (LEO) through efficient use of applicable sensors, efficiently track deep space (DS) frequent revisit objects, handle increased numbers of objects and new types of sensors, and take advantage of future improved communication and control to globally optimize the SSN schedule. We have developed a scheduling algorithm that takes as input the space catalog and the associated covariance matrices and produces a globally optimized schedule for each sensor site as to what objects to observe and when. This algorithm is able to schedule more observations with the same sensor resources and have those observations be more complementary, in terms of the precision with which each orbit metric is known, to produce a satellite observation schedule that, when executed, minimizes the covariances across the entire space object catalog. If used operationally, the results would be significantly increased accuracy of the space catalog with fewer lost objects with the same set of sensor resources. This approach inherently can also trade-off fewer high priority tasks against more lower-priority tasks, when there is benefit in doing so. Currently the project has completed a prototyping and feasibility study, using open source data on the SSN's sensors, that showed significant reduction in orbit metric covariances. The algorithm techniques and results will be discussed along with future directions for the research.

  11. Generalized in vitro-in vivo relationship (IVIVR model based on artificial neural networks

    Directory of Open Access Journals (Sweden)

    Mendyk A

    2013-03-01

    Full Text Available Aleksander Mendyk,1 Pawel Tuszynski,1 Sebastian Polak,2 Renata Jachowicz1 1Department of Pharmaceutical Technology and Biopharmaceutics, 2Department of Social Pharmacy, Faculty of Pharmacy, Jagiellonian University Medical College, Kraków, Poland Background: The aim of this study was to develop a generalized in vitro-in vivo relationship (IVIVR model based on in vitro dissolution profiles together with quantitative and qualitative composition of dosage formulations as covariates. Such a model would be of substantial aid in the early stages of development of a pharmaceutical formulation, when no in vivo results are yet available and it is impossible to create a classical in vitro-in vivo correlation (IVIVC/IVIVR. Methods: Chemoinformatics software was used to compute the molecular descriptors of drug substances (ie, active pharmaceutical ingredients and excipients. The data were collected from the literature. Artificial neural networks were used as the modeling tool. The training process was carried out using the 10-fold cross-validation technique. Results: The database contained 93 formulations with 307 inputs initially, and was later limited to 28 in a course of sensitivity analysis. The four best models were introduced into the artificial neural network ensemble. Complete in vivo profiles were predicted accurately for 37.6% of the formulations. Conclusion: It has been shown that artificial neural networks can be an effective predictive tool for constructing IVIVR in an integrated generalized model for various formulations. Because IVIVC/IVIVR is classically conducted for 2–4 formulations and with a single active pharmaceutical ingredient, the approach described here is unique in that it incorporates various active pharmaceutical ingredients and dosage forms into a single model. Thus, preliminary IVIVC/IVIVR can be available without in vivo data, which is impossible using current IVIVC/IVIVR procedures. Keywords: artificial neural networks

  12. Recognition of cDNA microarray image Using Feedforward artificial neural network

    OpenAIRE

    R. M. Farouk; E. M. Badr; M. A. SayedElahl

    2014-01-01

    The complementary DNA (cDNA) sequence is considered to be the magic biometric technique for personal identification. In this paper, we present a new method for cDNA recognition based on the artificial neural network (ANN). Microarray imaging is used for the concurrent identification of thousands of genes. We have segmented the location of the spots in a cDNA microarray. Thus, a precise localization and segmenting of a spot are essential to obtain a more accurate intensity measurement, leading...

  13. Connecting adaptive behaviour and expectations in models of innovation: The Potential Role of Artificial Neural Networks

    OpenAIRE

    Murat Yildizoglu

    2002-01-01

    In this methodological work I explore the possibility of explicitly modelling expectations conditioning the R&D decisions of firms. In order to isolate this problem from the controversies of cognitive science, I propose a black box strategy through the concept of “internal model”. The last part of the article uses artificial neural networks to model the expectations of firms in a model of industry dynamics based on Nelson & Winter (1982).

  14. Time-Delay Artificial Neural Network Computing Models for Predicting Shelf Life of Processed Cheese

    OpenAIRE

    Sumit Goyal; Gyanendra Kumar Goyal

    2012-01-01

    This paper presents the capability of Time–delay artificial neural network models for predicting shelf life of processed cheese. Datasets were divided into two subsets (30 for training and 6 for validation). Models with single and multi layers were developed and compared with each other. Mean Square Error, Root Mean Square Error, Coefficient of Determination and Nash -
    Sutcliffo Coefficient were used as performance evaluators, Time- delay model predicted the shelf life of...

  15. Artificial neural networks applied to quantitative elemental analysis of organic material using PIXE

    Energy Technology Data Exchange (ETDEWEB)

    Correa, R. [Universidad Tecnologica Metropolitana, Departamento de Fisica, Av. Jose Pedro Alessandri 1242, Nunoa, Santiago (Chile)]. E-mail: rcorrea@utem.cl; Chesta, M.A. [Universidad Nacional de Cordoba, Facultad de Matematica, Astronomia y Fisica, Medina Allende s/n Ciudad Universitaria, 5000 Cordoba (Argentina)]. E-mail: chesta@famaf.unc.edu.ar; Morales, J.R. [Universidad de Chile, Facultad de Ciencias, Departamento de Fisica, Las Palmeras 3425, Nunoa, Santiago (Chile)]. E-mail: rmorales@uchile.cl; Dinator, M.I. [Universidad de Chile, Facultad de Ciencias, Departamento de Fisica, Las Palmeras 3425, Nunoa, Santiago (Chile)]. E-mail: mdinator@uchile.cl; Requena, I. [Universidad de Granada, Departamento de Ciencias de la Computacion e Inteligencia Artificial, Daniel Saucedo Aranda s/n, 18071 Granada (Spain)]. E-mail: requena@decsai.ugr.es; Vila, I. [Universidad de Chile, Facultad de Ciencias, Departamento de Ecologia, Las Palmeras 3425, Nunoa, Santiago (Chile)]. E-mail: limnolog@uchile.cl

    2006-08-15

    An artificial neural network (ANN) has been trained with real-sample PIXE (particle X-ray induced emission) spectra of organic substances. Following the training stage ANN was applied to a subset of similar samples thus obtaining the elemental concentrations in muscle, liver and gills of Cyprinus carpio. Concentrations obtained with the ANN method are in full agreement with results from one standard analytical procedure, showing the high potentiality of ANN in PIXE quantitative analyses.

  16. Determination of Boiling Range of Xylene Mixed in PX Device Using Artificial Neural Networks

    OpenAIRE

    Zhu, Ting; Zhu, Yuxuan; Yang, Hong; Li, Hao

    2014-01-01

    Determination of boiling range of xylene mixed in PX device is currently a crucial topic in the practical applications because of the recent disputes of PX project in China. In our study, instead of determining the boiling range of xylene mixed by traditional approach in laboratory or industry, we successfully established two Artificial Neural Networks (ANNs) models to determine the initial boiling point and final boiling point respectively. Results show that the Multilayer Feedforward Neural...

  17. A Faster Convergence Artificial Bee Colony Algorithm in Sensor Deployment for Wireless Sensor Networks

    OpenAIRE

    Yu, Xiangyu; Zhang, Jiaxin; Fan, Jiaru; Zhang, Tao

    2013-01-01

    In wireless sensor networks (WSN), sensor deployment is one of the main topics for enhancing the sensor’s coverage rate. In this paper, by modifying updating equation of onlooker bee and scout bee of original artificial bee colony (ABC) algorithm, a sensor deployment algorithm based on the modified ABC algorithm is proposed. Some new parameters such as forgetting and neighbor factor for accelerating the convergence speed and probability of mutant for maximizing the coverage rate are introduce...

  18. Artificial Neural Networks for Estimating Soil Water Retention Curve Using Fitted and Measured Data

    OpenAIRE

    Tirzah Moreira de Melo; Olavo Correa Pedrollo

    2015-01-01

    Artificial neural networks for estimating the soil water retention curve have been developed considering measured data and require a large quantity of soil samples because only retention curve data obtained for the same set of matric potentials can be used. In order to preclude this drawback, we present two ANN models which tested the performance of ANNs trained with fitted water contents data. These models were compared to a recent new ANN approach for predicting water retention curve, the p...

  19. A Comparison of the Monetary Model and Artificial Neural Networks in Exchange Rate Forecasting

    OpenAIRE

    Filiz Ozkan

    2012-01-01

    Exchange value is one of the significant tools for investors in decision making. Since exchange values are volatile and they change within short periods, investors need an effective method to minimize the risk. This study compares the prediction performances of artificial neural networks, which is recently being used as an effective tool of prediction, and the monetary model, which is one of the methods to predict structural exchange rates. In this study exchange rates of Turkish Lira against...

  20. Cancer risk at low doses of ionizing radiation: artificial neural networks inference from atomic bomb survivors

    OpenAIRE

    Sasaki, Masao S.; Tachibana, Akira; Takeda, Shunichi

    2013-01-01

    Cancer risk at low doses of ionizing radiation remains poorly defined because of ambiguity in the quantitative link to doses below 0.2 Sv in atomic bomb survivors in Hiroshima and Nagasaki arising from limitations in the statistical power and information available on overall radiation dose. To deal with these difficulties, a novel nonparametric statistics based on the ‘integrate-and-fire’ algorithm of artificial neural networks was developed and tested in cancer databases established by the R...

  1. Cancer risk at low doses of ionizing radiation: artificial neural networks inference from atomic bomb survivors.

    OpenAIRE

    Sasaki, Masao S.; Tachibana, Akira; Takeda, Shunichi

    2014-01-01

    Cancer risk at low doses of ionizing radiation remains poorly defined because of ambiguity in the quantitative link to doses below 0.2 Sv in atomic bomb survivors in Hiroshima and Nagasaki arising from limitations in the statistical power and information available on overall radiation dose. To deal with these difficulties, a novel nonparametric statistics based on the 'integrate-and-fire' algorithm of artificial neural networks was developed and tested in cancer databases established by the R...

  2. EM-based optimization of microwave circuits using artificial neural networks: the state of the art

    OpenAIRE

    Rayas-Sánchez, José E.

    2004-01-01

    This paper reviews the current state-of-the-art in electromagnetic (EM)-based design and optimization of microwave circuits using artificial neural networks (ANNs). Measurement-based design of microwave circuits using ANNs is also reviewed. The conventional microwave neural optimization approach is surveyed, along with typical enhancing techniques, such as segmentation, decomposition, hierarchy, design of experiments and clusterization. Innovative strategies for ANN-based design exploiting...

  3. A Study of Using Artificial Neural Network in a Non-linear Centrifugal Compressor System

    OpenAIRE

    Prof. Pin-Chang Chen; Prof. Hung-Teng Chang; Mr. Armada Hsu

    2012-01-01

    This study adopts the centrifugal compressor system which produces the nitric acid equipment in China Petrochemical Development Corporation’s Plant. The system is non-linear and its manufacturing process is changeable, which the traditional PID (Proportional-Integral-Differential) control method is difficult to apply. This study is intended to apply the artificial neural network methodto test and forecast the compressor performance. By means of collecting the PLC (Programmable Logic Controlle...

  4. Classification of Siirt and Long Type Pistachios (Pistacia vera L.) by Artificial Neural Networks

    OpenAIRE

    Koklu, Murat; Unlersen, Muhammed Fahri; Sabanci, Kadir

    2015-01-01

    Quality is one of the important factors in agricultural products marketing. Grading machines have great role in quality control systems. The most efficient method used in grading machines today is image processing. This study aims to do the grading of high valued agricultural product of our land called pistachio that has two different types namely Siirt and Long type of pistachios by image processing methods and artificial neural networks. Photos of Siirt and long type of pistachios are taken...

  5. Earthquake-induced landslide-susceptibility mapping using an artificial neural network

    OpenAIRE

    Lee, S.; Evangelista, D. G.

    2006-01-01

    The purpose of this study was to apply and verify landslide-susceptibility analysis techniques using an artificial neural network and a Geographic Information System (GIS) applied to Baguio City, Philippines. The 16 July 1990 earthquake-induced landslides were studied. Landslide locations were identified from interpretation of aerial photographs and field survey, and a spatial database was constructed from topographic maps, geology, land cover and terrain mapping units. Factors that influence...

  6. Artificial Neural Network based Diagnostic Model For Causes of Success and Failures

    OpenAIRE

    Kaur, Bikrampal; Aggarwal, Himanshu

    2010-01-01

    In this paper an attempt has been made to identify most important human resource factors and propose a diagnostic model based on the back-propagation and connectionist model approaches of artificial neural network (ANN). The focus of the study is on the mobile -communication industry of India. The ANN based approach is particularly important because conventional approaches (such as algorithmic) to the problem solving have their inherent disadvantages. The algorithmic approach is well-suited t...

  7. Predicting the Effects of Medical Waste in the Environment Using Artificial Neural Networks: A Case Study

    OpenAIRE

    Qeethara Al-Shayea; Ghaleb El-Refae

    2013-01-01

    Protection of the environment from medical waste hazards is becoming a serious problem. There is a big relation between medical waste and disease injury. The main idea of this study is predict the relation between medical wastes and diseases in Hashemite Kingdom of Jordan using Artificial Neural Networks (ANNs) model. There are six predictor parameters associated with solid and liquid wastes in the medical services sector which are affecting the diseases injury. This study deals with two type...

  8. Inverse Problems In Structural Damage Identification, Structural Optimization, And Optical Medical Imaging Using Artificial Neural Networks

    OpenAIRE

    Kim, Yong Yook

    2004-01-01

    The objective of this work was to employ artificial neural networks (NN) to solve inverse problems in different engineering fields, overcoming various obstacles in applying NN to different problems and benefiting from the experience of solving different types of inverse problems. The inverse problems investigated are: 1) damage detection in structures, 2) detection of an anomaly in a light-diffusive medium, such as human tissue using optical imaging, 3) structural optimization of fiber optic ...

  9. An Artificial Neural Network Model for Wastewater Treatment Plant of Konya

    OpenAIRE

    Tumer, Abdullah Erdal; Edebali, Serpil

    2015-01-01

    In this study, modelling of Konya wastewater treatment plant was studied by using artificial neural network with different architectures in Matlab software. All data were obtained from wastewater treatment plant of Konya during daily records over four month. Treatment efficiency of the plant was determined by taking into account of input values of pH, temperature, COD, TSS and BOD with output values TSS. Performance of the model was compared via the parameters of Mean Squared Error (MSE), and...

  10. Application of artificial neural networks in modelling of normalised structural steels mechanical properties

    OpenAIRE

    L.A. Dobrzański; R. Honysz

    2009-01-01

    Purpose: This paper presents the application of artificial neural networks for mechanical properties prediction of constructional steels after heat treatment.Design/methodology/approach: On the basis of such input parameteres, such as the chemical composition, the ones of mechanical and heat treatment and dimensions of elements, mechanical properties such as strength, impact resistance or hardness are predicted.Findings: Results obtained in the given ranges of input parameters show very good ...

  11. Application of artificial neural networks in modelling of quenched and tempered structural steels mechanical properties

    OpenAIRE

    L.A. Dobrzański; R. Honysz

    2010-01-01

    Purpose: This paper presents the application of artificial neural networks for mechanical properties prediction of structuralal steels after quenching and tempering processes.Design/methodology/approach: On the basis of input parameters, which are chemical composition, parameters of mechanical and heat treatment and dimensions of elements, steels’ mechanical properties : yield stress, tensile strength stress, elongation, area reduction, impact strength and hardness are predicted.Findings: Res...

  12. Modelling of Biotechnological Processes - An approach based on Artificial Neural Networks

    OpenAIRE

    Valente, Eduardo; Rocha, Miguel; Ferreira, E.C.; Rocha, I

    2009-01-01

    In this chapter we describe a software tool for modelling fermentation processes, the FerMoANN, which allows researchers in biology and biotechnology areas to access the potential of Artificial Neural Networks (ANNs) for this task. The FerMoANN is tested and validated using two fermentation processes, an Escherichia coli recombinant protein production and the production of a secreted protein with Saccharomyces cerevisiae in fed-batch reactors. The application to these two case studies, tested...

  13. Modelling fed-batch fermentation processes : an approach based on artificial neural networks

    OpenAIRE

    Valente, Eduardo; Rocha, I; Rocha, Miguel

    2009-01-01

    Publicado em "2nd International Workshop on Practical Applications of Computational Biology and Bioinformatics (IWPACBB 2008)", ISBN 978-3-540-85860-7 Artificial Neural Networks (ANNs) have shown to be powerful tools for solving several problems which, due to their complexity, are extremely difficult to unravel with other methods. Their capabilities of massive parallel processing and learning from the environment make these structures ideal for prediction of nonlinear events. In this work,...

  14. Artificial Neural Network Technology: for the Classification and Cartography of Scientific and Technical Information

    OpenAIRE

    Polanco, Xavier; François, Claire; Keim, Jean-Pierre

    1998-01-01

    This paper describes the implementation of multivariate data analysis: NEURODOC applies the axial k-means method for automatic, non-hierarchical cluster analysis and a Principal Component Analysis (PCA) for representing the clusters on a map. We next introduce Artificial Neural Networks (ANNs) to extend NEURODOC into a neural platform for the cluster analysis and cartography of bibliographic data. The ANNs tested are: the Adaptive Resonance Theory (ART 1), a Multilayer Perceptron (MLP), and a...

  15. Trading "in-play" betting Exchange Markets with Artificial Neural Networks

    OpenAIRE

    Øvregård, Øyvind Norstein

    2008-01-01

    In this thesis I have studied, analyzed and implemented a solution for using artificial neural networks for prediction of in-play tennis match odds markets on Betfair. The overall prediction task was concentrated on maximizing potential profit, rather than just minimizing some standard error. The properties of odds trading were studied, and on the basis of this, a new cost function suitable to the underlying problem was proposed. The new cost function tried to capture some of the problem spec...

  16. A Power Market Forward Curve with Hydrology Dependence - An Approach based on Artificial Neural Networks

    OpenAIRE

    Green, Rikard

    2014-01-01

    This paper develops an hourly forward curve for power markets where the intra-day and intra-week shapes (profiles) depend on the level of the hydrological balance. The shaping model is based on a feed-forward Artificial Neural Network (ANN), which is trained on a historical data set of hourly electricity spot prices from the Nord Pool market and weekly measurements of the Nordic hydrological balance. The yearly seasonal cycle is estimated with historical electricity forward prices from the...

  17. Application of artificial neural network and information theory to detection of insulators

    Institute of Scientific and Technical Information of China (English)

    2000-01-01

    Information theory is used to obtain the information gain for each identification feature, and this gain is used as the weight factor for this feature to stress the role of effective feature, and the ART model based on artificial neural network theory is then used for identification thereby forming the detection system for poor insulators. Exper-iments and calculations show this approach is correct and feasible.

  18. Artificial Neural Networks Investigation of Indentation Force Effects on Nano- and Microhardness of Dual Phase Steels

    OpenAIRE

    Fotovati, A; J. Kadkhodapour; Schmauder, S.

    2014-01-01

    Nanoindentation test results on different grain sizes of dual phase (DP) steels are used to train artificial neural networks (ANNs). With selection of ferrite and martensite grain size, martensite volume fraction (MVF), and indentation force as input and microhardness, ferrite, and martensite nanohardness as outputs, six different ANNs are trained according to normalized datasets to predict hardness and their tolerances. A graphical user interface (GUI) is developed for a better investigation...

  19. Willingness to purchase Genetically Modified food: an analysis applying artificial Neural Networks

    OpenAIRE

    Salazar-Ordóñez, M.; Rodríguez-Entrena, M.; Becerra-Alonso, D.

    2014-01-01

    Findings about consumer decision-making process regarding GM food purchase remain mixed and are inconclusive. This paper offers a model which classifies willingness to purchase GM food, using data from 399 surveys in Southern Spain. Willingness to purchase has been measured using three dichotomous questions and classification, based on attitudinal, cognitive and socio-demographic factors, has been made by an artificial neural network model. The results show 74% accuracy to forecast the willin...

  20. Prediction of Global Solar Radiation in India using Artificial Neural Network

    OpenAIRE

    Rajiv Gupta; Saurabh Singhal

    2016-01-01

    Increasing global warming and decreasing fossil fuel reserves has necessitated the use of renewable energy resources like solar energy in India. To maximize return on a solar farm, it had to be set up at a place with high solar radiation. The solar radiation values are available only for a small number of places and must be interpolated for the rest. This paper utilizes Artificial Neural Network in interpolation, by obtaining a function with input as combinations of 7 geographical and meteoro...

  1. Application of artificial neural networks to predict the deflections of reinforced concrete beams

    Science.gov (United States)

    Kaczmarek, Mateusz; Szymańska, Agnieszka

    2016-06-01

    Nonlinear structural mechanics should be taken into account in the practical design of reinforced concrete structures. Cracking is one of the major sources of nonlinearity. Description of deflection of reinforced concrete elements is a computational problem, mainly because of the difficulties in modelling the nonlinear stress-strain relationship of concrete and steel. In design practise, in accordance with technical rules (e.g., Eurocode 2), a simplified approach for reinforced concrete is used, but the results of simplified calculations differ from the results of experimental studies. Artificial neural network is a versatile modelling tool capable of making predictions of values that are difficult to obtain in numerical analysis. This paper describes the creation and operation of a neural network for making predictions of deflections of reinforced concrete beams at different load levels. In order to obtain a database of results, that is necessary for training and testing the neural network, a research on measurement of deflections in reinforced concrete beams was conducted by the authors in the Certified Research Laboratory of the Building Engineering Institute at Wrocław University of Science and Technology. The use of artificial neural networks is an innovation and an alternative to traditional methods of solving the problem of calculating the deflections of reinforced concrete elements. The results show the effectiveness of using artificial neural network for predicting the deflection of reinforced concrete beams, compared with the results of calculations conducted in accordance with Eurocode 2. The neural network model presented in this paper can acquire new data and be used for further analysis, with availability of more research results.

  2. Increasing spatial resolution of CHIRPS rainfall datasets for Cyprus with artificial neural networks

    Science.gov (United States)

    Tymvios, Filippos; Michaelides, Silas; Retalis, Adrianos; Katsanos, Dimitrios; Lelieveld, Jos

    2016-08-01

    The use of high resolution rainfall datasets is an alternative way of studying climatological regions where conventional rain measurements are sparse or not available. Starting in 1981 to near-present, the CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data) dataset incorporates a 5km×5km resolution satellite imagery with in-situ station data to create gridded rainfall time series for trend analysis, severe events and seasonal drought monitoring. The aim of this work is to further increase the resolution of the rainfall dataset for Cyprus to 1km×1km, by correlating the CHIRPS dataset with elevation information, the NDVI index (Normalized Difference Vegetation Index) from satellite images at 1km×1km and precipitation measurements from the official raingauge network of the Cyprus' Department of Meteorology, utilizing Artificial Neural Networks. The Artificial Neural Networks' architecture that was implemented is the Multi-Layer Perceptron (MLP) trained with the back propagation method, which is widely used in environmental studies. Seven different network architectures were tested, all with two hidden layers. The number of neurons ranged from 3 to10 in the first hidden layer and from 5 to 25 in the second hidden layer. The dataset was separated into a randomly selected training set, a validation set and a testing set; the latter is independently used for the final assessment of the models' performance. Using the Artificial Neural Network approach, a new map of the spatial analysis of rainfall is constructed which exhibits a considerable increase in its spatial resolution. A statistical assessment of the new spatial analysis was made using the rainfall ground measurements from the raingauge network. The assessment indicates that the methodology is promising for several applications.

  3. Pattern-recognition by an artificial network derived from biologic neuronal systems.

    Science.gov (United States)

    Alkon, D L; Blackwell, K T; Barbour, G S; Rigler, A K; Vogl, T P

    1990-01-01

    A novel artificial neural network, derived from neurobiological observations, is described and examples of its performance are presented. This DYnamically STable Associative Learning (DYSTAL) network associatively learns both correlations and anticorrelations, and can be configured to classify or restore patterns with only a change in the number of output units. DYSTAL exhibits some particularly desirable properties: computational effort scales linearly with the number of connections, i.e., it is O(N) in complexity; performance of the network is stable with respect to network parameters over wide ranges of their values and over the size of the input field; storage of a very large number of patterns is possible; patterns need not be orthogonal; network connections are not restricted to multi-layer feed-forward or any other specific structure; and, for a known set of deterministic input patterns, the network weights can be computed, a priori, in closed form. The network has been associatively trained to perform the XOR function as well as other classification tasks. The network has also been trained to restore patterns obscured by binary or analog noise. Neither global nor local feedback connections are required during learning; hence the network is particularly suitable for hardware (VLSI) implementation.

  4. A neutron spectrum unfolding computer code based on artificial neural networks

    International Nuclear Information System (INIS)

    The Bonner Spheres Spectrometer consists of a thermal neutron sensor placed at the center of a number of moderating polyethylene spheres of different diameters. From the measured readings, information can be derived about the spectrum of the neutron field where measurements were made. Disadvantages of the Bonner system are the weight associated with each sphere and the need to sequentially irradiate the spheres, requiring long exposure periods. Provided a well-established response matrix and adequate irradiation conditions, the most delicate part of neutron spectrometry, is the unfolding process. The derivation of the spectral information is not simple because the unknown is not given directly as a result of the measurements. The drawbacks associated with traditional unfolding procedures have motivated the need of complementary approaches. Novel methods based on Artificial Intelligence, mainly Artificial Neural Networks, have been widely investigated. In this work, a neutron spectrum unfolding code based on neural nets technology is presented. This code is called Neutron Spectrometry and Dosimetry with Artificial Neural networks unfolding code that was designed in a graphical interface. The core of the code is an embedded neural network architecture previously optimized using the robust design of artificial neural networks methodology. The main features of the code are: easy to use, friendly and intuitive to the user. This code was designed for a Bonner Sphere System based on a 6LiI(Eu) neutron detector and a response matrix expressed in 60 energy bins taken from an International Atomic Energy Agency compilation. The main feature of the code is that as entrance data, for unfolding the neutron spectrum, only seven rate counts measured with seven Bonner spheres are required; simultaneously the code calculates 15 dosimetric quantities as well as the total flux for radiation protection purposes. This code generates a full report with all information of the unfolding in

  5. Kinetic parametric estimation in animal PET molecular imaging based on artificial immune network

    International Nuclear Information System (INIS)

    Objective: To develop an accurate,reliable method without the need of initialization in animal PET modeling for estimation of the tracer kinetic parameters based on the artificial immune network. Methods: The hepatic and left ventricular time activity curves (TACs) were obtained by drawing ROIs of liver tissue and left ventricle on dynamic 18F-FDG PET imaging of small mice. Meanwhile, the blood TAC was analyzed by sampling the tail vein blood at different time points after injection. The artificial immune network for parametric optimization of pharmacokinetics (PKAIN) was adapted to estimate the model parameters and the metabolic rate of glucose (Ki) was calculated. Results: TACs of liver,left ventricle and tail vein blood were obtained.Based on the artificial immune network, Ki in 3 mice was estimated as 0.0024, 0.0417 and 0.0047, respectively. The average weighted residual sum of squares of the output model generated by PKAIN was less than 0.0745 with a maximum standard deviation of 0.0084, which indicated that the proposed PKAIN method can provide accurate and reliable parametric estimation. Conclusion: The PKAIN method could provide accurate and reliable tracer kinetic modeling in animal PET imaging without the need of initialization of model parameters. (authors)

  6. Assembly Quality Prediction Based on Back-propagation Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    He Yong-yi

    2013-07-01

    Full Text Available Because of the severe geometrical distortion induced by the optical system and the limited kinetic accuracy of mechanical system in the vision-based mobile-phone lens’s assembly system, the nonlinear, perspective distortion errors and the kinematics errors generally exist in the assembly process of the mobile-phone lens. It is necessary to predict the assembly quality of the vision-based mobile-phone lens’s pick-and-place system so as to eliminate the immediate effect on the assembling process before extracting quantitative assembling. Comparison with current research methods, the back-propagation artificial neural network is applied to predict the assembly quality of the vision-based mobile-phone lens’s pick-and-place system. Firstly, the mobile-phone lens’s assembly quality characteristics are defined and sampled; Secondly, a back-propagation artificial neural network of the mobile-phone lens’s assembly quality prediction is presented; Finally apply some training samples obtained from the experiments to train and test this back-propagation artificial neural network. The results show that the proposed method is effective to predict the assembly quality of the vision-based mobile-phone lens’s pick-and-place system with high accuracy and high reliability.  

  7. Application of artificial neural network for prediction of marine diesel engine performance

    Science.gov (United States)

    Mohd Noor, C. W.; Mamat, R.; Najafi, G.; Nik, W. B. Wan; Fadhil, M.

    2015-12-01

    This study deals with an artificial neural network (ANN) modelling of a marine diesel engine to predict the brake power, output torque, brake specific fuel consumption, brake thermal efficiency and volumetric efficiency. The input data for network training was gathered from engine laboratory testing running at various engine speed. The prediction model was developed based on standard back-propagation Levenberg-Marquardt training algorithm. The performance of the model was validated by comparing the prediction data sets with the measured experiment data. Results showed that the ANN model provided good agreement with the experimental data with high accuracy.

  8. Proton exchange membrane fuel cells modeling based on artificial neural networks

    Institute of Scientific and Technical Information of China (English)

    Yudong Tian; Xinjian Zhu; Guangyi Cao

    2005-01-01

    To understand the complexity of the mathematical models of a proton exchange membrane fuel cell (PEMFC) and their shortage of practical PEMFC control, the PEMFC complex mechanism and the existing PEMFC models are analyzed, and artificial neural networks based PEMFC modeling is advanced. The structure, algorithm, training and simulation of PEMFC modeling based on improved BP networks are given out in detail. The computer simulation and conducted experiment verify that this model is fast and accurate, and can be used as a suitable operational model for PEMFC real-time control.

  9. A Cost Estimation Model for Repair Bridges Based on Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Mohamed Bouabaz

    2008-01-01

    Full Text Available Estimating the total cost of bridges repair and maintenance with high accuracy is an important components, and points to a need for a cost estimation model. This paper focuses on the development of a more accurate estimation model for repair and maintenance of bridges in developing countries using artificial neural networks. Cost and design data for two categories of repair bridges were used for training and testing our neural network model, with only three main parameters used in estimating the total cost of repairing bridges. An accuracy of 96% was achieved.

  10. [Diagnosis and prognosis of cerebral ischemic disturbances course using a method of artificial neuronal networks].

    Science.gov (United States)

    Ivanov, Iu S; Semin, G F

    2004-01-01

    Based on the data of examination of 224 patients with different stages of cerebral ischemic disturbances (CID) and 84 age-matched controls, an artificial neuronal network was constructed and tried in differential diagnosis of CID stages according to the data of transcranial ultrasonic dopplerography. Diagnostic efficacy of the network was 80% for sensitivity, 100% for specificity and 82.7% for reliability. A modeling of the influence of the main risk factors for cerebral ischemia and of the reserve state of cerebral hemodynamics for establishing the stage of CID was performed.

  11. Application of Artificial Neural Networks to Investigate the Energy Performance of Household Refrigerator-Freezers

    Science.gov (United States)

    Saidur, R.; Masjuki, H. H.

    In this study, the energy consumption of 149 domestic refrigerators has been monitored in Malaysian households. A questionnaire was used to get relevant information regarding the usage of this appliance in the actual kitchen environment to feed into neural networks. Prediction performance of Artificial Neural Networks (ANN) approach was investigated using actual monitored and survey data. Statistical analyses in terms of fraction of variance R2, Coefficient of Variation (COV), RMS are calculated to judge the performance of NN model. It has been found that the regression coefficient R2 is very close to unity for the best prediction performance results.

  12. Tea classification based on artificial olfaction using bionic olfactory neural network

    OpenAIRE

    X. L. Yang; Fu, J.; Lou, Z G; L. Y. Wang; Li, G.; Freeman, Walter J III

    2006-01-01

    Based on the research on mechanism of biological olfactory system, we constructed a K-set, which is a novel bionic neural network. Founded on the groundwork of K0, KI and KII sets, the KIII set in the K-set hierarchy simulates the whole olfactory neural system. In contrast to the conventional artificial neural networks, the KIII set operates in nonconvergent 'chaotic' dynamical modes similar to the biological olfactory system. In this paper, an application of electronic nose-brain for tea cla...

  13. Artificial neural network models for biomass gasification in fluidized bed gasifiers

    DEFF Research Database (Denmark)

    Puig Arnavat, Maria; Hernández, J. Alfredo; Bruno, Joan Carles;

    2013-01-01

    Artificial neural networks (ANNs) have been applied for modeling biomass gasification process in fluidized bed reactors. Two architectures of ANNs models are presented; one for circulating fluidized bed gasifiers (CFB) and the other for bubbling fluidized bed gasifiers (BFB). Both models determine...... bed gasifier can be successfully predicted by applying neural networks. ANNs models use in the input layer the biomass composition and few operating parameters, two neurons in the hidden layer and the backpropagation algorithm. The results obtained by these ANNs show high agreement with published...

  14. Prediction of Full-Scale Propulsion Power using Artificial Neural Networks

    DEFF Research Database (Denmark)

    Pedersen, Benjamin Pjedsted; Larsen, Jan

    2009-01-01

    Full scale measurements of the propulsion power, ship speed, wind speed and direction, sea and air temperature from four different loading conditions, together with hind cast data of wind and sea properties; and noon report data has been used to train an Artificial Neural Network for prediction...... of propulsion power. The model was optimized using a double cross validation procedure. The network was able to predict the propulsion power with accuracy between 0.8-1.7% using onboard measurement system data and 7% from manually acquired noon reports....

  15. Identification of phosphorylation sites in protein kinase A substrates using artificial neural networks and mass spectrometry

    DEFF Research Database (Denmark)

    Hjerrild, M.; Stensballe, A.; Rasmussen, T.E.;

    2004-01-01

    Protein phosphorylation plays a key role in cell regulation and identification of phosphorylation sites is important for understanding their functional significance. Here, we present an artificial neural network algorithm: NetPhosK (http://www.cbs.dtu.dk/services/NetPhosK/) that predicts protein...... kinase A (PKA) phosphorylation sites. The neural network was trained with a positive set of 258 experimentally verified PKA phosphorylation sites. The predictions by NetPhosK were! validated using four novel PKA substrates: Necdin, RFX5, En-2, and Wee 1. The four proteins were phosphorylated by PKA...

  16. Mechanical Property Prediction of Commercially Pure Titanium Welds with Artificial Neural Network

    Institute of Scientific and Technical Information of China (English)

    Yanhong WEI; H.K.D.H.Bhadeshia; T. Sourmail

    2005-01-01

    Factors that affect weld mechanical properties of commercially pure titanium have been investigated using artificial neural networks. Input data were obtained from mechanical testing of single-pass, autogenous welds, and neural network models were used to predict the ultimate tensile strength, yield strength, elongation, reduction of area,Vickers hardness and Rockwell B hardness. The results show that both oxygen and nitrogen have the most significant effects on the strength while hydrogen has the least effect over the range investigated. Predictions of the mechanical properties are shown and agree well with those obtained using the 'oxygen equivalent' (OE) equations.

  17. Identification of phosphorylation sites in protein kinase A substrates using artificial neural networks and mass spectrometry

    DEFF Research Database (Denmark)

    Hjerrild, Majbrit; Stensballe, Allan; Rasmussen, Thomas E;

    2011-01-01

    Protein phosphorylation plays a key role in cell regulation and identification of phosphorylation sites is important for understanding their functional significance. Here, we present an artificial neural network algorithm: NetPhosK (http://www.cbs.dtu.dk/services/NetPhosK/) that predicts protein...... kinase A (PKA) phosphorylation sites. The neural network was trained with a positive set of 258 experimentally verified PKA phosphorylation sites. The predictions by NetPhosK were validated using four novel PKA substrates: Necdin, RFX5, En-2, and Wee 1. The four proteins were phosphorylated by PKA...

  18. Application of Artificial Neural Network For Path Loss Prediction In Urban Macrocellular Environment

    Directory of Open Access Journals (Sweden)

    Joseph M. Mom

    2016-07-01

    Full Text Available An artificial neural network model for the prediction of path loss in urban macrocellular environment is presented. The model consists of a multilayer perceptron trained with measured data using Scaled Conjugate Gradient algorithm. Comparison between the proposed model on one hand, and the free space, Hata and Egli models on the other hand shows a better prediction result. With the proposed ANN model a good generalization is achieved, and it is accurate in environments different from the one used in training the network.

  19. Comparison of classical statistical methods and artificial neural network in traffic noise prediction

    International Nuclear Information System (INIS)

    Traffic is the main source of noise in urban environments and significantly affects human mental and physical health and labor productivity. Therefore it is very important to model the noise produced by various vehicles. Techniques for traffic noise prediction are mainly based on regression analysis, which generally is not good enough to describe the trends of noise. In this paper the application of artificial neural networks (ANNs) for the prediction of traffic noise is presented. As input variables of the neural network, the proposed structure of the traffic flow and the average speed of the traffic flow are chosen. The output variable of the network is the equivalent noise level in the given time period Leq. Based on these parameters, the network is modeled, trained and tested through a comparative analysis of the calculated values and measured levels of traffic noise using the originally developed user friendly software package. It is shown that the artificial neural networks can be a useful tool for the prediction of noise with sufficient accuracy. In addition, the measured values were also used to calculate equivalent noise level by means of classical methods, and comparative analysis is given. The results clearly show that ANN approach is superior in traffic noise level prediction to any other statistical method. - Highlights: • We proposed an ANN model for prediction of traffic noise. • We developed originally designed user friendly software package. • The results are compared with classical statistical methods. • The results are much better predictive capabilities of ANN model

  20. A COMPARATIVE ANALYSIS OF OPTIMIZATION TECHNIQUES FOR ARTIFICIAL NEURAL NETWORK IN BIO MEDICAL APPLICATIONS

    Directory of Open Access Journals (Sweden)

    V. Saishanmuga Raja

    2014-01-01

    Full Text Available In this study we compare the performance of three evolutionary algorithms such as Genetic Algorithm (GA Particle Swarm Optimization (PSO and Ant-Colony Optimization (ACO which are used to optimize the Artificial Neural Network (ANN. Optimization of Neural Networks improves speed of recall and may also improve the efficiency of training. Here we have used the Ant colony optimization, Particle Swarm Optimization and Genetic Algorithm to optimize the artificial neural networks for applications in medical image processing (extraction and compression. The aim of developing such algorithms is to arrive at near-optimum solutions to large-scale optimization problems, for which traditional mathematical techniques may fail. This study compares the efficiency and results of the three evolutionary algorithms. We have compared these algorithms based on processing time, accuracy and time taken to train Neural Networks. The results show that the Genetic Algorithm outperformed the other two algorithms. This study helps researchers to get an idea of selecting an optimization algorithm for configuring a neural network.