WorldWideScience

Sample records for neural cardiovascular control

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

    Science.gov (United States)

    Dampney, Roger A. L.

    2016-01-01

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

  2. Cardiovascular control by the suprachiasmatic nucleus: neural and neuroendocrine mechanisms in human and rat

    NARCIS (Netherlands)

    Scheer, Frank A.; Kalsbeek, Andries; Buijs, Ruud M.

    2003-01-01

    The risk for cardiovascular incidents is highest in the early morning, which seems partially due to endogenous factors. Endogenous circadian rhythms in mammalian physiology and behavior are regulated by the suprachiasmatic nucleus (SCN). Recently, anatomical evidence has been provided that SCN

  3. Neural circulatory control in vasovagal syncope

    NARCIS (Netherlands)

    van Lieshout, J. J.; Wieling, W.; Karemaker, J. M.

    1997-01-01

    The orthostatic volume displacement associated with the upright position necessitates effective neural cardiovascular modulation. Neural control of cardiac chronotropy and inotropy, and vasomotor tone aims at maintaining venous return, thus opposing gravitational pooling of blood in the lower part

  4. Role of the ovarian cycle on neural cardiovascular control in sleep-deprived women.

    Science.gov (United States)

    Yang, Huan; Durocher, John J; Larson, Robert A; Carter, Jason R

    2015-02-15

    The midluteal (ML) phase of the ovarian cycle is often sympathoexcitatory compared with the early follicular (EF) phase. We recently reported that 24-h total sleep deprivation (TSD) augmented cardiovascular reactivity in both men and women, but that sex differences existed in resting muscle sympathetic nerve activity (MSNA) responses to TSD. In the present study, we hypothesized increased resting MSNA and augmented cardiovascular reactivity to acute laboratory stressors during the ML phase in sleep-deprived women. Heart rate (HR), mean arterial pressure (MAP), forearm vascular conductance (FVC), and MSNA were measured in 14 eumenorrheic women (age, 20 ± 1 yr) during 10 min supine rest, 5 min mental stress (MS) trial, and 2 min cold pressor test (CPT) trial. Subjects were tested twice after TSD: once during EF phase and once during ML phase (randomized, crossover design). Estradiol (29 ± 2 vs. 63 ± 8 pg/ml, P = 0.001) and progesterone (1.6 ± 0.2 vs. 4.4 ± 0.7 ng/ml, P = 0.002) were elevated during the ML phase. Resting supine MAP (75 ± 2 vs. 72 ± 1 mmHg, P = 0.042) was lower during the ML phase. In contrast, resting supine HR, MSNA, and FVC were not significantly different between EF and ML phases. MAP, HR and FVC reactivity to MS were not statistically different between the EF and ML phases. Similarly, MAP and HR reactivity to CPT were not different between the ovarian phases. Contrary to our original hypothesis, the ML phase was not associated with sympathoexcitation or exaggerated cardiovascular reactivity in sleep-deprived premenopausal women. However, our data reveal elevated resting blood pressure during the EF phase in sleep-deprived women. Copyright © 2015 the American Physiological Society.

  5. Neural Control of the Circulation

    Science.gov (United States)

    Thomas, Gail D.

    2011-01-01

    The purpose of this brief review is to highlight key concepts about the neural control of the circulation that graduate and medical students should be expected to incorporate into their general knowledge of human physiology. The focus is largely on the sympathetic nerves, which have a dominant role in cardiovascular control due to their effects to…

  6. Fuzzy and neural control

    Science.gov (United States)

    Berenji, Hamid R.

    1992-01-01

    Fuzzy logic and neural networks provide new methods for designing control systems. Fuzzy logic controllers do not require a complete analytical model of a dynamic system and can provide knowledge-based heuristic controllers for ill-defined and complex systems. Neural networks can be used for learning control. In this chapter, we discuss hybrid methods using fuzzy logic and neural networks which can start with an approximate control knowledge base and refine it through reinforcement learning.

  7. Cardiovascular control during exercise

    DEFF Research Database (Denmark)

    Dela, Flemming; Mohr, Thomas; Jensen, Christina M R

    2003-01-01

    We studied the role of the central nervous system, neural feedback from contracting skeletal muscles, and sympathetic activity to the heart in the control of heart rate and blood pressure during 2 levels of dynamic exercise....

  8. Autonomic neural control of the cardiovascular system in patients with Behçet's disease in the absence of neurological involvement.

    Science.gov (United States)

    Erol, Tansel; Tekin, Abdullah; Tufan, Müge; Altay, Hakan; Tekin, Göknur; Bilgi, Muhammet; Özin, Bülent; Yücel, Eftal; Müderrisoğlu, Haldun

    2012-10-01

    Behçet's disease (BD) is a chronic multi-system disease presenting with recurrent oral and genital ulceration, and relapsing uveitis. Heart rate recovery (HRR) after exercise is a marker of parasympathetic activity. A delayed recovery of systolic blood pressure (SBP) after exercise might reflect sympathetic hyperactivity. The analysis of variations in heart rate has also been used to determine the balance between sympathetic and vagal nerve activities in the heart. Our objective was to determine HRR, the SBP response to exercise and heart rate variability (HRV) in patients with BD in the absence of neurological involvement. The study population consisted of 32 patients with BD and 30 healthy controls who were matched with respect to age, sex, and physical activity. Heart rate recovery was calculated as the difference between heart rate at peak exercise and heart rate at 1, 2, and 3 min of recovery. Blood pressure recovery indexes were determined by dividing the systolic blood pressure at 2 and 3 min in recovery to the systolic blood pressure at peak exercise. In patients with BD, mean HRR at 1 min (HRR1) were not significantly different than that of controls (21 ± 7 vs 20 ± 7 bpm, p = 0.50). Although, resting mean SBP of patients with BD was higher than controls (121 ± 13 vs 115 ± 12 mmHg, p = 0.039), the SBP recovery indices of the patients with BD at 2 and 3 min were similar to those of controls (0.84 ± 0.07 vs 0.84 ± 0.09, p = 0.89 and 0.78 ± 0.09 vs 0.78 ± 0.08, p = 0.93, respectively). Both time domain and frequency domain parameters of patients with BD were similar to that of controls. This study shows that the patients with BD have normal HRR1 and normal SBP response to exercise and normal HRV. These findings might suggest unaltered autonomic neural control of the cardiovascular system in this disorder in the absence of neurological involvement.

  9. Cardiovascular parameters and neural sympathetic discharge variability before orthostatic syncope: role of sympathetic baroreflex control to the vessels.

    Science.gov (United States)

    Barbic, Franca; Heusser, Karsten; Marchi, Andrea; Zamunér, Antonio Roberto; Gauger, Peter; Tank, Jens; Jordan, Jens; Diedrich, André; Robertson, David; Dipaola, Franca; Achenza, Sara; Porta, Alberto; Furlan, Raffaello

    2015-04-01

    We tested the hypothesis that altered sympathetic baroreceptor control to the vessels (svBRS) and disrupted coupling between blood pressure (BP) fluctuations and muscle sympathetic activity (MSNA) discharge pattern in the low frequency band (LF, around 0.1 Hz) precede vasovagal syncope. Seven healthy males underwent ECG, BP, respiratory, and MSNA recordings at baseline (REST) and during a 15 min 80° head-up tilt, followed by a -10 mmHg step wise increase of lower body negative pressure up to presyncope. Spectral and coherence analyses of systolic arterial pressure (SAP) and MSNA variability provided the indexes of vascular sympathetic modulation, LFSAP, and of the linear coupling between MSNA and SAP in the low frequency band (around 0.1 Hz), K(2)MSNA-SAP(LF). svBRS was assessed as the slope of the regression line between MSNA and diastolic arterial pressure (DAP). Data were analyzed at REST, during asymptomatic and presyncope periods of tilt. svBRS declined during presyncope period compared to REST and asymptomatic tilt. The presyncope period was characterized by a decrease of RR interval, LFMSNA, LFSAP, and K(2)MSNA-SAP(LF) values compared to the asymptomatic one, whereas MSNA burst rate was unchanged. The reduction of svBRS producing an altered coupling between MSNA and SAP variability at 0.1 Hz, may provoke circulatory changes leading to presyncope.

  10. Neural Networks for Optimal Control

    DEFF Research Database (Denmark)

    Sørensen, O.

    1995-01-01

    Two neural networks are trained to act as an observer and a controller, respectively, to control a non-linear, multi-variable process.......Two neural networks are trained to act as an observer and a controller, respectively, to control a non-linear, multi-variable process....

  11. Cardiovascular Development and the Colonizing Cardiac Neural Crest Lineage

    Directory of Open Access Journals (Sweden)

    Paige Snider

    2007-01-01

    Full Text Available Although it is well established that transgenic manipulation of mammalian neural crest-related gene expression and microsurgical removal of premigratory chicken and Xenopus embryonic cardiac neural crest progenitors results in a wide spectrum of both structural and functional congenital heart defects, the actual functional mechanism of the cardiac neural crest cells within the heart is poorly understood. Neural crest cell migration and appropriate colonization of the pharyngeal arches and outflow tract septum is thought to be highly dependent on genes that regulate cell-autonomous polarized movement (i.e., gap junctions, cadherins, and noncanonical Wnt1 pathway regulators. Once the migratory cardiac neural crest subpopulation finally reaches the heart, they have traditionally been thought to participate in septation of the common outflow tract into separate aortic and pulmonary arteries. However, several studies have suggested these colonizing neural crest cells may also play additional unexpected roles during cardiovascular development and may even contribute to a crest-derived stem cell population. Studies in both mice and chick suggest they can also enter the heart from the venous inflow as well as the usual arterial outflow region, and may contribute to the adult semilunar and atrioventricular valves as well as part of the cardiac conduction system. Furthermore, although they are not usually thought to give rise to the cardiomyocyte lineage, neural crest cells in the zebrafish (Danio rerio can contribute to the myocardium and may have different functions in a species-dependent context. Intriguingly, both ablation of chick and Xenopus premigratory neural crest cells, and a transgenic deletion of mouse neural crest cell migration or disruption of the normal mammalian neural crest gene expression profiles, disrupts ventral myocardial function and/or cardiomyocyte proliferation. Combined, this suggests that either the cardiac neural crest

  12. Neural Networks in Control Applications

    DEFF Research Database (Denmark)

    Sørensen, O.

    The intention of this report is to make a systematic examination of the possibilities of applying neural networks in those technical areas, which are familiar to a control engineer. In other words, the potential of neural networks in control applications is given higher priority than a detailed...... examined, and it appears that considering 'normal' neural network models with, say, 500 samples, the problem of over-fitting is neglible, and therefore it is not taken into consideration afterwards. Numerous model types, often met in control applications, are implemented as neural network models...... Kalmann filter) representing state space description. The potentials of neural networks for control of non-linear processes are also examined, focusing on three different groups of control concepts, all considered as generalizations of known linear control concepts to handle also non-linear processes...

  13. Neural systems for control

    National Research Council Canada - National Science Library

    Omidvar, Omid; Elliott, David L

    1997-01-01

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

  14. Neural Networks in Control Applications

    DEFF Research Database (Denmark)

    Sørensen, O.

    simulated process and compared. The closing chapter describes some practical experiments, where the different control concepts and training methods are tested on the same practical process operating in very noisy environments. All tests confirm that neural networks also have the potential to be trained......The intention of this report is to make a systematic examination of the possibilities of applying neural networks in those technical areas, which are familiar to a control engineer. In other words, the potential of neural networks in control applications is given higher priority than a detailed...... study of the networks themselves. With this end in view the following restrictions have been made: - Amongst numerous neural network structures, only the Multi Layer Perceptron (a feed-forward network) is applied. - Amongst numerous training algorithms, only four algorithms are examined, all...

  15. What is the Ultimate Goal in Neural Regulation of Cardiovascular Function?

    Science.gov (United States)

    Prakash, E. S.; Madanmohan; Pal, Gopal Krushna

    2004-01-01

    We used the following multiple-choice question after a series of lectures in cardiovascular physiology in the first year of an undergraduate medical curriculum (n = 66) to assess whether students had understood the neural regulation of cardiovascular function. In health, neural cardiovascular mechanisms are geared toward maintaining A) cardiac…

  16. Neural Networks in Control Applications

    DEFF Research Database (Denmark)

    Sørensen, O.

    The intention of this report is to make a systematic examination of the possibilities of applying neural networks in those technical areas, which are familiar to a control engineer. In other words, the potential of neural networks in control applications is given higher priority than a detailed...... study of the networks themselves. With this end in view the following restrictions have been made: - Amongst numerous neural network structures, only the Multi Layer Perceptron (a feed-forward network) is applied. - Amongst numerous training algorithms, only four algorithms are examined, all...... in a recursive form (sample updating). The simplest is the Back Probagation Error Algorithm, and the most complex is the recursive Prediction Error Method using a Gauss-Newton search direction. - Over-fitting is often considered to be a serious problem when training neural networks. This problem is specifically...

  17. Neural Manifolds for the Control of Movement.

    Science.gov (United States)

    Gallego, Juan A; Perich, Matthew G; Miller, Lee E; Solla, Sara A

    2017-06-07

    The analysis of neural dynamics in several brain cortices has consistently uncovered low-dimensional manifolds that capture a significant fraction of neural variability. These neural manifolds are spanned by specific patterns of correlated neural activity, the "neural modes." We discuss a model for neural control of movement in which the time-dependent activation of these neural modes is the generator of motor behavior. This manifold-based view of motor cortex may lead to a better understanding of how the brain controls movement. Copyright © 2017 Elsevier Inc. All rights reserved.

  18. Simplified LQG Control with Neural Networks

    DEFF Research Database (Denmark)

    Sørensen, O.

    1997-01-01

    A new neural network application for non-linear state control is described. One neural network is modelled to form a Kalmann predictor and trained to act as an optimal state observer for a non-linear process. Another neural network is modelled to form a state controller and trained to produce...

  19. Adaptive optimization and control using neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Mead, W.C.; Brown, S.K.; Jones, R.D.; Bowling, P.S.; Barnes, C.W.

    1993-10-22

    Recent work has demonstrated the ability of neural-network-based controllers to optimize and control machines with complex, non-linear, relatively unknown control spaces. We present a brief overview of neural networks via a taxonomy illustrating some capabilities of different kinds of neural networks. We present some successful control examples, particularly the optimization and control of a small-angle negative ion source.

  20. Neural regulation of cardiovascular response to exercise: role of central command and peripheral afferents.

    Science.gov (United States)

    Nobrega, Antonio C L; O'Leary, Donal; Silva, Bruno Moreira; Marongiu, Elisabetta; Piepoli, Massimo F; Crisafulli, Antonio

    2014-01-01

    During dynamic exercise, mechanisms controlling the cardiovascular apparatus operate to provide adequate oxygen to fulfill metabolic demand of exercising muscles and to guarantee metabolic end-products washout. Moreover, arterial blood pressure is regulated to maintain adequate perfusion of the vital organs without excessive pressure variations. The autonomic nervous system adjustments are characterized by a parasympathetic withdrawal and a sympathetic activation. In this review, we briefly summarize neural reflexes operating during dynamic exercise. The main focus of the present review will be on the central command, the arterial baroreflex and chemoreflex, and the exercise pressure reflex. The regulation and integration of these reflexes operating during dynamic exercise and their possible role in the pathophysiology of some cardiovascular diseases are also discussed.

  1. Neural Regulation of Cardiovascular Response to Exercise: Role of Central Command and Peripheral Afferents

    Science.gov (United States)

    Nobrega, Antonio C. L.; O'Leary, Donal; Silva, Bruno Moreira; Piepoli, Massimo F.; Crisafulli, Antonio

    2014-01-01

    During dynamic exercise, mechanisms controlling the cardiovascular apparatus operate to provide adequate oxygen to fulfill metabolic demand of exercising muscles and to guarantee metabolic end-products washout. Moreover, arterial blood pressure is regulated to maintain adequate perfusion of the vital organs without excessive pressure variations. The autonomic nervous system adjustments are characterized by a parasympathetic withdrawal and a sympathetic activation. In this review, we briefly summarize neural reflexes operating during dynamic exercise. The main focus of the present review will be on the central command, the arterial baroreflex and chemoreflex, and the exercise pressure reflex. The regulation and integration of these reflexes operating during dynamic exercise and their possible role in the pathophysiology of some cardiovascular diseases are also discussed. PMID:24818143

  2. Cognitive Control Signals for Neural Prosthetics

    National Research Council Canada - National Science Library

    S. Musallam; B. D. Corneil; B. Greger; H. Scherberger; R. A. Andersen

    2004-01-01

    Recent development of neural prosthetics for assisting paralyzed patients has focused on decoding intended hand trajectories from motor cortical neurons and using this signal to control external devices...

  3. Decentralized neural control application to robotics

    CERN Document Server

    Garcia-Hernandez, Ramon; Sanchez, Edgar N; Alanis, Alma y; Ruz-Hernandez, Jose A

    2017-01-01

    This book provides a decentralized approach for the identification and control of robotics systems. It also presents recent research in decentralized neural control and includes applications to robotics. Decentralized control is free from difficulties due to complexity in design, debugging, data gathering and storage requirements, making it preferable for interconnected systems. Furthermore, as opposed to the centralized approach, it can be implemented with parallel processors. This approach deals with four decentralized control schemes, which are able to identify the robot dynamics. The training of each neural network is performed on-line using an extended Kalman filter (EKF). The first indirect decentralized control scheme applies the discrete-time block control approach, to formulate a nonlinear sliding manifold. The second direct decentralized neural control scheme is based on the backstepping technique, approximated by a high order neural network. The third control scheme applies a decentralized neural i...

  4. Abnormal cardiovascular response to exercise in hypertension: contribution of neural factors.

    Science.gov (United States)

    Mitchell, Jere H

    2017-06-01

    During both dynamic (e.g., endurance) and static (e.g., strength) exercise there are exaggerated cardiovascular responses in hypertension. This includes greater increases in blood pressure, heart rate, and efferent sympathetic nerve activity than in normal controls. Two of the known neural factors that contribute to this abnormal cardiovascular response are the exercise pressor reflex (EPR) and functional sympatholysis. The EPR originates in contracting skeletal muscle and reflexly increases sympathetic efferent nerve activity to the heart and blood vessels as well as decreases parasympathetic efferent nerve activity to the heart. These changes in autonomic nerve activity cause an increase in blood pressure, heart rate, left ventricular contractility, and vasoconstriction in the arterial tree. However, arterial vessels in the contracting skeletal muscle have a markedly diminished vasoconstrictor response. The markedly diminished vasoconstriction in contracting skeletal muscle has been termed functional sympatholysis. It has been shown in hypertension that there is an enhanced EPR, including both its mechanoreflex and metaboreflex components, and an impaired functional sympatholysis. These conditions set up a positive feedback or vicious cycle situation that causes a progressively greater decrease in the blood flow to the exercising muscle. Thus these two neural mechanisms contribute significantly to the abnormal cardiovascular response to exercise in hypertension. In addition, exercise training in hypertension decreases the enhanced EPR, including both mechanoreflex and metaboreflex function, and improves the impaired functional sympatholysis. These two changes, caused by exercise training, improve the muscle blood flow to exercising muscle and cause a more normal cardiovascular response to exercise in hypertension. Copyright © 2017 the American Physiological Society.

  5. Flexible body control using neural networks

    Science.gov (United States)

    Mccullough, Claire L.

    1992-01-01

    Progress is reported on the control of Control Structures Interaction suitcase demonstrator (a flexible structure) using neural networks and fuzzy logic. It is concluded that while control by neural nets alone (i.e., allowing the net to design a controller with no human intervention) has yielded less than optimal results, the neural net trained to emulate the existing fuzzy logic controller does produce acceptible system responses for the initial conditions examined. Also, a neural net was found to be very successful in performing the emulation step necessary for the anticipatory fuzzy controller for the CSI suitcase demonstrator. The fuzzy neural hybrid, which exhibits good robustness and noise rejection properties, shows promise as a controller for practical flexible systems, and should be further evaluated.

  6. Additive Feed Forward Control with Neural Networks

    DEFF Research Database (Denmark)

    Sørensen, O.

    1999-01-01

    This paper demonstrates a method to control a non-linear, multivariable, noisy process using trained neural networks. The basis for the method is a trained neural network controller acting as the inverse process model. A training method for obtaining such an inverse process model is applied....... A suitable 'shaped' (low-pass filtered) reference is used to overcome problems with excessive control action when using a controller acting as the inverse process model. The control concept is Additive Feed Forward Control, where the trained neural network controller, acting as the inverse process model......, is placed in a supplementary pure feed-forward path to an existing feedback controller. This concept benefits from the fact, that an existing, traditional designed, feedback controller can be retained without any modifications, and after training the connection of the neural network feed-forward controller...

  7. Neural Networks for Non-linear Control

    DEFF Research Database (Denmark)

    Sørensen, O.

    1994-01-01

    This paper describes how a neural network, structured as a Multi Layer Perceptron, is trained to predict, simulate and control a non-linear process.......This paper describes how a neural network, structured as a Multi Layer Perceptron, is trained to predict, simulate and control a non-linear process....

  8. Control of autonomous robot using neural networks

    Science.gov (United States)

    Barton, Adam; Volna, Eva

    2017-07-01

    The aim of the article is to design a method of control of an autonomous robot using artificial neural networks. The introductory part describes control issues from the perspective of autonomous robot navigation and the current mobile robots controlled by neural networks. The core of the article is the design of the controlling neural network, and generation and filtration of the training set using ART1 (Adaptive Resonance Theory). The outcome of the practical part is an assembled Lego Mindstorms EV3 robot solving the problem of avoiding obstacles in space. To verify models of an autonomous robot behavior, a set of experiments was created as well as evaluation criteria. The speed of each motor was adjusted by the controlling neural network with respect to the situation in which the robot was found.

  9. Neural network topology design for nonlinear control

    Science.gov (United States)

    Haecker, Jens; Rudolph, Stephan

    2001-03-01

    Neural networks, especially in nonlinear system identification and control applications, are typically considered to be black-boxes which are difficult to analyze and understand mathematically. Due to this reason, an in- depth mathematical analysis offering insight into the different neural network transformation layers based on a theoretical transformation scheme is desired, but up to now neither available nor known. In previous works it has been shown how proven engineering methods such as dimensional analysis and the Laplace transform may be used to construct a neural controller topology for time-invariant systems. Using the knowledge of neural correspondences of these two classical methods, the internal nodes of the network could also be successfully interpreted after training. As further extension to these works, the paper describes the latest of a theoretical interpretation framework describing the neural network transformation sequences in nonlinear system identification and control. This can be achieved By incorporation of the method of exact input-output linearization in the above mentioned two transform sequences of dimensional analysis and the Laplace transformation. Based on these three theoretical considerations neural network topologies may be designed in special situations by pure translation in the sense of a structural compilation of the known classical solutions into their correspondent neural topology. Based on known exemplary results, the paper synthesizes the proposed approach into the visionary goals of a structural compiler for neural networks. This structural compiler for neural networks is intended to automatically convert classical control formulations into their equivalent neural network structure based on the principles of equivalence between formula and operator, and operator and structure which are discussed in detail in this work.

  10. Cardiovascular autonomic control after short-duration spaceflights

    Science.gov (United States)

    Beckers, Frank; Verheyden, Bart; Liu, Jiexin; Aubert, André E.

    2009-09-01

    After spaceflight, astronauts sometimes suffer a variable degree of reduced orthostatic tolerance. Although many studies have addressed this problem, many aspects remain unclear. Also, it is unknown how long the cardiovascular system needs to recover from short duration spaceflights. The scope of the present study was to determine a long-term follow-up of cardiovascular control up to 25 days after spaceflight under control conditions in five astronauts using heart rate variability, blood pressure variability and baroreflex sensitivity (BRS) indices. In standing position heart rate after spaceflight was significantly higher compared with pre-flight (R+1: 99 (SD 9) BPM vs L-30: 77 (SD 3) BPM; pblood pressure control was well maintained from the first day after landing. The decrease in BRS and in vagal heart rate modulation following short-duration spaceflight appear to constitute an adequate autonomic neural response to restored gravity. After 4 days upon return to Earth, vagal heart rate modulation is almost completely recovered to the pre-flight level. The findings of the present study demonstrate that the decrease in vagal heart rate modulation in standing position should not be characterised as some kind of cardiovascular deconditioning, but rather as the normal response to orthostatic stress after spaceflight.

  11. Baroreflex and metaboreflex control of cardiovascular system during exercise in space.

    Science.gov (United States)

    Pagani, Massimo; Pizzinelli, Paolo; Beltrami, Silvia; Massaro, Michele; Lucini, Daniela; Iellamo, Ferdinando

    2009-10-01

    This brief review summarizes current knowledge on the neural mechanisms of cardiovascular regulation during exercise in space, with specific emphasis on the role of the arterial baroreflex and the muscle metaboreflex, with the attendant modifications in autonomic nervous system activity, in determining the cardiovascular responses to exercise in microgravity conditions. Available data suggest that the muscle metaboreflex is enhanced during dynamic exercise in space and that the potentiation of the muscle metaboreflex affects the vagally mediated arterial baroreflex contribution to HR control.

  12. Dilated convolutional neural networks for cardiovascular MR segmentation in congenital heart disease

    NARCIS (Netherlands)

    Wolterink, Jelmer M.|info:eu-repo/dai/nl/413994112; Leiner, Tim|info:eu-repo/dai/nl/238322467; Viergever, Max A.|info:eu-repo/dai/nl/108781828; Išgum, Ivana|info:eu-repo/dai/nl/31484984X

    2017-01-01

    We propose an automatic method using dilated convolutional neural networks (CNNs) for segmentation of the myocardium and blood pool in cardiovascular MR (CMR) of patients with congenital heart disease (CHD). Ten training and ten test CMR scans cropped to an ROI around the heart were provided in the

  13. Neural predictive control for active buffet alleviation

    Science.gov (United States)

    Pado, Lawrence E.; Lichtenwalner, Peter F.; Liguore, Salvatore L.; Drouin, Donald

    1998-06-01

    The adaptive neural control of aeroelastic response (ANCAR) and the affordable loads and dynamics independent research and development (IRAD) programs at the Boeing Company jointly examined using neural network based active control technology for alleviating undesirable vibration and aeroelastic response in a scale model aircraft vertical tail. The potential benefits of adaptive control includes reducing aeroelastic response associated with buffet and atmospheric turbulence, increasing flutter margins, and reducing response associated with nonlinear phenomenon like limit cycle oscillations. By reducing vibration levels and thus loads, aircraft structures can have lower acquisition cost, reduced maintenance, and extended lifetimes. Wind tunnel tests were undertaken on a rigid 15% scale aircraft in Boeing's mini-speed wind tunnel, which is used for testing at very low air speeds up to 80 mph. The model included a dynamically scaled flexible fail consisting of an aluminum spar with balsa wood cross sections with a hydraulically powered rudder. Neural predictive control was used to actuate the vertical tail rudder in response to strain gauge feedback to alleviate buffeting effects. First mode RMS strain reduction of 50% was achieved. The neural predictive control system was developed and implemented by the Boeing Company to provide an intelligent, adaptive control architecture for smart structures applications with automated synthesis, self-optimization, real-time adaptation, nonlinear control, and fault tolerance capabilities. It is designed to solve complex control problems though a process of automated synthesis, eliminating costly control design and surpassing it in many instances by accounting for real world non-linearities.

  14. Acute effects on cardiovascular oscillations during controlled slow yogic breathing

    Directory of Open Access Journals (Sweden)

    Om Lata Bhagat

    2017-01-01

    Interpretation & conclusions: Significant increase in cardiovascular oscillations and baroreflex recruitments during-ANB suggested a dynamic interaction between respiratory and cardiovascular system. Enhanced phasic relationship with some delay indicated the complexity of the system. It indicated that respiratory and cardiovascular oscillations were coupled through multiple regulatory mechanisms, such as mechanical coupling, baroreflex and central cardiovascular control.

  15. Neural Control of the Lower Urinary Tract

    Science.gov (United States)

    de Groat, William C.; Griffiths, Derek; Yoshimura, Naoki

    2015-01-01

    This article summarizes anatomical, neurophysiological, pharmacological, and brain imaging studies in humans and animals that have provided insights into the neural circuitry and neurotransmitter mechanisms controlling the lower urinary tract. The functions of the lower urinary tract to store and periodically eliminate urine are regulated by a complex neural control system in the brain, spinal cord, and peripheral autonomic ganglia that coordinates the activity of smooth and striated muscles of the bladder and urethral outlet. The neural control of micturition is organized as a hierarchical system in which spinal storage mechanisms are in turn regulated by circuitry in the rostral brain stem that initiates reflex voiding. Input from the forebrain triggers voluntary voiding by modulating the brain stem circuitry. Many neural circuits controlling the lower urinary tract exhibit switch-like patterns of activity that turn on and off in an all-or-none manner. The major component of the micturition switching circuit is a spinobulbospinal parasympathetic reflex pathway that has essential connections in the periaqueductal gray and pontine micturition center. A computer model of this circuit that mimics the switching functions of the bladder and urethra at the onset of micturition is described. Micturition occurs involuntarily in infants and young children until the age of 3 to 5 years, after which it is regulated voluntarily. Diseases or injuries of the nervous system in adults can cause the re-emergence of involuntary micturition, leading to urinary incontinence. Neuroplasticity underlying these developmental and pathological changes in voiding function is discussed. PMID:25589273

  16. High-Intensity Progressive Resistance Training Increases Strength With No Change in Cardiovascular Function and Autonomic Neural Regulation in Older Adults.

    Science.gov (United States)

    Kanegusuku, Hélcio; Queiroz, Andréia C; Silva, Valdo J; de Mello, Marco T; Ugrinowitsch, Carlos; Forjaz, Cláudia L

    2015-07-01

    The effects of high-intensity progressive resistance training (HIPRT) on cardiovascular function and autonomic neural regulation in older adults are unclear. To investigate this issue, 25 older adults were randomly divided into two groups: control (CON, N = 13, 63 ± 4 years; no training) and HIPRT (N = 12, 64 ± 4 years; 2 sessions/week, 7 exercises, 2–4 sets, 10–4 RM). Before and after four months, maximal strength, quadriceps cross-sectional area (QCSA), clinic and ambulatory blood pressures (BP), systemic hemodynamics, and cardiovascular autonomic modulation were measured. Maximal strength and QCSA increased in the HIPRT group and did not change in the CON group. Clinic and ambulatory BP, cardiac output, systemic vascular resistance, stroke volume, heart rate, and cardiac sympathovagal balance did not change in the HIPRT group or the CON group. In conclusion, HIPRT was effective at increasing muscle mass and strength without promoting changes in cardiovascular function or autonomic neural regulation.

  17. The neural control of singing

    Directory of Open Access Journals (Sweden)

    Jean Mary eZarate

    2013-06-01

    Full Text Available Singing provides a unique opportunity to examine music performance—the musical instrument is contained wholly within the body, thus eliminating the need for creating artificial instruments or tasks in neuroimaging experiments. Here, more than two decades of voice and singing research will be reviewed to give an overview of the sensory-motor control of the singing voice, starting from the vocal tract and leading up to the brain regions involved in singing. Additionally, to demonstrate how sensory feedback is integrated with vocal motor control, recent functional magnetic resonance imaging (fMRI research on somatosensory and auditory feedback processing during singing will be presented. The relationship between the brain and singing behavior will be explored also by examining: 1 neuroplasticity as a function of various lengths and types of training, 2 vocal amusia due to a compromised singing network, and 3 singing performance in individuals with congenital amusia. Finally, the auditory-motor control network for singing will be considered alongside dual-stream models of auditory processing in music and speech to refine both these theoretical models and the singing network itself.

  18. The neural control of singing

    Science.gov (United States)

    Zarate, Jean Mary

    2013-01-01

    Singing provides a unique opportunity to examine music performance—the musical instrument is contained wholly within the body, thus eliminating the need for creating artificial instruments or tasks in neuroimaging experiments. Here, more than two decades of voice and singing research will be reviewed to give an overview of the sensory-motor control of the singing voice, starting from the vocal tract and leading up to the brain regions involved in singing. Additionally, to demonstrate how sensory feedback is integrated with vocal motor control, recent functional magnetic resonance imaging (fMRI) research on somatosensory and auditory feedback processing during singing will be presented. The relationship between the brain and singing behavior will be explored also by examining: (1) neuroplasticity as a function of various lengths and types of training, (2) vocal amusia due to a compromised singing network, and (3) singing performance in individuals with congenital amusia. Finally, the auditory-motor control network for singing will be considered alongside dual-stream models of auditory processing in music and speech to refine both these theoretical models and the singing network itself. PMID:23761746

  19. neural network based load frequency control for restructuring power

    African Journals Online (AJOL)

    2012-03-01

    Mar 1, 2012 ... Abstract. In this study, an artificial neural network (ANN) application of load frequency control. (LFC) of a Multi-Area power system by using a neural network controller is presented. The comparison between a conventional Proportional Integral (PI) controller and the proposed artificial neural networks ...

  20. Neural network controller for underwater work ROV. Suichu sagyoyo ROV no neural network controller

    Energy Technology Data Exchange (ETDEWEB)

    Yoshida, Y.; Kidoshi, H.; Arahata, M.; Shoji, K.; Takahashi, Y. (Ishikawajima-Harima Heavy Industries, Co. Ltd., Tokyo (Japan))

    1993-07-01

    The previous underwater work ROV (remotely operated vehicle) has been controlled manually because its dynamic properties are changeable underwater. Ishikawajima-Harima Heavy Industries (IHI) has applied a neural network to an adaptive controller for the ROV. This paper describes objectives of the research, design of control logic, and tank experiments on a model ROV. For the neural network, manual operation was used to provide the initial learning data for the neural network in order to initialize control parameters for optimization. The model ROV was designed to achieve and maintain constant depth in normal operation. As a consequence of the tank experiments, it was demonstrated that the controller can acquire skill of operators, can further improve the acquired skill of operators, and can construct an automatic control system autonomically even if any dynamic properties are not known. 6 refs., 8 figs.

  1. Neural Network Control of Asymmetrical Multilevel Converters

    Directory of Open Access Journals (Sweden)

    Patrice WIRA

    2009-12-01

    Full Text Available This paper proposes a neural implementation of a harmonic eliminationstrategy (HES to control a Uniform Step Asymmetrical Multilevel Inverter(USAMI. The mapping between the modulation rate and the requiredswitching angles is learned and approximated with a Multi-Layer Perceptron(MLP neural network. After learning, appropriate switching angles can bedetermined with the neural network leading to a low-computational-costneural controller which is well suited for real-time applications. Thistechnique can be applied to multilevel inverters with any number of levels. Asan example, a nine-level inverter and an eleven-level inverter are consideredand the optimum switching angles are calculated on-line. Comparisons to thewell-known sinusoidal pulse-width modulation (SPWM have been carriedout in order to evaluate the performance of the proposed approach. Simulationresults demonstrate the technical advantages of the proposed neuralimplementation over the conventional method (SPWM in eliminatingharmonics while controlling a nine-level and eleven-level USAMI. Thisneural approach is applied for the supply of an asynchronous machine andresults show that it ensures a highest quality torque by efficiently cancelingthe harmonics generated by the inverters.

  2. Robust Adaptive Control via Neural Linearization and Compensation

    Directory of Open Access Journals (Sweden)

    Roberto Carmona Rodríguez

    2012-01-01

    Full Text Available We propose a new type of neural adaptive control via dynamic neural networks. For a class of unknown nonlinear systems, a neural identifier-based feedback linearization controller is first used. Dead-zone and projection techniques are applied to assure the stability of neural identification. Then four types of compensator are addressed. The stability of closed-loop system is also proven.

  3. Neural Networks for Modeling and Control of Particle Accelerators

    CERN Document Server

    Edelen, A.L.; Chase, B.E.; Edstrom, D.; Milton, S.V.; Stabile, P.

    2016-01-01

    We describe some of the challenges of particle accelerator control, highlight recent advances in neural network techniques, discuss some promising avenues for incorporating neural networks into particle accelerator control systems, and describe a neural network-based control system that is being developed for resonance control of an RF electron gun at the Fermilab Accelerator Science and Technology (FAST) facility, including initial experimental results from a benchmark controller.

  4. Fusion Control of Flexible Logic Control and Neural Network

    Directory of Open Access Journals (Sweden)

    Lihua Fu

    2014-01-01

    Full Text Available Based on the basic physical meaning of error E and error variety EC, this paper analyzes the logical relationship between them and uses Universal Combinatorial Operation Model in Universal Logic to describe it. Accordingly, a flexible logic control method is put forward to realize effective control on multivariable nonlinear system. In order to implement fusion control with artificial neural network, this paper proposes a new neuron model of Zero-level Universal Combinatorial Operation in Universal Logic. And the artificial neural network of flexible logic control model is implemented based on the proposed neuron model. Finally, stability control, anti-interference control of double inverted-pendulum system, and free walking of cart pendulum system on a level track are realized, showing experimentally the feasibility and validity of this method.

  5. Neural PID Control Strategy for Networked Process Control

    Directory of Open Access Journals (Sweden)

    Jianhua Zhang

    2013-01-01

    Full Text Available A new method with a two-layer hierarchy is presented based on a neural proportional-integral-derivative (PID iterative learning method over the communication network for the closed-loop automatic tuning of a PID controller. It can enhance the performance of the well-known simple PID feedback control loop in the local field when real networked process control applied to systems with uncertain factors, such as external disturbance or randomly delayed measurements. The proposed PID iterative learning method is implemented by backpropagation neural networks whose weights are updated via minimizing tracking error entropy of closed-loop systems. The convergence in the mean square sense is analysed for closed-loop networked control systems. To demonstrate the potential applications of the proposed strategies, a pressure-tank experiment is provided to show the usefulness and effectiveness of the proposed design method in network process control systems.

  6. Neural Network for Optimization of Existing Control Systems

    DEFF Research Database (Denmark)

    Madsen, Per Printz

    1995-01-01

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

  7. Identification and Position Control of Marine Helm using Artificial Neural Network Neural Network

    Directory of Open Access Journals (Sweden)

    Hui ZHU

    2008-02-01

    Full Text Available If nonlinearities such as saturation of the amplifier gain and motor torque, gear backlash, and shaft compliances- just to name a few - are considered in the position control system of marine helm, traditional control methods are no longer sufficient to be used to improve the performance of the system. In this paper an alternative approach to traditional control methods - a neural network reference controller - is proposed to establish an adaptive control of the position of the marine helm to achieve the controlled variable at the command position. This neural network controller comprises of two neural networks. One is the plant model network used to identify the nonlinear system and the other the controller network used to control the output to follow the reference model. The experimental results demonstrate that this adaptive neural network reference controller has much better control performance than is obtained with traditional controllers.

  8. Neural Generalized Predictive Control of a non-linear Process

    DEFF Research Database (Denmark)

    Sørensen, Paul Haase; Nørgård, Peter Magnus; Ravn, Ole

    1998-01-01

    The use of neural network in non-linear control is made difficult by the fact the stability and robustness is not guaranteed and that the implementation in real time is non-trivial. In this paper we introduce a predictive controller based on a neural network model which has promising stability qu...... detail and discuss the implementation difficulties. The neural generalized predictive controller is tested on a pneumatic servo sys-tem....

  9. IDENTIFICATION AND CONTROL OF AN ASYNCHRONOUS MACHINE USING NEURAL NETWORKS

    Directory of Open Access Journals (Sweden)

    A ZERGAOUI

    2000-06-01

    Full Text Available In this work, we present the application of artificial neural networks to the identification and control of the asynchronous motor, which is a complex nonlinear system with variable internal dynamics.  We show that neural networks can be applied to control the stator currents of the induction motor.  The results of the different simulations are presented to evaluate the performance of the neural controller proposed.

  10. Are muscle synergies useful for neural control ?

    Directory of Open Access Journals (Sweden)

    Aymar ede Rugy

    2013-03-01

    Full Text Available The observation that the activity of multiple muscles can be well approximated by a few linear synergies is viewed by some as a sign that such low-dimensional modules constitute a key component of the neural control system. Here, we argue that the usefulness of muscle synergies as a control principle should be evaluated in terms of errors produced not only in muscle space, but also in task space. We used data from a force-aiming task in two dimensions at the wrist, using an EMG-driven virtual biomechanics technique that overcomes typical errors in predicting force from recorded EMG, to illustrate through simulation how synergy decomposition inevitably introduces substantial task space errors. Then, we computed the optimal pattern of muscle activation that minimizes summed-squared muscle activities, and demonstrated that synergy decomposition produced similar results on real and simulated data. We further assessed the influence of synergy decomposition on aiming errors in a more redundant system, using the optimal muscle pattern computed for the elbow-joint complex (i.e., 13 muscles acting in two dimensions. Because EMG records are typically not available from all contributing muscles, we also explored reconstructions from incomplete sets of muscles. The redundancy of a given set of muscles had opposite effects on the goodness of muscle reconstruction and on task achievement; higher redundancy is associated with better EMG approximation (lower residuals, but with higher aiming errors. Finally, we showed that the number of synergies required to approximate the optimal muscle pattern for an arbitrary biomechanical system increases with task-space dimensionality, which indicates that the capacity of synergy decomposition to explain behaviour depends critically on the scope of the original database. These results have implications regarding the viability of muscle synergy as a putative neural control mechanism, and also as a control algorithm to

  11. Neural processing of auditory signals and modular neural control for sound tropism of walking machines

    DEFF Research Database (Denmark)

    Manoonpong, Poramate; Pasemann, Frank; Fischer, Joern

    2005-01-01

    . The parameters of these networks are optimized by an evolutionary algorithm. In addition, a simple modular neural controller then generates the desired different walking patterns such that the machine walks straight, then turns towards a switched-on sound source, and then stops near to it....... and a neural preprocessing system together with a modular neural controller are used to generate a sound tropism of a four-legged walking machine. The neural preprocessing network is acting as a low-pass filter and it is followed by a network which discerns between signals coming from the left or the right...

  12. Added value of a resting ECG neural network that predicts cardiovascular mortality.

    Science.gov (United States)

    Perez, Marco V; Dewey, Frederick E; Tan, Swee Y; Myers, Jonathan; Froelicher, Victor F

    2009-01-01

    The resting 12-lead electrocardiogram (ECG) remains the most commonly used test in evaluating patients with suspected cardiovascular disease. Prognostic values of individual findings on the ECG have been reported but may be of limited use. The characteristics of 45,855 ECGs ordered by physician's discretion were first recorded and analyzed using a computerized system. Ninety percent of these ECGs were used to train an artifical neural network (ANN) to predict cardiovascular mortality (CVM) based on 132 ECG and four demographic characteristics. The ANN generated a Resting ECG Neural Network (RENN) score that was then tested in the remaining ECGs. The RENN score was finally assessed in a cohort of 2189 patients who underwent exercise treadmill testing and were followed for CVM. The RENN score was able to better predict CVM compared to individual ECG markers or a traditional Cox regression model in the testing cohort. Over a mean of 8.6 years, there were 156 cardiovascular deaths in the treadmill cohort. Among the patients who were classified as intermediate risk by Duke Treadmill Scoring (DTS), the third tertile of the RENN score demonstrated an adjusted Cox hazard ratio of 5.4 (95% CI 2.0-15.2) compared to the first RENN tertile. The 10-year CVM was 2.8%, 8.6% and 22% in the first, second and third RENN tertiles, respectively. An ANN that uses the resting ECG and demographic variables to predict CVM was created. The RENN score can further risk stratify patients deemed at moderate risk on exercise treadmill testing.

  13. [Control of cardiovascular risk factors among patients with diabetes with and without cardiovascular disease].

    Science.gov (United States)

    Herrero, A; Garzón, G; Gil, A; García, I; Vargas, E; Torres, N

    2015-10-01

    There is evidence that cardiovascular goals are beneficial in diabetes. To determine the distribution of cardiovascular risk levels in patients with diabetes and the clinical interventions they have received. Descriptive cross-sectional study. SERMAS (Madrid) 2010. All patients with diabetes. (n=41,096). Patients in primary or secondary prevention, metabolic and cardiovascular risk factors control, pharmacological and non-pharmacological interventions. Patient and professional variables. Around one-fifth (21.5%) (95%CI: 21.1% -21.9%) in secondary prevention (very high cardiovascular risk). HbA1c was under control in 31% (95%CI: 30.1%-32%), with 49.9% (95%CI: 48.8%-50.9%) with BP under control, and 39.4% (95% CI: 38.4%-40.4%) with LDL controlled. Only 8.9% (95%CI: 8.3%-9.5%) had a well-controlled HdA1c, BP and LDL, and in 19.8% (95%CI: 19%-20.6%) none of these were under control. Of those with an uncontrolled BP, 23.6% (95% CI: 23.2%-24%) had antihypertensive drugs. There was better control in patients older than 70 years, and those who lived in an urban center, or a lower number of patients per day. In diabetic patients with very high cardiovascular risk (secondary prevention), just half of them had good control of cardiovascular risk factors (BP and LDL). An association was found between better control and older than 70, urban center or lower number of patients per day. This suggests developing strategies to promote a comprehensive control of cardiovascular risk factors in diabetic patients in secondary prevention. Copyright © 2014 Sociedad Española de Médicos de Atención Primaria (SEMERGEN). Publicado por Elsevier España, S.L.U. All rights reserved.

  14. Multi-layer neural networks for robot control

    Science.gov (United States)

    Pourboghrat, Farzad

    1989-01-01

    Two neural learning controller designs for manipulators are considered. The first design is based on a neural inverse-dynamics system. The second is the combination of the first one with a neural adaptive state feedback system. Both types of controllers enable the manipulator to perform any given task very well after a period of training and to do other untrained tasks satisfactorily. The second design also enables the manipulator to compensate for unpredictable perturbations.

  15. Neural set point for the control of arterial pressure: role of the nucleus tractus solitarius

    Directory of Open Access Journals (Sweden)

    Valentinuzzi Max E

    2010-01-01

    Full Text Available Abstract Background Physiological experiments have shown that the mean arterial blood pressure (MAP can not be regulated after chemo and cardiopulmonary receptor denervation. Neuro-physiological information suggests that the nucleus tractus solitarius (NTS is the only structure that receives information from its rostral neural nuclei and from the cardiovascular receptors and projects to nuclei that regulate the circulatory variables. Methods From a control theory perspective, to answer if the cardiovascular regulation has a set point, we should find out whether in the cardiovascular control there is something equivalent to a comparator evaluating the error signal (between the rostral projections to the NTS and the feedback inputs. The NTS would function as a comparator if: a its lesion suppresses cardiovascular regulation; b the negative feedback loop still responds normally to perturbations (such as mechanical or electrical after cutting the rostral afferent fibers to the NTS; c perturbation of rostral neural structures (RNS to the NTS modifies the set point without changing the dynamics of the elicited response; and d cardiovascular responses to perturbations on neural structures within the negative feedback loop compensate for much faster than perturbations on the NTS rostral structures. Results From the control theory framework, experimental evidence found currently in the literature plus experimental results from our group was put together showing that the above-mentioned conditions (to show that the NTS functions as a comparator are satisfied. Conclusions Physiological experiments suggest that long-term blood pressure is regulated by the nervous system. The NTS functions as a comparator (evaluating the error signal between its RNS and the cardiovascular receptor afferents and projects to nuclei that regulate the circulatory variables. The mean arterial pressure (MAP is regulated by the feedback of chemo and cardiopulmonary receptors and

  16. Cardiovascular risk score and cardiovascular events among airline pilots: a case-control study.

    Science.gov (United States)

    Wirawan, I Made Ady; Larsen, Peter D; Aldington, Sarah; Griffiths, Robin F; Ellis, Chris J

    2012-05-01

    A cardiovascular risk prediction score is routinely applied by aviation authorities worldwide. We examined the accuracy of the Framingham-based risk chart used by the New Zealand Civil Aviation Authority in predicting cardiovascular events among airline pilots. A matched case-control design was applied to assess the association of 5-yr cardiovascular risk score and cardiovascular events in Oceania-based airline pilots. Cases were pilots with cardiovascular events as recorded on their medical records. Each case was age and gender matched with four controls that were randomly selected from the pilot population. To collect data before the events, 5-yr retrospective evaluations were conducted. Over a 16-yr study period we identified 15 cases of cardiovascular events, 9 (60%) of which were sudden clinical presentations and only 6 (40%) of which were detected using cardiovascular screening. There were 8 cases (53%) and 16 controls (27%) who had a 5-yr risk of > or = 10-15%. Almost half of the events (7/15) occurred in pilots whose highest 5-yr risk was in the 5-10% range. Cases were 3.91 times more likely to have highest 5-yr risk score of > or =10-15% than controls (OR = 3.91, 95% CI 1.04-16.35). The accuracy of the highest risk scores were moderate (AUC = 0.723, 95% CI 0.583-0.863). The cutoff point of 10% is valid, with a specificity of 0.73, but low sensitivity (0.53). Despite a valid and appropriate cutoff point, the tool had low sensitivity and was unable to predict almost half of the cardiovascular events.

  17. Neural Network Based Load Frequency Control for Restructuring ...

    African Journals Online (AJOL)

    Electric load variations can happen independently in both units. Both neural controllers are trained with the back propagation-through-time algorithm. Use of a neural network to model the dynamic system is avoided by introducing the Jacobian matrices of the system in the back propagation chain used in controller training.

  18. Implementation of neural network based non-linear predictive control

    DEFF Research Database (Denmark)

    Sørensen, Paul Haase; Nørgård, Peter Magnus; Ravn, Ole

    1999-01-01

    of non-linear systems. GPC is model based and in this paper we propose the use of a neural network for the modeling of the system. Based on the neural network model, a controller with extended control horizon is developed and the implementation issues are discussed, with particular emphasis...

  19. Accelerator diagnosis and control by Neural Nets

    Energy Technology Data Exchange (ETDEWEB)

    Spencer, J.E.

    1989-01-01

    Neural Nets (NN) have been described as a solution looking for a problem. In the last conference, Artificial Intelligence (AI) was considered in the accelerator context. While good for local surveillance and control, its use for large complex systems (LCS) was much more restricted. By contrast, NN provide a good metaphor for LCS. It can be argued that they are logically equivalent to multi-loop feedback/forward control of faulty systems, and therefore provide an ideal adaptive control system. Thus, where AI may be good for maintaining a 'golden orbit,' NN should be good for obtaining it via a quantitative approach to 'look and adjust' methods like operator tweaking which use pattern recognition to deal with hardware and software limitations, inaccuracies or errors as well as imprecise knowledge or understanding of effects like annealing and hysteresis. Further, insights from NN allow one to define feasibility conditions for LCS in terms of design constraints and tolerances. Hardware and software implications are discussed and several LCS of current interest are compared and contrasted. 15 refs., 5 figs.

  20. Range of control of cardiovascular variables by the hypothalamus

    Science.gov (United States)

    Smith, O. A.; Stephenson, R. B.; Randall, D. C.

    1974-01-01

    New methodologies were utilized to study the influence of the hypothalamus on the cardiovascular system. The regulation of myocardial activity was investigated in monkeys with hypothalamic lesions that eliminate cardiovascular responses. Observations showed that a specific part of the hypothalamus regulates changes in myocardial contractility that accompanies emotion. Studies of the hypothalamus control of renal blood flow showed the powerful potential control of this organ over renal circulation.

  1. Active Engine Mounting Control Algorithm Using Neural Network

    Directory of Open Access Journals (Sweden)

    Fadly Jashi Darsivan

    2009-01-01

    Full Text Available This paper proposes the application of neural network as a controller to isolate engine vibration in an active engine mounting system. It has been shown that the NARMA-L2 neurocontroller has the ability to reject disturbances from a plant. The disturbance is assumed to be both impulse and sinusoidal disturbances that are induced by the engine. The performance of the neural network controller is compared with conventional PD and PID controllers tuned using Ziegler-Nichols. From the result simulated the neural network controller has shown better ability to isolate the engine vibration than the conventional controllers.

  2. Epinephrine biosynthesis: hormonal and neural control during stress.

    Science.gov (United States)

    Wong, Dona Lee

    2006-01-01

    1. Stress contributes to the pathophysiology of many diseases, including psychiatric disorders, immune dysfunction, nicotine addiction and cardiovascular illness. Epinephrine and the glucocorticoids, cortisol and corticosterone, are major stress hormones. 2. Release of epinephrine from the adrenal medulla and glucocorticoids from the adrenal cortex initiate the biological responses permitting the organism to cope with adverse psychological, physiological and environmental stressors. Following its massive release during stress, epinephrine must be restored to replenish cellular pools and sustain release to maintain the heightened awareness and sequelae of responses to re-establish homeostasis and ensure survival. 3. Epinephrine is regulated in part through its biosynthesis catalyzed by the final enzyme in the catecholamine pathway, phenylethanolamine N-methyltransferase (E.C. 2.1.1.28, PNMT). PNMT expression, in turn, is controlled through hormonal and neural stimuli, which exert their effects on gene transcription through protein stability. 4. The pioneering work of Julius Axelrod forged the path to our present understanding of how the stress hormone and neurotransmitter epinephrine, is regulated, in particular via its biosynthesis by PNMT.

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

    DEFF Research Database (Denmark)

    Sørensen, O.

    1997-01-01

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

  4. Computer model of cardiovascular control system responses to exercise

    Science.gov (United States)

    Croston, R. C.; Rummel, J. A.; Kay, F. J.

    1973-01-01

    Approaches of systems analysis and mathematical modeling together with computer simulation techniques are applied to the cardiovascular system in order to simulate dynamic responses of the system to a range of exercise work loads. A block diagram of the circulatory model is presented, taking into account arterial segments, venous segments, arterio-venous circulation branches, and the heart. A cardiovascular control system model is also discussed together with model test results.

  5. Neural control of choroidal blood flow.

    Science.gov (United States)

    Reiner, Anton; Fitzgerald, Malinda E C; Del Mar, Nobel; Li, Chunyan

    2017-12-08

    The choroid is richly innervated by parasympathetic, sympathetic and trigeminal sensory nerve fibers that regulate choroidal blood flow in birds and mammals, and presumably other vertebrate classes as well. The parasympathetic innervation has been shown to vasodilate and increase choroidal blood flow, the sympathetic input has been shown to vasoconstrict and decrease choroidal blood flow, and the sensory input has been shown to both convey pain and thermal information centrally and act locally to vasodilate and increase choroidal blood flow. As the choroid lies behind the retina and cannot respond readily to retinal metabolic signals, its innervation is important for adjustments in flow required by either retinal activity, by fluctuations in the systemic blood pressure driving choroidal perfusion, and possibly by retinal temperature. The former two appear to be mediated by the sympathetic and parasympathetic nervous systems, via central circuits responsive to retinal activity and systemic blood pressure, but adjustments for ocular perfusion pressure also appear to be influenced by local autoregulatory myogenic mechanisms. Adaptive choroidal responses to temperature may be mediated by trigeminal sensory fibers. Impairments in the neural control of choroidal blood flow occur with aging, and various ocular or systemic diseases such as glaucoma, age-related macular degeneration (AMD), hypertension, and diabetes, and may contribute to retinal pathology and dysfunction in these conditions, or in the case of AMD be a precondition. The present manuscript reviews findings in birds and mammals that contribute to the above-summarized understanding of the roles of the autonomic and sensory innervation of the choroid in controlling choroidal blood flow, and in the importance of such regulation for maintaining retinal health. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

  6. Neural-Network Control Of Prosthetic And Robotic Hands

    Science.gov (United States)

    Buckley, Theresa M.

    1991-01-01

    Electronic neural networks proposed for use in controlling robotic and prosthetic hands and exoskeletal or glovelike electromechanical devices aiding intact but nonfunctional hands. Specific to patient, who activates grasping motion by voice command, by mechanical switch, or by myoelectric impulse. Patient retains higher-level control, while lower-level control provided by neural network analogous to that of miniature brain. During training, patient teaches miniature brain to perform specialized, anthropomorphic movements unique to himself or herself.

  7. Neural Networks for Modeling and Control of Particle Accelerators

    Science.gov (United States)

    Edelen, A. L.; Biedron, S. G.; Chase, B. E.; Edstrom, D.; Milton, S. V.; Stabile, P.

    2016-04-01

    Particle accelerators are host to myriad nonlinear and complex physical phenomena. They often involve a multitude of interacting systems, are subject to tight performance demands, and should be able to run for extended periods of time with minimal interruptions. Often times, traditional control techniques cannot fully meet these requirements. One promising avenue is to introduce machine learning and sophisticated control techniques inspired by artificial intelligence, particularly in light of recent theoretical and practical advances in these fields. Within machine learning and artificial intelligence, neural networks are particularly well-suited to modeling, control, and diagnostic analysis of complex, nonlinear, and time-varying systems, as well as systems with large parameter spaces. Consequently, the use of neural network-based modeling and control techniques could be of significant benefit to particle accelerators. For the same reasons, particle accelerators are also ideal test-beds for these techniques. Many early attempts to apply neural networks to particle accelerators yielded mixed results due to the relative immaturity of the technology for such tasks. The purpose of this paper is to re-introduce neural networks to the particle accelerator community and report on some work in neural network control that is being conducted as part of a dedicated collaboration between Fermilab and Colorado State University (CSU). We describe some of the challenges of particle accelerator control, highlight recent advances in neural network techniques, discuss some promising avenues for incorporating neural networks into particle accelerator control systems, and describe a neural network-based control system that is being developed for resonance control of an RF electron gun at the Fermilab Accelerator Science and Technology (FAST) facility, including initial experimental results from a benchmark controller.

  8. cardiovasculares

    Directory of Open Access Journals (Sweden)

    Cristina Guerrero

    2006-01-01

    Full Text Available Uno de los aspectos que más discusión ha suscitado en los últimos tiempos entre quienes nos dedicamos al estudio de la emoción tiene que ver con la eventual asociación entre percepción, valoración y respuesta fisiológica. Esto es, siguiendo la máxima aristotélica, cabría cuestionar si las cosas son como son o son como cada quien las percibe. El objetivo de este experimento ha sido establecer la existencia de una conexión entre percepción de control y responsividad cardiovascular. La muestra estudiada ha estado conformada por estudiantes de la Universidad de Castellón; todos ellos han participado de forma voluntaria. La prueba de estrés ha consistido en un examen real de una asignatura troncal de la titulación que cursaban los participantes. Así pues, utilizando una situación de estrés real, hipotetizamos que las respuestas cardiovasculares (medidas a través de la tasa cardiaca, la presión sanguínea sistólica y la presión sanguínea diastólica dependen de la percepción de control que el individuo tiene, o cree tener, sobre la situación.

  9. Neural Processing of Auditory Signals and Modular Neural Control for Sound Tropism of Walking Machines

    Directory of Open Access Journals (Sweden)

    Hubert Roth

    2008-11-01

    Full Text Available The specialized hairs and slit sensillae of spiders (Cupiennius salei can sense the airflow and auditory signals in a low-frequency range. They provide the sensor information for reactive behavior, like e.g. capturing a prey. In analogy, in this paper a setup is described where two microphones and a neural preprocessing system together with a modular neural controller are used to generate a sound tropism of a four-legged walking machine. The neural preprocessing network is acting as a low-pass filter and it is followed by a network which discerns between signals coming from the left or the right. The parameters of these networks are optimized by an evolutionary algorithm. In addition, a simple modular neural controller then generates the desired different walking patterns such that the machine walks straight, then turns towards a switched-on sound source, and then stops near to it.

  10. Stability of a neural predictive controller scheme on a neural model

    DEFF Research Database (Denmark)

    Luther, Jim Benjamin; Sørensen, Paul Haase

    2009-01-01

    In previous works presenting various forms of neural-network-based predictive controllers, the main emphasis has been on the implementation aspects, i.e. the development of a robust optimization algorithm for the controller, which will be able to perform in real time. However, the stability issue....... The resulting controller is tested on a nonlinear pneumatic servo system....

  11. Qualitative analysis and control of complex neural networks with delays

    CERN Document Server

    Wang, Zhanshan; Zheng, Chengde

    2016-01-01

    This book focuses on the stability of the dynamical neural system, synchronization of the coupling neural system and their applications in automation control and electrical engineering. The redefined concept of stability, synchronization and consensus are adopted to provide a better explanation of the complex neural network. Researchers in the fields of dynamical systems, computer science, electrical engineering and mathematics will benefit from the discussions on complex systems. The book will also help readers to better understand the theory behind the control technique and its design.

  12. Robust adaptive fuzzy neural tracking control for a class of unknown ...

    Indian Academy of Sciences (India)

    In this paper, an adaptive fuzzy neural controller (AFNC) for a class of unknown chaotic systems is proposed. The proposed AFNC is comprised of a fuzzy neural controller and a robust controller. The fuzzy neural controller including a fuzzy neural network identifier (FNNI) is the principal controller. The FNNI is used for ...

  13. Genetic control of active neural circuits

    Directory of Open Access Journals (Sweden)

    Leon Reijmers

    2009-12-01

    Full Text Available The use of molecular tools to study the neurobiology of complex behaviors has been hampered by an inability to target the desired changes to relevant groups of neurons. Specific memories and specific sensory representations are sparsely encoded by a small fraction of neurons embedded in a sea of morphologically and functionally similar cells. In this review we discuss genetics techniques that are being developed to address this difficulty. In several studies the use of promoter elements that are responsive to neural activity have been used to drive long lasting genetic alterations into neural ensembles that are activated by natural environmental stimuli. This approach has been used to examine neural activity patterns during learning and retrieval of a memory, to examine the regulation of receptor trafficking following learning and to functionally manipulate a specific memory trace. We suggest that these techniques will provide a general approach to experimentally investigate the link between patterns of environmentally activated neural firing and cognitive processes such as perception and memory.

  14. Attractor switching by neural control of chaotic neurodynamics.

    Science.gov (United States)

    Pasemann, F; Stollenwerk, N

    1998-11-01

    Chaotic attractors of discrete-time neural networks include infinitely many unstable periodic orbits, which can be stabilized by small parameter changes in a feedback control. Here we explore the control of unstable periodic orbits in a chaotic neural network with only two neurons. Analytically, a local control algorithm is derived on the basis of least squares minimization of the future deviations between actual system states and the desired orbit. This delayed control allows a consistent neural implementation, i.e. the same types of neurons are used for chaotic and controlling modules. The control signal is realized with one layer of neurons, allowing selective switching between different stabilized periodic orbits. For chaotic modules with noise, random switching between different periodic orbits is observed.

  15. A hyperstable neural network for the modelling and control of ...

    Indian Academy of Sciences (India)

    A multivariable hyperstable robust adaptive decoupling control algorithm based on a neural network is presented for the control of nonlinear multivariable coupled systems with unknown parameters and structure. The Popov theorem is used in the design of the controller. The modelling errors, coupling action and other ...

  16. Neural Control of Energy Balance: Translating Circuits to Therapies

    OpenAIRE

    Gautron, Laurent; Elmquist, Joel K.; Williams, Kevin W.

    2015-01-01

    Recent insights into the neural circuits controlling energy balance and glucose homeostasis have rekindled the hope for development of novel treatments for obesity and diabetes. However, many therapies contribute relatively modest beneficial gains with accompanying side effects, and the mechanisms of action for other interventions remain undefined. This Review summarizes current knowledge linking the neural circuits regulating energy and glucose balance with current and potential pharmacother...

  17. PID Neural Network Based Speed Control of Asynchronous Motor Using Programmable Logic Controller

    Directory of Open Access Journals (Sweden)

    MARABA, V. A.

    2011-11-01

    Full Text Available This paper deals with the structure and characteristics of PID Neural Network controller for single input and single output systems. PID Neural Network is a new kind of controller that includes the advantages of artificial neural networks and classic PID controller. Functioning of this controller is based on the update of controller parameters according to the value extracted from system output pursuant to the rules of back propagation algorithm used in artificial neural networks. Parameters obtained from the application of PID Neural Network training algorithm on the speed model of the asynchronous motor exhibiting second order linear behavior were used in the real time speed control of the motor. Programmable logic controller (PLC was used as real time controller. The real time control results show that reference speed successfully maintained under various load conditions.

  18. Neural networks for process control and optimization: two industrial applications.

    Science.gov (United States)

    Bloch, Gérard; Denoeux, Thierry

    2003-01-01

    The two most widely used neural models, multilayer perceptron (MLP) and radial basis function network (RBFN), are presented in the framework of system identification and control. The main steps for building such nonlinear black box models are regressor choice, selection of internal architecture, and parameter estimation. The advantages of neural network models are summarized: universal approximation capabilities, flexibility, and parsimony. Two applications are described in steel industry and water treatment, respectively, the control of alloying process in a hot dipped galvanizing line and the control of a coagulation process in a drinking water treatment plant. These examples highlight the interest of neural techniques, when complex nonlinear phenomena are involved, but the empirical knowledge of control operators can be learned.

  19. Dual adaptive dynamic control of mobile robots using neural networks.

    Science.gov (United States)

    Bugeja, Marvin K; Fabri, Simon G; Camilleri, Liberato

    2009-02-01

    This paper proposes two novel dual adaptive neural control schemes for the dynamic control of nonholonomic mobile robots. The two schemes are developed in discrete time, and the robot's nonlinear dynamic functions are assumed to be unknown. Gaussian radial basis function and sigmoidal multilayer perceptron neural networks are used for function approximation. In each scheme, the unknown network parameters are estimated stochastically in real time, and no preliminary offline neural network training is used. In contrast to other adaptive techniques hitherto proposed in the literature on mobile robots, the dual control laws presented in this paper do not rely on the heuristic certainty equivalence property but account for the uncertainty in the estimates. This results in a major improvement in tracking performance, despite the plant uncertainty and unmodeled dynamics. Monte Carlo simulation and statistical hypothesis testing are used to illustrate the effectiveness of the two proposed stochastic controllers as applied to the trajectory-tracking problem of a differentially driven wheeled mobile robot.

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

    Energy Technology Data Exchange (ETDEWEB)

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

    1996-04-01

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

  1. Stability and synchronization control of stochastic neural networks

    CERN Document Server

    Zhou, Wuneng; Zhou, Liuwei; Tong, Dongbing

    2016-01-01

    This book reports on the latest findings in the study of Stochastic Neural Networks (SNN). The book collects the novel model of the disturbance driven by Levy process, the research method of M-matrix, and the adaptive control method of the SNN in the context of stability and synchronization control. The book will be of interest to university researchers, graduate students in control science and engineering and neural networks who wish to learn the core principles, methods, algorithms and applications of SNN.

  2. Neural networks for predictive control of the mechanism of ...

    African Journals Online (AJOL)

    In this paper, we are interested in the study of the control of orientation of a wind turbine like means of optimization of his output/input ratio (efficiency). The approach suggested is based on the neural predictive control which is justified by the randomness of the wind on the one hand, and on the other hand by the capacity of ...

  3. Adaptive plasticity in vestibular influences on cardiovascular control

    Science.gov (United States)

    Yates, B. J.; Holmes, M. J.; Jian, B. J.

    2000-01-01

    Data collected in both human subjects and animal models indicate that the vestibular system influences the control of blood pressure. In animals, peripheral vestibular lesions diminish the capacity to rapidly and accurately make cardiovascular adjustments to changes in posture. Thus, one role of vestibulo-cardiovascular influences is to elicit changes in blood distribution in the body so that stable blood pressure is maintained during movement. However, deficits in correcting blood pressure following vestibular lesions diminish over time, and are less severe when non-labyrinthine sensory cues regarding body position in space are provided. These observations show that pathways that mediate vestibulo-sympathetic reflexes can be subject to plastic changes. This review considers the adaptive plasticity in cardiovascular responses elicited by the central vestibular system. Recent data indicate that the posterior cerebellar vermis may play an important role in adaptation of these responses, such that ablation of the posterior vermis impairs recovery of orthostatic tolerance following subsequent vestibular lesions. Furthermore, recent experiments suggest that non-labyrinthine inputs to the central vestibular system may be important in controlling blood pressure during movement, particularly following vestibular dysfunction. A number of sensory inputs appear to be integrated to produce cardiovascular adjustments during changes in posture. Although loss of any one of these inputs does not induce lability in blood pressure, it is likely that maximal blood pressure stability is achieved by the integration of a variety of sensory cues signaling body position in space.

  4. Cardiovascular risk awareness, treatment, and control in urban Latin America.

    Science.gov (United States)

    Silva, Honorio; Hernandez-Hernandez, Rafael; Vinueza, Raul; Velasco, Manuel; Boissonnet, Carlos Pablo; Escobedo, Jorge; Silva, H Elif; Pramparo, Palmira; Wilson, Elinor

    2010-01-01

    Effective prevention and treatment of cardiovascular diseases require regular screening for risk factors, high awareness of the condition, effective treatment of the identified risk factors, and adherence to the prescribed treatment. The Cardiovascular Risk Factor Multiple Evaluation in Latin America study was a cross-sectional, population-based, observational study of major cardiovascular risk factors-including hypertension, diabetes, and hypercholesterolemia-in 7 Latin American cities. This report presents data on assessment, diagnosis, extent, and effectiveness of treatment, adherence to treatment, and reasons for nonadherence. Data were collected through household questionnaire-based interviews administered to 5383 men and 6167 women, 25-64 years of age, living in the following cities: Barquisimeto, Venezuela; Bogota, Colombia; Buenos Aires, Argentina; Lima, Peru; Mexico City, Mexico; Quito, Ecuador; and Santiago, Chile. Participants also completed a clinic visit for anthromorphometric and laboratory assessments. Rates of prior diagnosis of hypertension and diabetes were high (64% and 78% of affected individuals, respectively) but relatively low for hypercholesterolemia (41%). The majority of affected individuals (hypercholesterolemia 88%, diabetes 67%, and hypertension 53%) were untreated. Among individuals who were receiving pharmacologic treatment, targets for control of hypertension, diabetes, and hypercholesterolemia were achieved by 51%, 16%, and 52%, respectively. Adherence to treatment was observed in 69% of individuals with hypertension, 63% with diabetes, and 66% with hypercholesterolemia. Forgetfulness was the major cause of nonadherence for all 3 conditions. There is a substantial need for increasing patient education, diagnosis, treatment, adherence, and control of cardiovascular risk factors in the 7 Latin American cities.

  5. The application of neural network PID controller to control the light gasoline etherification

    Science.gov (United States)

    Cheng, Huanxin; Zhang, Yimin; Kong, Lingling; Meng, Xiangyong

    2017-06-01

    Light gasoline etherification technology can effectively improve the quality of gasoline, which is environmental- friendly and economical. By combining BP neural network and PID control and using BP neural network self-learning ability for online parameter tuning, this method optimizes the parameters of PID controller and applies this to the Fcc gas flow control to achieve the control of the final product- heavy oil concentration. Finally, through MATLAB simulation, it is found that the PID control based on BP neural network has better controlling effect than traditional PID control.

  6. Distributed Recurrent Neural Forward Models with Neural Control for Complex Locomotion in Walking Robots

    DEFF Research Database (Denmark)

    Dasgupta, Sakyasingha; Goldschmidt, Dennis; Wörgötter, Florentin

    2015-01-01

    movements, (2) distributed (at each leg) recurrent neural network based adaptive forward models with efference copies as internal models for sensory predictions and instantaneous state estimations, and (3) searching and elevation control for adapting the movement of an individual leg to deal with different...... conditions, like uneven terrains, gaps, obstacles etc. Biological study has revealed that such complex behaviors are a result of a combination of biomechanics and neural mechanism thus representing the true nature of embodied interactions. While the biomechanics helps maintain flexibility and sustain...... a variety of movements, the neural mechanisms generate movements while making appropriate predictions crucial for achieving adaptation. Such predictions or planning ahead can be achieved by way of internal models that are grounded in the overall behavior of the animal. Inspired by these findings, we present...

  7. Steam turbine stress control using NARX neural network

    Science.gov (United States)

    Dominiczak, Krzysztof; Rzadkowski, Romuald; Radulski, Wojciech

    2015-11-01

    Considered here is concept of steam turbine stress control, which is based on Nonlinear AutoRegressive neural networks with eXogenous inputs. Using NARX neural networks,whichwere trained based on experimentally validated FE model allows to control stresses in protected thickwalled steam turbine element with FE model quality. Additionally NARX neural network, which were trained base on FE model, includes: nonlinearity of steam expansion in turbine steam path during transients, nonlinearity of heat exchange inside the turbine during transients and nonlinearity of material properties during transients. In this article NARX neural networks stress controls is shown as an example of HP rotor of 18K390 turbine. HP part thermodynamic model as well as heat exchange model in vicinity of HP rotor,whichwere used in FE model of the HP rotor and the HP rotor FE model itself were validated based on experimental data for real turbine transient events. In such a way it is ensured that NARX neural network behave as real HP rotor during steam turbine transient events.

  8. Four Degree Freedom Robot Arm with Fuzzy Neural Network Control

    Directory of Open Access Journals (Sweden)

    Şinasi Arslan

    2013-01-01

    Full Text Available In this study, the control of four degree freedom robot arm has been realized with the computed torque control method.. It is usually required that the four jointed robot arm has high precision capability and good maneuverability for using in industrial applications. Besides, high speed working and external applied loads have been acting as important roles. For those purposes, the computed torque control method has been developed in a good manner that the robot arm can track the given trajectory, which has been able to enhance the feedback control together with fuzzy neural network control. The simulation results have proved that the computed torque control with the neural network has been so successful in robot control.

  9. Space motion sickness: The sensory motor controls and cardiovascular correlation

    Science.gov (United States)

    Souvestre, Philippe A.; Blaber, Andrew P.; Landrock, Clinton K.

    supine parasympathetic activity pre-flight may present with PFOI indicators. Not only HRV provides information on autonomic regulation, but HRV pattern appears to be chaotic and/or fractal. Beat-by-beat HRV yields fractal dimension of the cardiovascular control system [C.K. Peng, J. Mistus, J.M. Hausdorff, S. Havlin, H.E. Stanley, A.L. Goldberger, Long-range anticorrelations and non-Gaussian behavior of the heartbeat, Physics Review Letters 70 (1999) 1343-1346]. Similar properties can be found in other physiological signals such as breathing intervals and gait pattern [N. Scafetta, R. Moon, B.J. West, Physiological signals and their fractal response to stress conditions, environmental changes and neurodegenerative diseases, in: Proceedings of The 25th Army Science Conference (ASC), Orlando, Florida, November 27-30, 2006]. ConclusionsA strong correlation between unmitigated SMS and PFOI related symptoms in astronauts has been presented. There is also strong correlation with PDS related symptoms, which can be accurately identified, measured, and monitored via a specific ocular-vestibular-postural monitoring system along with relevant clinical data. Along with the associated autonomic interactions detected by HRV, the fractal nature of the HRV data may provide useful information on the nature and complexity of central neural controls in relation to physiological [A.P. Blaber, R.L. Bondar, R. Freeman, Coarse grained spectral analysis of HR and BP variability in patients with autonomic failure, American Journal of Physiology 271 (1996) H1555-H1564] and mental stress [Y. Hoshikawa, Y. Yamamoto, Effects of Stroop color-word conflict test on the autonomic nervous system responses, American Journal of Physiology, 1997]. The data presented provide strong evidence that proper biomedical assessment methodologies employed with appropriate technology can lead to better understanding Astronauts' pre-flight and post-flight biomedical status, necessary to further human exploration in

  10. Adaptive model predictive process control using neural networks

    Science.gov (United States)

    Buescher, K.L.; Baum, C.C.; Jones, R.D.

    1997-08-19

    A control system for controlling the output of at least one plant process output parameter is implemented by adaptive model predictive control using a neural network. An improved method and apparatus provides for sampling plant output and control input at a first sampling rate to provide control inputs at the fast rate. The MPC system is, however, provided with a network state vector that is constructed at a second, slower rate so that the input control values used by the MPC system are averaged over a gapped time period. Another improvement is a provision for on-line training that may include difference training, curvature training, and basis center adjustment to maintain the weights and basis centers of the neural in an updated state that can follow changes in the plant operation apart from initial off-line training data. 46 figs.

  11. Spiking neural network-based control chart pattern recognition

    Directory of Open Access Journals (Sweden)

    Medhat H.A. Awadalla

    2012-03-01

    Full Text Available Due to an increasing competition in products, consumers have become more critical in choosing products. The quality of products has become more important. Statistical Process Control (SPC is usually used to improve the quality of products. Control charting plays the most important role in SPC. Control charts help to monitor the behavior of the process to determine whether it is stable or not. Unnatural patterns in control charts mean that there are some unnatural causes for variations in SPC. Spiking neural networks (SNNs are the third generation of artificial neural networks that consider time as an important feature for information representation and processing. In this paper, a spiking neural network architecture is proposed to be used for control charts pattern recognition (CCPR. Furthermore, enhancements to the SpikeProp learning algorithm are proposed. These enhancements provide additional learning rules for the synaptic delays, time constants and for the neurons thresholds. Simulated experiments have been conducted and the achieved results show a remarkable improvement in the overall performance compared with artificial neural networks.

  12. Neural Control Mechanisms and Body Fluid Homeostasis

    Science.gov (United States)

    Johnson, Alan Kim

    1998-01-01

    The goal of the proposed research was to study the nature of afferent signals to the brain that reflect the status of body fluid balance and to investigate the central neural mechanisms that process this information for the activation of response systems which restore body fluid homeostasis. That is, in the face of loss of fluids from intracellular or extracellular fluid compartments, animals seek and ingest water and ionic solutions (particularly Na(+) solutions) to restore the intracellular and extracellular spaces. Over recent years, our laboratory has generated a substantial body of information indicating that: (1) a fall in systemic arterial pressure facilitates the ingestion of rehydrating solutions and (2) that the actions of brain amine systems (e.g., norepinephrine; serotonin) are critical for precise correction of fluid losses. Because both acute and chronic dehydration are associated with physiological stresses, such as exercise and sustained exposure to microgravity, the present research will aid in achieving a better understanding of how vital information is handled by the nervous system for maintenance of the body's fluid matrix which is critical for health and well-being.

  13. An architecture for designing fuzzy logic controllers using neural networks

    Science.gov (United States)

    Berenji, Hamid R.

    1991-01-01

    Described here is an architecture for designing fuzzy controllers through a hierarchical process of control rule acquisition and by using special classes of neural network learning techniques. A new method for learning to refine a fuzzy logic controller is introduced. A reinforcement learning technique is used in conjunction with a multi-layer neural network model of a fuzzy controller. The model learns by updating its prediction of the plant's behavior and is related to the Sutton's Temporal Difference (TD) method. The method proposed here has the advantage of using the control knowledge of an experienced operator and fine-tuning it through the process of learning. The approach is applied to a cart-pole balancing system.

  14. A Gain-Scheduling PI Control Based on Neural Networks

    Directory of Open Access Journals (Sweden)

    Stefania Tronci

    2017-01-01

    Full Text Available This paper presents a gain-scheduling design technique that relies upon neural models to approximate plant behaviour. The controller design is based on generic model control (GMC formalisms and linearization of the neural model of the process. As a result, a PI controller action is obtained, where the gain depends on the state of the system and is adapted instantaneously on-line. The algorithm is tested on a nonisothermal continuous stirred tank reactor (CSTR, considering both single-input single-output (SISO and multi-input multi-output (MIMO control problems. Simulation results show that the proposed controller provides satisfactory performance during set-point changes and disturbance rejection.

  15. Discriminative training of self-structuring hidden control neural models

    DEFF Research Database (Denmark)

    Sørensen, Helge Bjarup Dissing; Hartmann, Uwe; Hunnerup, Preben

    1995-01-01

    This paper presents a new training algorithm for self-structuring hidden control neural (SHC) models. The SHC models were trained non-discriminatively for speech recognition applications. Better recognition performance can generally be achieved, if discriminative training is applied instead. Thus...

  16. Bionic cardiology: exploration into a wealth of controllable body parts in the cardiovascular system.

    Science.gov (United States)

    Sugimachi, Masaru; Sunagawa, Kenji

    2009-01-01

    Bionic cardiology is the medical science of exploring electronic control of the body, usually via the neural system. Mimicking or modifying biological regulation is a strategy used to combat diseases. Control of ventricular rate during atrial fibrillation by selective vagal stimulation, suppression of ischemia-related ventricular fibrillation by vagal stimulation, and reproduction of neurally commanded heart rate are some examples of bionic treatment for arrhythmia. Implantable radio-frequency-coupled on-demand carotid sinus stimulators succeeded in interrupting or preventing anginal attacks but were replaced later by coronary revascularization. Similar but fixed-intensity carotid sinus stimulators were used for hypertension but were also replaced by drugs. Recently, however, a self-powered implantable device has been reappraised for the treatment of drug-resistant hypertension. Closed-loop spinal cord stimulation has successfully treated severe orthostatic hypotension in a limited number of patients. Vagal nerve stimulation is effective in treating heart failure in animals, and a small-size clinical trial has just started. Simultaneous corrections of multiple hemodynamic abnormalities in an acute decompensated state are accomplished simply by quantifying fundamental cardiovascular parameters and controlling these parameters. Bionic cardiology will continue to promote the development of more sophisticated device-based therapies for otherwise untreatable diseases and will inspire more intricate applications in the twenty-first century.

  17. Neural control of energy balance: translating circuits to therapies.

    Science.gov (United States)

    Gautron, Laurent; Elmquist, Joel K; Williams, Kevin W

    2015-03-26

    Recent insights into the neural circuits controlling energy balance and glucose homeostasis have rekindled the hope for development of novel treatments for obesity and diabetes. However, many therapies contribute relatively modest beneficial gains with accompanying side effects, and the mechanisms of action for other interventions remain undefined. This Review summarizes current knowledge linking the neural circuits regulating energy and glucose balance with current and potential pharmacotherapeutic and surgical interventions for the treatment of obesity and diabetes. Copyright © 2015 Elsevier Inc. All rights reserved.

  18. A Review of Cardiovascular Autonomic Control in Cluster Headache

    DEFF Research Database (Denmark)

    Barloese, Mads C J

    2016-01-01

    OBJECTIVE: This review aims to evaluate existing literature concerning cardiovascular autonomic function and CH. Suggestions about future research are offered and known difficulties in investigating the autonomic nervous system in cluster headache are discussed. BACKGROUND: Little is known...... of the pathophysiological mechanisms behind cluster headache. Cranial autonomic features are an inherent and diagnostic feature; however, a number of studies and clinical observations support the involvement of systemic autonomic control in its pathophysiology. Further, cluster headache attacks are apparently more easily...... triggered during periods of parasympathetic dominance. A better understanding of this interaction may provide insight into central autonomic regulation and its role in cluster headache. METHODS: A PubMed search was performed in April 2015 using the search terms "cluster headache," "cardiovascular...

  19. Adaptive nonlinear control of missiles using neural networks

    Science.gov (United States)

    McFarland, Michael Bryan

    Research has shown that neural networks can be used to improve upon approximate dynamic inversion for control of uncertain nonlinear systems. In one architecture, the neural network adaptively cancels inversion errors through on-line learning. Such learning is accomplished by a simple weight update rule derived from Lyapunov theory, thus assuring stability of the closed-loop system. In this research, previous results using linear-in-parameters neural networks were reformulated in the context of a more general class of composite nonlinear systems, and the control scheme was shown to possess important similarities and major differences with established methods of adaptive control. The neural-adaptive nonlinear control methodology in question has been used to design an autopilot for an anti-air missile with enhanced agile maneuvering capability, and simulation results indicate that this approach is a feasible one. There are, however, certain difficulties associated with choosing the proper network architecture which make it difficult to achieve the rapid learning required in this application. Accordingly, this technique has been further extended to incorporate the important class of feedforward neural networks with a single hidden layer. These neural networks feature well-known approximation capabilities and provide an effective, although nonlinear, parameterization of the adaptive control problem. Numerical results from a six-degree-of-freedom nonlinear agile anti-air missile simulation demonstrate the effectiveness of the autopilot design based on multilayer networks. Previous work in this area has implicitly assumed precise knowledge of the plant order, and made no allowances for unmodeled dynamics. This thesis describes an approach to the problem of controlling a class of nonlinear systems in the face of both unknown nonlinearities and unmodeled dynamics. The proposed methodology is similar to robust adaptive control techniques derived for control of linear

  20. The Adaptive Neural Network Control of Quadrotor Helicopter

    Directory of Open Access Journals (Sweden)

    A. S. Yushenko

    2017-01-01

    Full Text Available The current steady-rising interest in using the unmanned multi-rotor aerial vehicles (UMAV designed to solve a wide range of tasks is, mainly, due to their simple design and high weight-carrying capacity as compared to classical helicopter options. Unfortunately, to solve a problem of multi-copter control is complicated because of essential nonlinearity and environmental perturbations. The most widely spread PID controllers and linear-quadratic regulators do not quite well cope with this task. The need arises for the prompt adjustment of PID controller coefficients in the course of operation or their complete re-tuning in cases of changing parameters of the control object.One of the control methods under changing conditions is the use of the sliding mode. This technology enables us to reach the stabilization and proper operation of the controlled system even under accidental external exposures and when there is a lack of the reasonably accurate mathematical model of the control object. The sliding principle is to ensure the system motion in the immediate vicinity of the sliding surface in the phase space. On the other hand, the sliding mode has some essential disadvantages. The most significant one is the high-frequency jitter of the system near the sliding surface. The sliding mode also implies the complete knowledge of the system dynamics. Various methods have been proposed to eliminate these drawbacks. For example, A.G. Aissaoui’s, H. Abid’s and M. Abid’s paper describes the application of fuzzy logic to control a drive and in Lon-Chen Hung’s and Hung-Yuan Chung’s paper an artificial neural network is used for the manipulator control.This paper presents a method of the quad-copter control with the aid of a neural network controller. This method enables us to control the system without a priori information on parameters of the dynamic model of the controlled object. The main neural network is a MIMO (“Multiple Input Multiple

  1. Projection learning algorithm for threshold - controlled neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Reznik, A.M.

    1995-03-01

    The projection learning algorithm proposed in [1, 2] and further developed in [3] substantially improves the efficiency of memorizing information and accelerates the learning process in neural networks. This algorithm is compatible with the completely connected neural network architecture (the Hopfield network [4]), but its application to other networks involves a number of difficulties. The main difficulties include constraints on interconnection structure and the need to eliminate the state uncertainty of latent neurons if such are present in the network. Despite the encouraging preliminary results of [3], further extension of the applications of the projection algorithm therefore remains problematic. In this paper, which is a continuation of the work begun in [3], we consider threshold-controlled neural networks. Networks of this type are quite common. They represent the receptor neuron layers in some neurocomputer designs. A similar structure is observed in the lower divisions of biological sensory systems [5]. In multilayer projection neural networks with lateral interconnections, the neuron layers or parts of these layers may also have the structure of a threshold-controlled completely connected network. Here the thresholds are the potentials delivered through the projection connections from other parts of the network. The extension of the projection algorithm to the class of threshold-controlled networks may accordingly prove to be useful both for extending its technical applications and for better understanding of the operation of the nervous system in living organisms.

  2. Neural aspects of second language representation and language control.

    Science.gov (United States)

    Abutalebi, Jubin

    2008-07-01

    A basic issue in the neurosciences of language is whether an L2 can be processed through the same neural mechanism underlying L1 acquisition and processing. In the present paper I review data from functional neuroimaging studies focusing on grammatical and lexico-semantic processing in bilinguals. The available evidence indicates that the L2 seems to be acquired through the same neural structures responsible for L1 acquisition. This fact is also observed for grammar acquisition in late L2 learners contrary to what one may expect from critical period accounts. However, neural differences for an L2 may be observed, in terms of more extended activity of the neural system mediating L1 processing. These differences may disappear once a more 'native-like' proficiency is established, reflecting a change in language processing mechanisms: from controlled processing for a weak L2 system (i.e., a less proficient L2) to more automatic processing. The neuroimaging data reviewed in this paper also support the notion that language control is a crucial aspect specific to the bilingual language system. The activity of brain areas related to cognitive control during the processing of a 'weak' L2 may reflect competition and conflict between languages which may be resolved with the intervention of these areas.

  3. Neural systems for preparatory control of imitation

    National Research Council Canada - National Science Library

    Cross, Katy A; Iacoboni, Marco

    2014-01-01

    Humans have an automatic tendency to imitate others. Previous studies on how we control these tendencies have focused on reactive mechanisms, where inhibition of imitation is implemented after seeing an action...

  4. Two stage neural network modelling for robust model predictive control.

    Science.gov (United States)

    Patan, Krzysztof

    2017-11-02

    The paper proposes a novel robust model predictive control scheme realized by means of artificial neural networks. The neural networks are used twofold: to design the so-called fundamental model of a plant and to catch uncertainty associated with the plant model. In order to simplify the optimization process carried out within the framework of predictive control an instantaneous linearization is applied which renders it possible to define the optimization problem in the form of constrained quadratic programming. Stability of the proposed control system is also investigated by showing that a cost function is monotonically decreasing with respect to time. Derived robust model predictive control is tested and validated on the example of a pneumatic servomechanism working at different operating regimes. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

  5. Neural Network Predictive Control for Vanadium Redox Flow Battery

    Directory of Open Access Journals (Sweden)

    Hai-Feng Shen

    2013-01-01

    Full Text Available The vanadium redox flow battery (VRB is a nonlinear system with unknown dynamics and disturbances. The flowrate of the electrolyte is an important control mechanism in the operation of a VRB system. Too low or too high flowrate is unfavorable for the safety and performance of VRB. This paper presents a neural network predictive control scheme to enhance the overall performance of the battery. A radial basis function (RBF network is employed to approximate the dynamics of the VRB system. The genetic algorithm (GA is used to obtain the optimum initial values of the RBF network parameters. The gradient descent algorithm is used to optimize the objective function of the predictive controller. Compared with the constant flowrate, the simulation results show that the flowrate optimized by neural network predictive controller can increase the power delivered by the battery during the discharge and decrease the power consumed during the charge.

  6. Comparative Study between Robust Control of Robotic Manipulators by Static and Dynamic Neural Networks

    OpenAIRE

    Ghrab, Nadya; Kallel, Hichem

    2013-01-01

    A comparative study between static and dynamic neural networks for robotic systems control is considered. So, two approaches of neural robot control were selected, exposed, and compared. One uses a static neural network; the other uses a dynamic neural network. Both compensate the nonlinear modeling and uncertainties of robotic systems. The first approach is direct; it approximates the nonlinearities and uncertainties by a static neural network. The second approach is indirect; it uses a dyna...

  7. Earth Station Neural Network Control Methodology and Simulation

    OpenAIRE

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

    2012-01-01

    Renewable energy resources are inexhaustible, clean as compared with conventional resources. Also, it is used to supply regions with no grid, no telephone lines, and often with difficult accessibility by common transport. Satellite earth stations which located in remote areas are the most important application of renewable energy. Neural control is a branch of the general field of intelligent control, which is based on the concept of artificial intelligence. This paper presents the mathematic...

  8. Computation and control with neural nets

    Energy Technology Data Exchange (ETDEWEB)

    Corneliusen, A.; Terdal, P.; Knight, T.; Spencer, J.

    1989-10-04

    As energies have increased exponentially with time so have the size and complexity of accelerators and control systems. NN may offer the kinds of improvements in computation and control that are needed to maintain acceptable functionality. For control their associative characteristics could provide signal conversion or data translation. Because they can do any computation such as least squares, they can close feedback loops autonomously to provide intelligent control at the point of action rather than at a central location that requires transfers, conversions, hand-shaking and other costly repetitions like input protection. Both computation and control can be integrated on a single chip, printed circuit or an optical equivalent that is also inherently faster through full parallel operation. For such reasons one expects lower costs and better results. Such systems could be optimized by integrating sensor and signal processing functions. Distributed nets of such hardware could communicate and provide global monitoring and multiprocessing in various ways e.g. via token, slotted or parallel rings (or Steiner trees) for compatibility with existing systems. Problems and advantages of this approach such as an optimal, real-time Turing machine are discussed. Simple examples are simulated and hardware implemented using discrete elements that demonstrate some basic characteristics of learning and parallelism. Future microprocessors' are predicted and requested on this basis. 19 refs., 18 figs.

  9. Role of neurons and glia in the CNS actions of the renin-angiotensin system in cardiovascular control.

    Science.gov (United States)

    de Kloet, Annette D; Liu, Meng; Rodríguez, Vermalí; Krause, Eric G; Sumners, Colin

    2015-09-01

    Despite tremendous research efforts, hypertension remains an epidemic health concern, leading often to the development of cardiovascular disease. It is well established that in many instances, the brain plays an important role in the onset and progression of hypertension via activation of the sympathetic nervous system. Further, the activity of the renin-angiotensin system (RAS) and of glial cell-mediated proinflammatory processes have independently been linked to this neural control and are, as a consequence, both attractive targets for the development of antihypertensive therapeutics. Although it is clear that the predominant effector peptide of the RAS, ANG II, activates its type-1 receptor on neurons to mediate some of its hypertensive actions, additional nuances of this brain RAS control of blood pressure are constantly being uncovered. One of these complexities is that the RAS is now thought to impact cardiovascular control, in part, via facilitating a glial cell-dependent proinflammatory milieu within cardiovascular control centers. Another complexity is that the newly characterized antihypertensive limbs of the RAS are now recognized to, in many cases, antagonize the prohypertensive ANG II type 1 receptor (AT1R)-mediated effects. That being said, the mechanism by which the RAS, glia, and neurons interact to regulate blood pressure is an active area of ongoing research. Here, we review the current understanding of these interactions and present a hypothetical model of how these exchanges may ultimately regulate cardiovascular function. Copyright © 2015 the American Physiological Society.

  10. Stability analysis of embedded nonlinear predictor neural generalized predictive controller

    Directory of Open Access Journals (Sweden)

    Hesham F. Abdel Ghaffar

    2014-03-01

    Full Text Available Nonlinear Predictor-Neural Generalized Predictive Controller (NGPC is one of the most advanced control techniques that are used with severe nonlinear processes. In this paper, a hybrid solution from NGPC and Internal Model Principle (IMP is implemented to stabilize nonlinear, non-minimum phase, variable dead time processes under high disturbance values over wide range of operation. Also, the superiority of NGPC over linear predictive controllers, like GPC, is proved for severe nonlinear processes over wide range of operation. The necessary conditions required to stabilize NGPC is derived using Lyapunov stability analysis for nonlinear processes. The NGPC stability conditions and improvement in disturbance suppression are verified by both simulation using Duffing’s nonlinear equation and real-time using continuous stirred tank reactor. Up to our knowledge, the paper offers the first hardware embedded Neural GPC which has been utilized to verify NGPC–IMP improvement in realtime.

  11. Piecewise-linear artificial neural networks for PID controller tuning

    Directory of Open Access Journals (Sweden)

    Petr Doležel

    2012-12-01

    Full Text Available A new algorithm of PID controller tuning is presented in this paper. It is well known that there have been introduced manytechniques for PID controller tuning, both theoretical and experimental ones. However, this algorithm is suitable especially forhighly nonlinear processes. It uses a model of the controlled process in the shape of piecewise-linear neural network which islinearized continuously and resulting linearized model is used for PID controller online tuning. While at the beginning of the paperthe algorithm is described in theory, at the end there are mentioned some practical applications

  12. [Integrated approach to prevention and control of cardiovascular diseases].

    Science.gov (United States)

    Rakić, Dušica; Jakovljević, Djordje

    2011-01-01

    From 1984 to 2004, the city of Novi Sad participated in the international MONICA (Multinational MONItoring of trends and determinants in CArdiovascular disease) project, as one of the 38 research centres form 21 countries around the world and in CINDI (Countrywide Integrated Noncommunicable Disease Intervention Programme) programme. Objective was to indicate the advantages of the integrated approach to prevention and control of the cardiovascular disease (CVD) over mono-programmes and still present curative approach. Additional objective was the assessment of the vulnerability from the leading risk factors of the population of Novi Sad, based on the results and experience gained in realization of international projects. We analysed the results obtained in the countries where MONICA project and CINDI programme have been applied. The results of their application in Novi Sad are analysed (the trend of the prevalence of risk factors, the incidence of coronary and cerebrovascular events). The prevalence of risk factors (except smoking), the incidence of coronary and cerebrovascular events are significantly increasing and are in positive correlation with the values of the linear trend.The decrease was only recorded in 1987 (the implementation of the intervention programme). The review of results and experiences in international projects and programs, clearly indicate the advantage of an integrated approach to prevention and control of CVD in relation to monoprograme. The great vulnerability of the population of Novi Sad of the risk factors of CVD points out the necessity of their reductions by the principles of integrated programmes of prevention and control.

  13. Adaptive Gain Scheduled Semiactive Vibration Control Using a Neural Network

    Directory of Open Access Journals (Sweden)

    Kazuhiko Hiramoto

    2018-01-01

    Full Text Available We propose an adaptive gain scheduled semiactive control method using an artificial neural network for structural systems subject to earthquake disturbance. In order to design a semiactive control system with high control performance against earthquakes with different time and/or frequency properties, multiple semiactive control laws with high performance for each of multiple earthquake disturbances are scheduled with an adaptive manner. Each semiactive control law to be scheduled is designed based on the output emulation approach that has been proposed by the authors. As the adaptive gain scheduling mechanism, we introduce an artificial neural network (ANN. Input signals of the ANN are the measured earthquake disturbance itself, for example, the acceleration, velocity, and displacement. The output of the ANN is the parameter for the scheduling of multiple semiactive control laws each of which has been optimized for a single disturbance. Parameters such as weight and bias in the ANN are optimized by the genetic algorithm (GA. The proposed design method is applied to semiactive control design of a base-isolated building with a semiactive damper. With simulation study, the proposed adaptive gain scheduling method realizes control performance exceeding single semiactive control optimizing the average of the control performance subject to various earthquake disturbances.

  14. Income, neural executive processes, and preschool children's executive control.

    Science.gov (United States)

    Ruberry, Erika J; Lengua, Liliana J; Crocker, Leanna Harris; Bruce, Jacqueline; Upshaw, Michaela B; Sommerville, Jessica A

    2017-02-01

    This study aimed to specify the neural mechanisms underlying the link between low household income and diminished executive control in the preschool period. Specifically, we examined whether individual differences in the neural processes associated with executive attention and inhibitory control accounted for income differences observed in performance on a neuropsychological battery of executive control tasks. The study utilized a sample of preschool-aged children (N = 118) whose families represented the full range of income, with 32% of families at/near poverty, 32% lower income, and 36% middle to upper income. Children completed a neuropsychological battery of executive control tasks and then completed two computerized executive control tasks while EEG data were collected. We predicted that differences in the event-related potential (ERP) correlates of executive attention and inhibitory control would account for income differences observed on the executive control battery. Income and ERP measures were related to performance on the executive control battery. However, income was unrelated to ERP measures. The findings suggest that income differences observed in executive control during the preschool period might relate to processes other than executive attention and inhibitory control.

  15. Neural control hierarchy of the heart has not evolved to deal with myocardial ischemia.

    Science.gov (United States)

    Kember, G; Armour, J A; Zamir, M

    2013-08-01

    The consequences of myocardial ischemia are examined from the standpoint of the neural control system of the heart, a hierarchy of three neuronal centers residing in central command, intrathoracic ganglia, and intrinsic cardiac ganglia. The basis of the investigation is the premise that while this hierarchical control system has evolved to deal with "normal" physiological circumstances, its response in the event of myocardial ischemia is unpredictable because the singular circumstances of this event are as yet not part of its evolutionary repertoire. The results indicate that the harmonious relationship between the three levels of control breaks down, because of a conflict between the priorities that they have evolved to deal with. Essentially, while the main priority in central command is blood demand, the priority at the intrathoracic and cardiac levels is heart rate. As a result of this breakdown, heart rate becomes less predictable and therefore less reliable as a diagnostic guide as to the traumatic state of the heart, which it is commonly used as such following an ischemic event. On the basis of these results it is proposed that under the singular conditions of myocardial ischemia a determination of neural control indexes in addition to cardiovascular indexes has the potential of enhancing clinical outcome.

  16. Sympathetic neural and cardiovascular responses during static handgrip exercise in women with a history of hypertensive pregnancy.

    Science.gov (United States)

    Stickford, Abigail S L; Okada, Yoshiyuki; Best, Stuart A; Parker, Rosemary S; Levine, Benjamin D; Fu, Qi

    2016-12-01

    Women with a history of hypertensive pregnancy are at greater risk for future cardiovascular events; however, the mechanisms for this increased risk are unknown. Evidence suggests that an exercise stimulus unmasks latent hypertensive tendencies, identifying individuals at the greatest risk for developing cardiovascular disease. The current study examined the hypothesis that women with a hypertensive pregnancy history exhibit an augmented exercise pressor response. Normotensive women with a history of healthy pregnancy (CON; n = 9) and hypertensive pregnancy (HP+; n = 12) were studied during the mid-luteal phase of the menstrual cycle. Heart rate (HR), systolic and diastolic blood pressure (SBP, DBP), and muscle sympathetic nerve activity (MSNA) were measured during a cold pressor test (CPT), and, following a sufficient period of recovery, during static handgrip to fatigue (SHG) and post-exercise circulatory arrest (PECA). The BP, HR, and MSNA responses to the CPT were similar between groups. The SBP response to SHG and PECA was similar between groups, but DBP and HR were significantly greater in HP+ women (both p cardiovascular disease or hypertension, women with a history of hypertensive pregnancy display an enhanced cardiovascular reactivity to an exercise stimulus compared to women with a healthy pregnancy history. This response may be indicative of impaired cardiovascular control that precedes the clinical manifestation of hypertension or cardiovascular events.

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

    Directory of Open Access Journals (Sweden)

    Bahita Mohamed

    2011-01-01

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

  18. ER fluid applications to vibration control devices and an adaptive neural-net controller

    Science.gov (United States)

    Morishita, Shin; Ura, Tamaki

    1993-07-01

    Four applications of electrorheological (ER) fluid to vibration control actuators and an adaptive neural-net control system suitable for the controller of ER actuators are described: a shock absorber system for automobiles, a squeeze film damper bearing for rotational machines, a dynamic damper for multidegree-of-freedom structures, and a vibration isolator. An adaptive neural-net control system composed of a forward model network for structural identification and a controller network is introduced for the control system of these ER actuators. As an example study of intelligent vibration control systems, an experiment was performed in which the ER dynamic damper was attached to a beam structure and controlled by the present neural-net controller so that the vibration in several modes of the beam was reduced with a single dynamic damper.

  19. Control of cardiovascular variability during undisturbed wake-sleep behavior in hypocretin-deficient mice.

    Science.gov (United States)

    Silvani, Alessandro; Bastianini, Stefano; Berteotti, Chiara; Lo Martire, Viviana; Zoccoli, Giovanna

    2012-04-15

    The central neural mechanisms underlying differences in cardiovascular variability between wakefulness, non-rapid-eye-movement sleep (NREMS), and rapid-eye-movement sleep (REMS) remain poorly understood. These mechanisms may involve hypocretin (HCRT)/orexin signaling. HCRT signaling is linked to wake-sleep states, involved in central autonomic control, and impaired in narcoleptic patients. Thus, we investigated whether HCRT signaling plays a role in controlling cardiovascular variability during spontaneous behavior in HCRT-deficient mice. HCRT-ataxin3 transgenic mice lacking HCRT neurons (TG), knockout mice lacking HCRT peptides (KO), and wild-type controls (WT) were instrumented with electrodes for sleep recordings and a telemetric blood pressure transducer. Fluctuations of systolic blood pressure (SBP) and heart period (HP) during undisturbed wake-sleep behavior were analyzed with the sequence technique, cross-correlation functions, and coherent averaging of SBP surges. During NREMS, all mice had lower SBP variability, greater baroreflex contribution to HP control at low frequencies, and greater amplitude of the central autonomic and baroreflex changes in HP associated with SBP surges than during wakefulness. During REMS, all mice had higher SBP variability and depressed central autonomic and baroreflex HP controls relative to NREMS. HP variability during REMS was higher than during NREMS in WT only. TG and KO also had lower amplitude of the cardiac baroreflex response to SBP surges during REMS than WT. These results indicate that chronic lack of HCRT signaling may cause subtle alterations in the control of HP during spontaneous behavior. Conversely, the integrity of HCRT signaling is not necessary for the occurrence of physiological sleep-dependent changes in SBP variability.

  20. Prediction and control of neural responses to pulsatile electrical stimulation

    Science.gov (United States)

    Campbell, Luke J.; Sly, David James; O'Leary, Stephen John

    2012-04-01

    This paper aims to predict and control the probability of firing of a neuron in response to pulsatile electrical stimulation of the type delivered by neural prostheses such as the cochlear implant, bionic eye or in deep brain stimulation. Using the cochlear implant as a model, we developed an efficient computational model that predicts the responses of auditory nerve fibers to electrical stimulation and evaluated the model's accuracy by comparing the model output with pooled responses from a group of guinea pig auditory nerve fibers. It was found that the model accurately predicted the changes in neural firing probability over time to constant and variable amplitude electrical pulse trains, including speech-derived signals, delivered at rates up to 889 pulses s-1. A simplified version of the model that did not incorporate adaptation was used to adaptively predict, within its limitations, the pulsatile electrical stimulus required to cause a desired response from neurons up to 250 pulses s-1. Future stimulation strategies for cochlear implants and other neural prostheses may be enhanced using similar models that account for the way that neural responses are altered by previous stimulation.

  1. Noise Control for a Moving Evaluation Point Using Neural Networks

    Science.gov (United States)

    Maeda, Toshiki; Shiraishi, Toshihiko

    2016-09-01

    This paper describes the noise control for a moving evaluation point using neural networks by making the best use of its learning ability. Noise control is a technology which is effective on low-frequency noise. Based on the principle of superposition, a primary sound wave can be cancelled at an evaluation point by emitting a secondary opposite sound wave. To obtain good control performance, it is important to precisely identify the characteristics of all the sound paths. One of the most popular algorithms of noise control is filtered-x LMS algorithm. This algorithm can deliver a good result while all the sound paths do not change. However, the control system becomes uncontrollable while the evaluation point is moving. To solve the problem, the characteristics of all the paths are must be identified at all time. In this paper, we applied neural networks with the learning ability to the noise control system to follow the time-varying paths and verified its control performance by numerical simulations. Then, dropout technique for the networks is also applied. Dropout is a technique that prevent the network from overfitting and enables better control performance. By applying dropout for noise control, it prevents the system from diverging.

  2. Biologically Inspired Modular Neural Control for a Leg-Wheel Hybrid Robot

    DEFF Research Database (Denmark)

    Manoonpong, Poramate; Wörgötter, Florentin; Laksanacharoen, Pudit

    2014-01-01

    In this article we present modular neural control for a leg-wheel hybrid robot consisting of three legs with omnidirectional wheels. This neural control has four main modules having their functional origin in biological neural systems. A minimal recurrent control (MRC) module is for sensory signal...... processing and state memorization. Its outputs drive two front wheels while the rear wheel is controlled through a velocity regulating network (VRN) module. In parallel, a neural oscillator network module serves as a central pattern generator (CPG) controls leg movements for sidestepping. Stepping directions...... or they can serve as useful modules for other module-based neural control applications....

  3. Neural Control of Chronic Stress Adaptation

    Directory of Open Access Journals (Sweden)

    James eHerman

    2013-08-01

    Full Text Available Stress initiates adaptive processes that allow the organism to physiologically cope with prolonged or intermittent exposure to real or perceived threats. A major component of this response is repeated activation of glucocorticoid secretion by the hypothalamo-pituitary-adrenocortical (HPA axis, which promotes redistribution of energy in a wide range of organ systems, including the brain. Prolonged or cumulative increases in glucocorticoid secretion can reduce benefits afforded by enhanced stress reactivity and eventually become maladaptive. The long-term impact of stress is kept in check by the process of habituation, which reduces HPA axis responses upon repeated exposure to homotypic stressors and likely limits deleterious actions of prolonged glucocorticoid secretion. Habituation is regulated by limbic stress-regulatory sites, and is at least in part glucocorticoid feedback-dependent. Chronic stress also sensitizes reactivity to new stimuli. While sensitization may be important in maintaining response flexibility in response to new threats, it may also add to the cumulative impact of glucocorticoids on the brain and body. Finally, unpredictable or severe stress exposure may cause long-term and lasting dysregulation of the HPA axis, likely due to altered limbic control of stress effector pathways. Stress-related disorders, such as depression and PTSD, are accompanied by glucocorticoid imbalances and structural/ functional alterations in limbic circuits that resemble those seen following chronic stress, suggesting that inappropriate processing of stressful information may be part of the pathological process.

  4. Diagnostics and control of pressurized reactors using artificial neural networks

    Science.gov (United States)

    Ikonomopoulos, Andreas; Tsoukalas, Lefteri H.; Uhrig, Robert E.

    1992-09-01

    A methodology employing artificial neural networks and fuzzy arithmetic in the diagnosis and control of complex systems such as pressurized water reactors is presented. Fuzzy numbers represent the linguistic values of plant-specific variables, e.g., performance or availability. The notion of a virtual instrument, i.e., a software-based measuring device calibrated to the idiosyncrasies of a specific system is used. Neural networks perform a mapping of physically measurable parameters to fuzzy numbers called Virtual Measurement Values (VMV). The methodology is tested with start-up data from an experimental nuclear reactor. The results demonstrate the very good capacity of such virtual instruments for failure-tolerance and suggest the possibility of developing alternative algorithms for diagnostics and control.

  5. Active Vibration Control of the Smart Plate Using Artificial Neural Network Controller

    Directory of Open Access Journals (Sweden)

    Mohit

    2015-01-01

    Full Text Available The active vibration control (AVC of a rectangular plate with single input and single output approach is investigated using artificial neural network. The cantilever plate of finite length, breadth, and thickness having piezoelectric patches as sensors/actuators fixed at the upper and lower surface of the metal plate is considered for examination. The finite element model of the cantilever plate is utilized to formulate the whole strategy. The compact RIO and MATLAB simulation software are exercised to get the appropriate results. The cantilever plate is subjected to impulse input and uniform white noise disturbance. The neural network is trained offline and tuned with LQR controller. The various training algorithms to tune the neural network are exercised. The best efficient algorithm is finally considered to tune the neural network controller designed for active vibration control of the smart plate.

  6. A model for the neural control of pineal periodicity

    Science.gov (United States)

    de Oliveira Cruz, Frederico Alan; Soares, Marilia Amavel Gomes; Cortez, Celia Martins

    2016-12-01

    The aim of this work was verify if a computational model associating the synchronization dynamics of coupling oscillators to a set of synaptic transmission equations would be able to simulate the control of pineal by a complex neural pathway that connects the retina to this gland. Results from the simulations showed that the frequency and temporal firing patterns were in the range of values found in literature.

  7. Practical Application of Neural Networks in State Space Control

    DEFF Research Database (Denmark)

    Bendtsen, Jan Dimon

    theoretic notions followed by a detailed description of the topology, neuron functions and learning rules of the two types of neural networks treated in the thesis, the multilayer perceptron and the neurofuzzy networks. In both cases, a Least Squares second-order gradient method is used to train....... Then the controller is shown to work on a simulation example. We also address the potential problem of too rapidly fluctuating parameters by including regularization in the learning rule. Next we develop a direct adaptive certainty-equivalence controller based on neurofuzzy models. The control loop is proven...

  8. A biologically inspired neural network controller for ballistic arm movements

    Directory of Open Access Journals (Sweden)

    Schmid Maurizio

    2007-09-01

    Full Text Available Abstract Background In humans, the implementation of multijoint tasks of the arm implies a highly complex integration of sensory information, sensorimotor transformations and motor planning. Computational models can be profitably used to better understand the mechanisms sub-serving motor control, thus providing useful perspectives and investigating different control hypotheses. To this purpose, the use of Artificial Neural Networks has been proposed to represent and interpret the movement of upper limb. In this paper, a neural network approach to the modelling of the motor control of a human arm during planar ballistic movements is presented. Methods The developed system is composed of three main computational blocks: 1 a parallel distributed learning scheme that aims at simulating the internal inverse model in the trajectory formation process; 2 a pulse generator, which is responsible for the creation of muscular synergies; and 3 a limb model based on two joints (two degrees of freedom and six muscle-like actuators, that can accommodate for the biomechanical parameters of the arm. The learning paradigm of the neural controller is based on a pure exploration of the working space with no feedback signal. Kinematics provided by the system have been compared with those obtained in literature from experimental data of humans. Results The model reproduces kinematics of arm movements, with bell-shaped wrist velocity profiles and approximately straight trajectories, and gives rise to the generation of synergies for the execution of movements. The model allows achieving amplitude and direction errors of respectively 0.52 cm and 0.2 radians. Curvature values are similar to those encountered in experimental measures with humans. The neural controller also manages environmental modifications such as the insertion of different force fields acting on the end-effector. Conclusion The proposed system has been shown to properly simulate the development of

  9. Neural control of muscle relaxation in echinoderms.

    Science.gov (United States)

    Elphick, M R; Melarange, R

    2001-03-01

    Smooth muscle relaxation in vertebrates is regulated by a variety of neuronal signalling molecules, including neuropeptides and nitric oxide (NO). The physiology of muscle relaxation in echinoderms is of particular interest because these animals are evolutionarily more closely related to the vertebrates than to the majority of invertebrate phyla. However, whilst in vertebrates there is a clear structural and functional distinction between visceral smooth muscle and skeletal striated muscle, this does not apply to echinoderms, in which the majority of muscles, whether associated with the body wall skeleton and its appendages or with visceral organs, are made up of non-striated fibres. The mechanisms by which the nervous system controls muscle relaxation in echinoderms were, until recently, unknown. Using the cardiac stomach of the starfish Asterias rubens as a model, it has been established that the NO-cGMP signalling pathway mediates relaxation. NO also causes relaxation of sea urchin tube feet, and NO may therefore function as a 'universal' muscle relaxant in echinoderms. The first neuropeptides to be identified in echinoderms were two related peptides isolated from Asterias rubens known as SALMFamide-1 (S1) and SALMFamide-2 (S2). Both S1 and S2 cause relaxation of the starfish cardiac stomach, but with S2 being approximately ten times more potent than S1. SALMFamide neuropeptides have also been isolated from sea cucumbers, in which they cause relaxation of both gut and body wall muscle. Therefore, like NO, SALMFamides may also function as 'universal' muscle relaxants in echinoderms. The mechanisms by which SALMFamides cause relaxation of echinoderm muscle are not known, but several candidate signal transduction pathways are discussed here. The SALMFamides do not, however, appear to act by promoting release of NO, and muscle relaxation in echinoderms is therefore probably regulated by at least two neuronal signalling systems acting in parallel. Recently, other

  10. Real-Time Inverse Optimal Neural Control for Image Based Visual Servoing with Nonholonomic Mobile Robots

    Directory of Open Access Journals (Sweden)

    Carlos López-Franco

    2015-01-01

    Full Text Available We present an inverse optimal neural controller for a nonholonomic mobile robot with parameter uncertainties and unknown external disturbances. The neural controller is based on a discrete-time recurrent high order neural network (RHONN trained with an extended Kalman filter. The reference velocities for the neural controller are obtained with a visual sensor. The effectiveness of the proposed approach is tested by simulations and real-time experiments.

  11. Control strategies for underactuated neural ensembles driven by optogenetic stimulation

    Directory of Open Access Journals (Sweden)

    ShiNung eChing

    2013-04-01

    Full Text Available Motivated by experiments employing optogenetic stimulation of cortical regions, we consider spike control strategies for ensembles of uncoupled integrate and fire neurons with a common conductance input. We construct strategies for control of spike patterns, that is, multineuron trains of action potentials, up to some maximal spike rate determined by the neural biophysics. We emphasize a constructive role for parameter heterogeneity, and find a simple rule for controllability in pairs of neurons. In particular, we determine parameters for which common drive is not limited to inducing synchronous spiking. For large ensembles, we determine how the number of controllable neurons varies with the number of observed (recorded neurons, and what collateral spiking occurs in the full ensemble during control of the subensemble. While complete control of spiking in every neuron is not possible with a single input, we find that a degree of subensemble control is made possible by exploiting dynamical heterogeneity. As most available technologies for neural stimulation are underactuated, in the sense that the number of target neurons far exceeds the number of independent channels of stimulation, these results suggest partial control strategies that may be important in the development of sensory neuroprosthetics and other neurocontrol applications.

  12. Control strategies for underactuated neural ensembles driven by optogenetic stimulation.

    Science.gov (United States)

    Ching, ShiNung; Ritt, Jason T

    2013-01-01

    Motivated by experiments employing optogenetic stimulation of cortical regions, we consider spike control strategies for ensembles of uncoupled integrate and fire neurons with a common conductance input. We construct strategies for control of spike patterns, that is, multineuron trains of action potentials, up to some maximal spike rate determined by the neural biophysics. We emphasize a constructive role for parameter heterogeneity, and find a simple rule for controllability in pairs of neurons. In particular, we determine parameters for which common drive is not limited to inducing synchronous spiking. For large ensembles, we determine how the number of controllable neurons varies with the number of observed (recorded) neurons, and what collateral spiking occurs in the full ensemble during control of the subensemble. While complete control of spiking in every neuron is not possible with a single input, we find that a degree of subensemble control is made possible by exploiting dynamical heterogeneity. As most available technologies for neural stimulation are underactuated, in the sense that the number of target neurons far exceeds the number of independent channels of stimulation, these results suggest partial control strategies that may be important in the development of sensory neuroprosthetics and other neurocontrol applications.

  13. Neural Network Control for a Batch Distillation Column

    Directory of Open Access Journals (Sweden)

    Duraid Fadhil Ahmed

    2016-07-01

    Full Text Available The  present  work  deals  with  studying  the  dynamic  behavior  of  a  batch  distillation  column  and implemented  two  types  of  control  strategies  for  the  separation  different  types  of  binary  systems.  The model  was  derived  and  then  simulated  using  "MATLAB"  program.  The  experimental  data  of  dynamic behavior  were  to  tune  the  parameters  of  PID  controller  and  developed  the  training  of  neural  networks controller by using supervised  learning algorithms. The simulation results show a qualitatively acceptable behavior.  This  study  shows  also  that  the  response  of  PID  controller  was  oscillatory  behavior  with  high offset value while neural network controller gave less offset value and less  time to reach the steady state. In general, a good improvement is achieved when the  neural network controller  is used compared with PID control.

  14. Neural Mobilization: A Systematic Review of Randomized Controlled Trials with an Analysis of Therapeutic Efficacy

    Science.gov (United States)

    Ellis, Richard F.; Hing, Wayne A.

    2008-01-01

    Neural mobilization is a treatment modality used in relation to pathologies of the nervous system. It has been suggested that neural mobilization is an effective treatment modality, although support of this suggestion is primarily anecdotal. The purpose of this paper was to provide a systematic review of the literature pertaining to the therapeutic efficacy of neural mobilization. A search to identify randomized controlled trials investigating neural mobilization was conducted using the key words neural mobilisation/mobilization, nerve mobilisation/mobilization, neural manipulative physical therapy, physical therapy, neural/nerve glide, nerve glide exercises, nerve/neural treatment, nerve/neural stretching, neurodynamics, and nerve/neural physiotherapy. The titles and abstracts of the papers identified were reviewed to select papers specifically detailing neural mobilization as a treatment modality. The PEDro scale, a systematic tool used to critique RCTs and grade methodological quality, was used to assess these trials. Methodological assessment allowed an analysis of research investigating therapeutic efficacy of neural mobilization. Ten randomized clinical trials (discussed in 11 retrieved articles) were identified that discussed the therapeutic effect of neural mobilization. This review highlights the lack in quantity and quality of the available research. Qualitative analysis of these studies revealed that there is only limited evidence to support the use of neural mobilization. Future research needs to re-examine the application of neural mobilization with use of more homogeneous study designs and pathologies; in addition, it should standardize the neural mobilization interventions used in the study. PMID:19119380

  15. Computational models of the neural control of breathing.

    Science.gov (United States)

    Molkov, Yaroslav I; Rubin, Jonathan E; Rybak, Ilya A; Smith, Jeffrey C

    2017-03-01

    The ongoing process of breathing underlies the gas exchange essential for mammalian life. Each respiratory cycle ensues from the activity of rhythmic neural circuits in the brainstem, shaped by various modulatory signals, including mechanoreceptor feedback sensitive to lung inflation and chemoreceptor feedback dependent on gas composition in blood and tissues. This paper reviews a variety of computational models designed to reproduce experimental findings related to the neural control of breathing and generate predictions for future experimental testing. The review starts from the description of the core respiratory network in the brainstem, representing the central pattern generator (CPG) responsible for producing rhythmic respiratory activity, and progresses to encompass additional complexities needed to simulate different metabolic challenges, closed-loop feedback control including the lungs, and interactions between the respiratory and autonomic nervous systems. The integrated models considered in this review share a common framework including a distributed CPG core network responsible for generating the baseline three-phase pattern of rhythmic neural activity underlying normal breathing. WIREs Syst Biol Med 2017, 9:e1371. doi: 10.1002/wsbm.1371 For further resources related to this article, please visit the WIREs website. © 2016 Wiley Periodicals, Inc.

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

    CERN Document Server

    Liu, Jinkun

    2013-01-01

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

  17. Neural correlates of cognitive style and flexible cognitive control.

    Science.gov (United States)

    Shin, Gyeonghee; Kim, Chobok

    2015-06-01

    Human abilities of flexible cognitive control are associated with appropriately regulating the amount of cognitive control required in response to contextual demands. In the context of conflicting situations, for instance, the amount of cognitive control increases according to the level of previously experienced conflict, resulting in optimized performance. We explored whether the amount of cognitive control in conflict resolution was related to individual differences in cognitive style that were determined with the Object-Spatial-Verbal cognitive style questionnaire. In this functional magnetic resonance imaging (fMRI) study, a version of the color-word Stroop task, which evokes conflict between color and verbal components, was employed to explore whether individual preferences for distracting information were related to the increases in neural conflict adaptation in cognitive control network regions. The behavioral data revealed that the more the verbal style was preferred, the greater the conflict adaptation effect was observed, especially when the current trial type was congruent. Consistent with the behavioral data, the imaging results demonstrated increased neural conflict adaptation effects in task-relevant network regions, including the left dorsolateral prefrontal cortex, left fusiform gyrus, and left precuneus, as the preference for verbal style increased. These results provide new evidence that flexible cognitive control is closely associated with individuals' preference of cognitive style. Copyright © 2015 Elsevier Inc. All rights reserved.

  18. Acute exercise adjustments of cardiovascular autonomic control in diabetic rats.

    Science.gov (United States)

    Da Pureza, Demilto Yamagushi; Jorge, Luciana; Sanches, Iris Callado; Irigoyen, Maria-Cláudia; De Souza, Romeu Rodrigues; De Angelis, Kátia

    2012-07-01

    We evaluated the role of cardiovascular autonomic changes in hemodynamics at rest and in response to exercise in streptozotocin-induced diabetic rats. Male Wistar rats were divided into nondiabetic (ND, n = 8) and diabetic (D, n = 8) groups. Arterial pressure signals were recorded in the basal state and after atropine or propranolol injections at rest, during exercise and during recovery. At rest, vagal tonus was reduced in D (37 ± 3 bpm) in comparison with the ND group (61 ± 9 bpm). Heart rate during exercise was lower in D in relation to ND rats associated with reduced vagal withdrawal in the D group. The D rats had an increase in vagal tonus in the recovery period (49 ± 6 bpm). Exercise-induced hemodynamic adjustment impairment in diabetic rats was associated with reduced cardiac vagal control. The vagal dysfunction was attenuated after aerobic exercise, reinforcing the positive role of this approach in the management of cardiovascular risk in diabetics. Copyright © 2012 Wiley Periodicals, Inc.

  19. Neural control of rhythmic arm cycling after stroke

    Science.gov (United States)

    Loadman, Pamela M.; Hundza, Sandra R.

    2012-01-01

    Disordered reflex activity and alterations in the neural control of walking have been observed after stroke. In addition to impairments in leg movement that affect locomotor ability after stroke, significant impairments are also seen in the arms. Altered neural control in the upper limb can often lead to altered tone and spasticity resulting in impaired coordination and flexion contractures. We sought to address the extent to which the neural control of movement is disordered after stroke by examining the modulation pattern of cutaneous reflexes in arm muscles during arm cycling. Twenty-five stroke participants who were at least 6 mo postinfarction and clinically stable, performed rhythmic arm cycling while cutaneous reflexes were evoked with trains (5 × 1.0-ms pulses at 300 Hz) of constant-current electrical stimulation to the superficial radial (SR) nerve at the wrist. Both the more (MA) and less affected (LA) arms were stimulated in separate trials. Bilateral electromyography (EMG) activity was recorded from muscles acting at the shoulder, elbow, and wrist. Analysis was conducted on averaged reflexes in 12 equidistant phases of the movement cycle. Phase-modulated cutaneous reflexes were present, but altered, in both MA and LA arms after stroke. Notably, the pattern was “blunted” in the MA arm in stroke compared with control participants. Differences between stroke and control were progressively more evident moving from shoulder to wrist. The results suggest that a reduced pattern of cutaneous reflex modulation persists during rhythmic arm movement after stroke. The overall implication of this result is that the putative spinal contributions to rhythmic human arm movement remain accessible after stroke, which has translational implications for rehabilitation. PMID:22572949

  20. Translating evidence into policy for cardiovascular disease control in India

    Directory of Open Access Journals (Sweden)

    Joshi Rajnish

    2011-02-01

    Full Text Available Abstract Cardiovascular diseases (CVD are leading causes of premature mortality in India. Evidence from developed countries shows that mortality from these can be substantially prevented using population-wide and individual-based strategies. Policy initiatives for control of CVD in India have been suggested but evidence of efficacy has emerged only recently. These initiatives can have immediate impact in reducing morbidity and mortality. Of the prevention strategies, primordial involve improvement in socioeconomic status and literacy, adequate healthcare financing and public health insurance, effective national CVD control programme, smoking control policies, legislative control of saturated fats, trans fats, salt and alcohol, and development of facilities for increasing physical activity through better urban planning and school-based and worksite interventions. Primary prevention entails change in medical educational curriculum and improved healthcare delivery for control of CVD risk factors-smoking, hypertension, dyslipidemia and diabetes. Secondary prevention involves creation of facilities and human resources for optimum acute CVD care and secondary prevention. There is need to integrate various policy makers, develop effective policies and modify healthcare systems for effective delivery of CVD preventive care.

  1. Statistical process control using optimized neural networks: a case study.

    Science.gov (United States)

    Addeh, Jalil; Ebrahimzadeh, Ata; Azarbad, Milad; Ranaee, Vahid

    2014-09-01

    The most common statistical process control (SPC) tools employed for monitoring process changes are control charts. A control chart demonstrates that the process has altered by generating an out-of-control signal. This study investigates the design of an accurate system for the control chart patterns (CCPs) recognition in two aspects. First, an efficient system is introduced that includes two main modules: feature extraction module and classifier module. In the feature extraction module, a proper set of shape features and statistical feature are proposed as the efficient characteristics of the patterns. In the classifier module, several neural networks, such as multilayer perceptron, probabilistic neural network and radial basis function are investigated. Based on an experimental study, the best classifier is chosen in order to recognize the CCPs. Second, a hybrid heuristic recognition system is introduced based on cuckoo optimization algorithm (COA) algorithm to improve the generalization performance of the classifier. The simulation results show that the proposed algorithm has high recognition accuracy. Copyright © 2013 ISA. Published by Elsevier Ltd. All rights reserved.

  2. Direct Adaptive Aircraft Control Using Dynamic Cell Structure Neural Networks

    Science.gov (United States)

    Jorgensen, Charles C.

    1997-01-01

    A Dynamic Cell Structure (DCS) Neural Network was developed which learns topology representing networks (TRNS) of F-15 aircraft aerodynamic stability and control derivatives. The network is integrated into a direct adaptive tracking controller. The combination produces a robust adaptive architecture capable of handling multiple accident and off- nominal flight scenarios. This paper describes the DCS network and modifications to the parameter estimation procedure. The work represents one step towards an integrated real-time reconfiguration control architecture for rapid prototyping of new aircraft designs. Performance was evaluated using three off-line benchmarks and on-line nonlinear Virtual Reality simulation. Flight control was evaluated under scenarios including differential stabilator lock, soft sensor failure, control and stability derivative variations, and air turbulence.

  3. Intelligent control based on fuzzy logic and neural net theory

    Science.gov (United States)

    Lee, Chuen-Chien

    1991-01-01

    In the conception and design of intelligent systems, one promising direction involves the use of fuzzy logic and neural network theory to enhance such systems' capability to learn from experience and adapt to changes in an environment of uncertainty and imprecision. Here, an intelligent control scheme is explored by integrating these multidisciplinary techniques. A self-learning system is proposed as an intelligent controller for dynamical processes, employing a control policy which evolves and improves automatically. One key component of the intelligent system is a fuzzy logic-based system which emulates human decision making behavior. It is shown that the system can solve a fairly difficult control learning problem. Simulation results demonstrate that improved learning performance can be achieved in relation to previously described systems employing bang-bang control. The proposed system is relatively insensitive to variations in the parameters of the system environment.

  4. Application of neural models as controllers in mobile robot velocity control loop

    Science.gov (United States)

    Cerkala, Jakub; Jadlovska, Anna

    2017-01-01

    This paper presents the application of an inverse neural models used as controllers in comparison to classical PI controllers for velocity tracking control task used in two-wheel, differentially driven mobile robot. The PI controller synthesis is based on linear approximation of actuators with equivalent load. In order to obtain relevant datasets for training of feed-forward multi-layer perceptron based neural network used as neural model, the mathematical model of mobile robot, that combines its kinematic and dynamic properties such as chassis dimensions, center of gravity offset, friction and actuator parameters is used. Neural models are trained off-line to act as an inverse dynamics of DC motors with particular load using data collected in simulation experiment for motor input voltage step changes within bounded operating area. The performances of PI controllers versus inverse neural models in mobile robot internal velocity control loops are demonstrated and compared in simulation experiment of navigation control task for line segment motion in plane.

  5. Control of 12-Cylinder Camless Engine with Neural Networks

    Directory of Open Access Journals (Sweden)

    Ashhab Moh’d Sami

    2017-01-01

    Full Text Available The 12-cyliner camless engine breathing process is modeled with artificial neural networks (ANN’s. The inputs to the net are the intake valve lift (IVL and intake valve closing timing (IVC whereas the output of the net is the cylinder air charge (CAC. The ANN is trained with data collected from an engine simulation model which is based on thermodynamics principles and calibrated against real engine data. A method for adapting single-output feed-forward neural networks is proposed and applied to the camless engine ANN model. As a consequence the overall 12-cyliner camless engine feedback controller is upgraded and the necessary changes are implemented in order to contain the adaptive neural network with the objective of tracking the cylinder air charge (driver’s torque demand while minimizing the pumping losses (increasing engine efficiency. All the needed measurements are extracted only from the two conventional and inexpensive sensors, namely, the mass air flow through the throttle body (MAF and the intake manifold absolute pressure (MAP sensors. The feedback controller’s capability is demonstrated through computer simulation.

  6. Adaptive PID control based on orthogonal endocrine neural networks.

    Science.gov (United States)

    Milovanović, Miroslav B; Antić, Dragan S; Milojković, Marko T; Nikolić, Saša S; Perić, Staniša Lj; Spasić, Miodrag D

    2016-12-01

    A new intelligent hybrid structure used for online tuning of a PID controller is proposed in this paper. The structure is based on two adaptive neural networks, both with built-in Chebyshev orthogonal polynomials. First substructure network is a regular orthogonal neural network with implemented artificial endocrine factor (OENN), in the form of environmental stimuli, to its weights. It is used for approximation of control signals and for processing system deviation/disturbance signals which are introduced in the form of environmental stimuli. The output values of OENN are used to calculate artificial environmental stimuli (AES), which represent required adaptation measure of a second network-orthogonal endocrine adaptive neuro-fuzzy inference system (OEANFIS). OEANFIS is used to process control, output and error signals of a system and to generate adjustable values of proportional, derivative, and integral parameters, used for online tuning of a PID controller. The developed structure is experimentally tested on a laboratory model of the 3D crane system in terms of analysing tracking performances and deviation signals (error signals) of a payload. OENN-OEANFIS performances are compared with traditional PID and 6 intelligent PID type controllers. Tracking performance comparisons (in transient and steady-state period) showed that the proposed adaptive controller possesses performances within the range of other tested controllers. The main contribution of OENN-OEANFIS structure is significant minimization of deviation signals (17%-79%) compared to other controllers. It is recommended to exploit it when dealing with a highly nonlinear system which operates in the presence of undesirable disturbances. Copyright © 2016 Elsevier Ltd. All rights reserved.

  7. Cardiovascular control during exercise in type 2 diabetes mellitus.

    Science.gov (United States)

    Green, Simon; Egaña, Mikel; Baldi, J Chris; Lamberts, Regis; Regensteiner, Judith G

    2015-01-01

    Controlled studies of male and female subjects with type 2 diabetes mellitus (DM) of short duration (~3-5 years) show that DM reduces peak VO2 (L·min(-1) and mL·kg(-1)·min(-1)) by an average of 12-15% and induces a greater slowing of the dynamic response of pulmonary VO2 during submaximal exercise. These effects occur in individuals less than 60 years of age but are reduced or absent in older males and are consistently associated with significant increases in the exercise pressor response despite normal resting blood pressure. This exaggerated pressor response, evidence of exertional hypertension in DM, is manifest during moderate submaximal exercise and coincides with a more constrained vasodilation in contracting muscles. Maximum vasodilation during contractions involving single muscle groups is reduced by DM, and the dynamic response of vasodilation during submaximal contractions is slowed. Such vascular constraint most likely contributes to exertional hypertension, impairs dynamic and peak VO2 responses, and reduces exercise tolerance. There is a need to establish the effect of DM on dynamic aspects of vascular control in skeletal muscle during whole-body exercise and to clarify contributions of altered cardiovascular control and increased arterial stiffness to exertional hypertension.

  8. Lifelong bilingualism maintains neural efficiency for cognitive control in aging.

    Science.gov (United States)

    Gold, Brian T; Kim, Chobok; Johnson, Nathan F; Kryscio, Richard J; Smith, Charles D

    2013-01-09

    Recent behavioral data have shown that lifelong bilingualism can maintain youthful cognitive control abilities in aging. Here, we provide the first direct evidence of a neural basis for the bilingual cognitive control boost in aging. Two experiments were conducted, using a perceptual task-switching paradigm, including a total of 110 participants. In Experiment 1, older adult bilinguals showed better perceptual switching performance than their monolingual peers. In Experiment 2, younger and older adult monolinguals and bilinguals completed the same perceptual task-switching experiment while functional magnetic resonance imaging (fMRI) was performed. Typical age-related performance reductions and fMRI activation increases were observed. However, like younger adults, bilingual older adults outperformed their monolingual peers while displaying decreased activation in left lateral frontal cortex and cingulate cortex. Critically, this attenuation of age-related over-recruitment associated with bilingualism was directly correlated with better task-switching performance. In addition, the lower blood oxygenation level-dependent response in frontal regions accounted for 82% of the variance in the bilingual task-switching reaction time advantage. These results suggest that lifelong bilingualism offsets age-related declines in the neural efficiency for cognitive control processes.

  9. Analog neural network control method proposed for use in a backup satellite control mode

    Energy Technology Data Exchange (ETDEWEB)

    Frigo, J.R.; Tilden, M.W.

    1998-03-01

    The authors propose to use an analog neural network controller implemented in hardware, independent of the active control system, for use in a satellite backup control mode. The controller uses coarse sun sensor inputs. The field of view of the sensors activate the neural controller, creating an analog dead band with respect to the direction of the sun on each axis. This network controls the orientation of the vehicle toward the sunlight to ensure adequate power for the system. The attitude of the spacecraft is stabilized with respect to the ambient magnetic field on orbit. This paper develops a model of the controller using real-time coarse sun sensor data and a dynamic model of a prototype system based on a satellite system. The simulation results and the feasibility of this control method for use in a satellite backup control mode are discussed.

  10. Neural network output feedback control of robot formations.

    Science.gov (United States)

    Dierks, Travis; Jagannathan, Sarangapani

    2010-04-01

    In this paper, a combined kinematic/torque output feedback control law is developed for leader-follower-based formation control using backstepping to accommodate the dynamics of the robots and the formation in contrast with kinematic-based formation controllers. A neural network (NN) is introduced to approximate the dynamics of the follower and its leader using online weight tuning. Furthermore, a novel NN observer is designed to estimate the linear and angular velocities of both the follower robot and its leader. It is shown, by using the Lyapunov theory, that the errors for the entire formation are uniformly ultimately bounded while relaxing the separation principle. In addition, the stability of the formation in the presence of obstacles, is examined using Lyapunov methods, and by treating other robots in the formation as obstacles, collisions within the formation are prevented. Numerical results are provided to verify the theoretical conjectures.

  11. Complexity Variability Assessment of Nonlinear Time-Varying Cardiovascular Control

    Science.gov (United States)

    Valenza, Gaetano; Citi, Luca; Garcia, Ronald G.; Taylor, Jessica Noggle; Toschi, Nicola; Barbieri, Riccardo

    2017-02-01

    The application of complex systems theory to physiology and medicine has provided meaningful information about the nonlinear aspects underlying the dynamics of a wide range of biological processes and their disease-related aberrations. However, no studies have investigated whether meaningful information can be extracted by quantifying second-order moments of time-varying cardiovascular complexity. To this extent, we introduce a novel mathematical framework termed complexity variability, in which the variance of instantaneous Lyapunov spectra estimated over time serves as a reference quantifier. We apply the proposed methodology to four exemplary studies involving disorders which stem from cardiology, neurology and psychiatry: Congestive Heart Failure (CHF), Major Depression Disorder (MDD), Parkinson’s Disease (PD), and Post-Traumatic Stress Disorder (PTSD) patients with insomnia under a yoga training regime. We show that complexity assessments derived from simple time-averaging are not able to discern pathology-related changes in autonomic control, and we demonstrate that between-group differences in measures of complexity variability are consistent across pathologies. Pathological states such as CHF, MDD, and PD are associated with an increased complexity variability when compared to healthy controls, whereas wellbeing derived from yoga in PTSD is associated with lower time-variance of complexity.

  12. Improved methods in neural network-based adaptive output feedback control, with applications to flight control

    Science.gov (United States)

    Kim, Nakwan

    Utilizing the universal approximation property of neural networks, we develop several novel approaches to neural network-based adaptive output feedback control of nonlinear systems, and illustrate these approaches for several flight control applications. In particular, we address the problem of non-affine systems and eliminate the fixed point assumption present in earlier work. All of the stability proofs are carried out in a form that eliminates an algebraic loop in the neural network implementation. An approximate input/output feedback linearizing controller is augmented with a neural network using input/output sequences of the uncertain system. These approaches permit adaptation to both parametric uncertainty and unmodeled dynamics. All physical systems also have control position and rate limits, which may either deteriorate performance or cause instability for a sufficiently high control bandwidth. Here we apply a method for protecting an adaptive process from the effects of input saturation and time delays, known as "pseudo control hedging". This method was originally developed for the state feedback case, and we provide a stability analysis that extends its domain of applicability to the case of output feedback. The approach is illustrated by the design of a pitch-attitude flight control system for a linearized model of an R-50 experimental helicopter, and by the design of a pitch-rate control system for a 58-state model of a flexible aircraft consisting of rigid body dynamics coupled with actuator and flexible modes. A new approach to augmentation of an existing linear controller is introduced. It is especially useful when there is limited information concerning the plant model, and the existing controller. The approach is applied to the design of an adaptive autopilot for a guided munition. Design of a neural network adaptive control that ensures asymptotically stable tracking performance is also addressed.

  13. Cardiovascular reactivity in Black and White siblings versus matched controls.

    Science.gov (United States)

    Wilson, D K; Holmes, S D; Arheart, K; Alpert, B S

    1995-09-01

    Elevated cardiovascular (CV) reactivity may be a marker or mechanism for the early development of essential hypertension (EH) and may contribute to the greater prevalence of EH observed in Black adults. Previous research has demonstrated that Black children show greater CV reactivity than White children to psychological stressors, however, the role of heritability in understanding these racial differences is still unknown. Evidence which supports a genetic influence on CV reactivity comes from animal studies, research on family history of EH, and from twin and sibling studies. The present study expands on previous findings by examining racial differences in CV reactivity in 15 pairs of Black siblings, 15 pairs of age-and sex-matched unrelated Black control subjects, 17 pairs of White siblings, and 17 pairs of age-and sex-matched unrelated White control subjects. Systolic blood pressure (SBP), diastolic blood pressure (DBP), and heart rate (HR) measurements were obtained at rest and during a stress task (competitive video game). Black siblings demonstrated a significantly higher intraclass correlation for DBP reactivity than Black controls or White siblings (r=0.73, versus 0.16, 0.14, respectively). Additionally, Black siblings demonstrated a steeper rise and then a plateau in DBP and HR reactivity to the video game task, while White siblings showed a more gradual increase in these measures over the course of playing three video games. The results for DBP and HR reactivity, however, were not consistent among either of the matched control groups. These results expand on previous research by suggesting a stronger genetic influence of CV reactivity in Black than in White children.

  14. Geometrical approach to neural net control of movements and posture

    Science.gov (United States)

    Pellionisz, A. J.; Ramos, C. F.

    1993-01-01

    In one approach to modeling brain function, sensorimotor integration is described as geometrical mapping among coordinates of non-orthogonal frames that are intrinsic to the system; in such a case sensors represent (covariant) afferents and motor effectors represent (contravariant) motor efferents. The neuronal networks that perform such a function are viewed as general tensor transformations among different expressions and metric tensors determining the geometry of neural functional spaces. Although the non-orthogonality of a coordinate system does not impose a specific geometry on the space, this "Tensor Network Theory of brain function" allows for the possibility that the geometry is non-Euclidean. It is suggested that investigation of the non-Euclidean nature of the geometry is the key to understanding brain function and to interpreting neuronal network function. This paper outlines three contemporary applications of such a theoretical modeling approach. The first is the analysis and interpretation of multi-electrode recordings. The internal geometries of neural networks controlling external behavior of the skeletomuscle system is experimentally determinable using such multi-unit recordings. The second application of this geometrical approach to brain theory is modeling the control of posture and movement. A preliminary simulation study has been conducted with the aim of understanding the control of balance in a standing human. The model appears to unify postural control strategies that have previously been considered to be independent of each other. Third, this paper emphasizes the importance of the geometrical approach for the design and fabrication of neurocomputers that could be used in functional neuromuscular stimulation (FNS) for replacing lost motor control.

  15. Web based educational tool for neural network robot control

    Directory of Open Access Journals (Sweden)

    Jure Čas

    2007-05-01

    Full Text Available Abstract— This paper describes the application for teleoperations of the SCARA robot via the internet. The SCARA robot is used by students of mehatronics at the University of Maribor as a remote educational tool. The developed software consists of two parts i.e. the continuous neural network sliding mode controller (CNNSMC and the graphical user interface (GUI. Application is based on two well-known commercially available software packages i.e. MATLAB/Simulink and LabVIEW. Matlab/Simulink and the DSP2 Library for Simulink are used for control algorithm development, simulation and executable code generation. While this code is executing on the DSP-2 Roby controller and through the analog and digital I/O lines drives the real process, LabVIEW virtual instrument (VI, running on the PC, is used as a user front end. LabVIEW VI provides the ability for on-line parameter tuning, signal monitoring, on-line analysis and via Remote Panels technology also teleoperation. The main advantage of a CNNSMC is the exploitation of its self-learning capability. When friction or an unexpected impediment occurs for example, the user of a remote application has no information about any changed robot dynamic and thus is unable to dispatch it manually. This is not a control problem anymore because, when a CNNSMC is used, any approximation of changed robot dynamic is estimated independently of the remote’s user. Index Terms—LabVIEW; Matlab/Simulink; Neural network control; remote educational tool; robotics

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

    Directory of Open Access Journals (Sweden)

    Ruliang Wang

    2012-01-01

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

  17. Output feedback control of a quadrotor UAV using neural networks.

    Science.gov (United States)

    Dierks, Travis; Jagannathan, Sarangapani

    2010-01-01

    In this paper, a new nonlinear controller for a quadrotor unmanned aerial vehicle (UAV) is proposed using neural networks (NNs) and output feedback. The assumption on the availability of UAV dynamics is not always practical, especially in an outdoor environment. Therefore, in this work, an NN is introduced to learn the complete dynamics of the UAV online, including uncertain nonlinear terms like aerodynamic friction and blade flapping. Although a quadrotor UAV is underactuated, a novel NN virtual control input scheme is proposed which allows all six degrees of freedom (DOF) of the UAV to be controlled using only four control inputs. Furthermore, an NN observer is introduced to estimate the translational and angular velocities of the UAV, and an output feedback control law is developed in which only the position and the attitude of the UAV are considered measurable. It is shown using Lyapunov theory that the position, orientation, and velocity tracking errors, the virtual control and observer estimation errors, and the NN weight estimation errors for each NN are all semiglobally uniformly ultimately bounded (SGUUB) in the presence of bounded disturbances and NN functional reconstruction errors while simultaneously relaxing the separation principle. The effectiveness of proposed output feedback control scheme is then demonstrated in the presence of unknown nonlinear dynamics and disturbances, and simulation results are included to demonstrate the theoretical conjecture.

  18. Central neural control of thermoregulation and brown adipose tissue.

    Science.gov (United States)

    Morrison, Shaun F

    2016-04-01

    Central neural circuits orchestrate the homeostatic repertoire that maintains body temperature during environmental temperature challenges and alters body temperature during the inflammatory response. This review summarizes the experimental underpinnings of our current model of the CNS pathways controlling the principal thermoeffectors for body temperature regulation: cutaneous vasoconstriction controlling heat loss, and shivering and brown adipose tissue for thermogenesis. The activation of these effectors is regulated by parallel but distinct, effector-specific, core efferent pathways within the CNS that share a common peripheral thermal sensory input. Via the lateral parabrachial nucleus, skin thermal afferent input reaches the hypothalamic preoptic area to inhibit warm-sensitive, inhibitory output neurons which control heat production by inhibiting thermogenesis-promoting neurons in the dorsomedial hypothalamus that project to thermogenesis-controlling premotor neurons in the rostral ventromedial medulla, including the raphe pallidus, that descend to provide the excitation of spinal circuits necessary to drive thermogenic thermal effectors. A distinct population of warm-sensitive preoptic neurons controls heat loss through an inhibitory input to raphe pallidus sympathetic premotor neurons controlling cutaneous vasoconstriction. The model proposed for central thermoregulatory control provides a useful platform for further understanding of the functional organization of central thermoregulation and elucidating the hypothalamic circuitry and neurotransmitters involved in body temperature regulation. Copyright © 2016 Elsevier B.V. All rights reserved.

  19. Application of Fuzzy-Logic Controller and Neural Networks Controller in Gas Turbine Speed Control and Overheating Control and Surge Control on Transient Performance

    Science.gov (United States)

    Torghabeh, A. A.; Tousi, A. M.

    2007-08-01

    This paper presents Fuzzy Logic and Neural Networks approach to Gas Turbine Fuel schedules. Modeling of non-linear system using feed forward artificial Neural Networks using data generated by a simulated gas turbine program is introduced. Two artificial Neural Networks are used , depicting the non-linear relationship between gas generator speed and fuel flow, and turbine inlet temperature and fuel flow respectively . Off-line fast simulations are used for engine controller design for turbojet engine based on repeated simulation. The Mamdani and Sugeno models are used to expression the Fuzzy system . The linguistic Fuzzy rules and membership functions are presents and a Fuzzy controller will be proposed to provide an Open-Loop control for the gas turbine engine during acceleration and deceleration . MATLAB Simulink was used to apply the Fuzzy Logic and Neural Networks analysis. Both systems were able to approximate functions characterizing the acceleration and deceleration schedules . Surge and Flame-out avoidance during acceleration and deceleration phases are then checked . Turbine Inlet Temperature also checked and controls by Neural Networks controller. This Fuzzy Logic and Neural Network Controllers output results are validated and evaluated by GSP software . The validation results are used to evaluate the generalization ability of these artificial Neural Networks and Fuzzy Logic controllers.

  20. Neural time course of emotional conflict control: an ERP study.

    Science.gov (United States)

    Shen, Yimo; Xue, Song; Wang, Kangcheng; Qiu, Jiang

    2013-04-29

    Previous imaging studies have revealed brain mechanisms associated with emotional conflict control. However, the neural time course remains largely unknown. Therefore, in the present study a face-word Stroop task was used to explore the electrophysiological correlates of emotional conflict control by using event-related potentials (ERPs). Behavioral data indicated that response time of congruent condition was faster than incongruent condition, while the accuracy rates of congruent condition was higher than incongruent condition, which showed a robust emotional conflict effect. ERP revealed N350-550 and P700-800 components in the incongruent minus congruent condition. N350-550 might be related to conflict resolution and response selection; P700-800 might be related to post-response monitoring. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

  1. Neural network based adaptive control for nonlinear dynamic regimes

    Science.gov (United States)

    Shin, Yoonghyun

    Adaptive control designs using neural networks (NNs) based on dynamic inversion are investigated for aerospace vehicles which are operated at highly nonlinear dynamic regimes. NNs play a key role as the principal element of adaptation to approximately cancel the effect of inversion error, which subsequently improves robustness to parametric uncertainty and unmodeled dynamics in nonlinear regimes. An adaptive control scheme previously named 'composite model reference adaptive control' is further developed so that it can be applied to multi-input multi-output output feedback dynamic inversion. It can have adaptive elements in both the dynamic compensator (linear controller) part and/or in the conventional adaptive controller part, also utilizing state estimation information for NN adaptation. This methodology has more flexibility and thus hopefully greater potential than conventional adaptive designs for adaptive flight control in highly nonlinear flight regimes. The stability of the control system is proved through Lyapunov theorems, and validated with simulations. The control designs in this thesis also include the use of 'pseudo-control hedging' techniques which are introduced to prevent the NNs from attempting to adapt to various actuation nonlinearities such as actuator position and rate saturations. Control allocation is introduced for the case of redundant control effectors including thrust vectoring nozzles. A thorough comparison study of conventional and NN-based adaptive designs for a system under a limit cycle, wing-rock, is included in this research, and the NN-based adaptive control designs demonstrate their performances for two highly maneuverable aerial vehicles, NASA F-15 ACTIVE and FQM-117B unmanned aerial vehicle (UAV), operated under various nonlinearities and uncertainties.

  2. A High-Performance Neural Prosthesis Enabled by Control Algorithm Design

    Science.gov (United States)

    Gilja, Vikash; Nuyujukian, Paul; Chestek, Cindy A.; Cunningham, John P.; Yu, Byron M.; Fan, Joline M.; Churchland, Mark M.; Kaufman, Matthew T.; Kao, Jonathan C.; Ryu, Stephen I.; Shenoy, Krishna V.

    2012-01-01

    Neural prostheses translate neural activity from the brain into control signals for guiding prosthetic devices, such as computer cursors and robotic limbs, and thus offer disabled patients greater interaction with the world. However, relatively low performance remains a critical barrier to successful clinical translation; current neural prostheses are considerably slower with less accurate control than the native arm. Here we present a new control algorithm, the recalibrated feedback intention-trained Kalman filter (ReFIT-KF), that incorporates assumptions about the nature of closed loop neural prosthetic control. When tested with rhesus monkeys implanted with motor cortical electrode arrays, the ReFIT-KF algorithm outperforms existing neural prostheses in all measured domains and halves acquisition time. This control algorithm permits sustained uninterrupted use for hours and generalizes to more challenging tasks without retraining. Using this algorithm, we demonstrate repeatable high performance for years after implantation across two monkeys, thereby increasing the clinical viability of neural prostheses. PMID:23160043

  3. Sufficient Condition for the Existence of the Compact Set in the RBF Neural Network Control.

    Science.gov (United States)

    Zhu, Jiaming; Cao, Zhiqiang; Zhang, Tianping; Yang, Yuequan; Yi, Yang

    2017-06-20

    In this brief, sufficient conditions are proposed for the existence of the compact sets in the neural network controls. First, we point out that the existence of the compact set in a classical neural network control scheme is unsolved and its result is incomplete. Next, as a simple case, we derive the sufficient condition of the existence of the compact set for the neural network control of first-order systems. Finally, we propose the sufficient condition of the existence of the compact set for the neural-network-based backstepping control of high-order nonlinear systems. The theoretic result is illustrated through a simulation example.

  4. SECONDARY VOLTAGE CONTROL BASED ON ADAPTIVE NEURAL PI CONTROLLERS

    Directory of Open Access Journals (Sweden)

    RUBEN TAPIA

    2010-01-01

    Full Text Available Este trabajo tiene como objetivo presentar el desempeño de un controlador basado en redes neuronales Bspline que regula el aporte de potencia reactiva de las máquinas síncronas. Debido a que los sistemas de potencia operan con parámetros no estacionarios y configuración cambiante, es preferible utilizar esquemas de control adaptativos. La tecnología de control debe asegurar su desempeño en términos de condiciones operativas prácticas de los sistemas de potencia, que considere la diversidad de cargas conectadas a la red y maximice la disponibilidad de recursos. La red neuronal Bspline es una herramienta conveniente para implementar el control adaptativo de voltaje, con la posibilidad de llevar a cabo ésta tarea enlínea considerando las no linealidades del sistema. El despacho de potencia reactiva se basa en la premisa de que cada máquina debe aportar en proporción a su capacidad nominal de operación. La aplicabilidad de la propuesta se demuestra mediante simulación en un sistema de potencia multimáquinas.

  5. Control of neural stem cell survival by electroactive polymer substrates.

    Directory of Open Access Journals (Sweden)

    Vanessa Lundin

    Full Text Available Stem cell function is regulated by intrinsic as well as microenvironmental factors, including chemical and mechanical signals. Conducting polymer-based cell culture substrates provide a powerful tool to control both chemical and physical stimuli sensed by stem cells. Here we show that polypyrrole (PPy, a commonly used conducting polymer, can be tailored to modulate survival and maintenance of rat fetal neural stem cells (NSCs. NSCs cultured on PPy substrates containing different counter ions, dodecylbenzenesulfonate (DBS, tosylate (TsO, perchlorate (ClO(4 and chloride (Cl, showed a distinct correlation between PPy counter ion and cell viability. Specifically, NSC viability was high on PPy(DBS but low on PPy containing TsO, ClO(4 and Cl. On PPy(DBS, NSC proliferation and differentiation was comparable to standard NSC culture on tissue culture polystyrene. Electrical reduction of PPy(DBS created a switch for neural stem cell viability, with widespread cell death upon polymer reduction. Coating the PPy(DBS films with a gel layer composed of a basement membrane matrix efficiently prevented loss of cell viability upon polymer reduction. Here we have defined conditions for the biocompatibility of PPy substrates with NSC culture, critical for the development of devices based on conducting polymers interfacing with NSCs.

  6. Neural control of daily and seasonal timing of songbird migration.

    Science.gov (United States)

    Stevenson, Tyler J; Kumar, Vinod

    2017-07-01

    Bird migration is one of most salient annual events in nature. It involves predictable seasonal movements between breeding and non-breeding habitats. Both circadian and circannual clocks are entrained by photoperiodic cues and time daily and seasonal changes in migratory physiology and behavior. This mini-review provides an update on daily and seasonal rhythms of migratory behavior, and examines the neuroendocrine and molecular pathways involved in the timing of migration in songbirds. Recent findings have identified key neural substrates, and suggest the involvement of multiple neuroendocrine regulatory systems in controlling seasonal states in migrants. We propose that four distinct neural substrates are involved in the timing of migration and include (1) pineal gland and suprachiasmatic nucleus (mSCN); (2) a cluster of hypothalamic nuclei, the mediobasal hypothalamus (MBH); (3) dorsomedial hypothalamic nucleus (DMH); and (4) tanycytes along ependymal layer of the 3rd ventricle (3V). Cluster N, a nucleus in the telencephalon involved in the integration of geomagnetic cues, likely maintains functional connectivity with brain regions involved in timing songbird migration. These nuclei form an interconnected network that coordinates daily timing (pineal gland/mSCN), annual photoperiodic response (MBH, 3V), energetic state (MBH, DMH, 3V), and magnetic compass information (i.e., cluster N) for migration in songbirds.

  7. Neural contributions to the motivational control of appetite in humans.

    Science.gov (United States)

    Hinton, Elanor C; Parkinson, John A; Holland, Anthony J; Arana, F Sergio; Roberts, Angela C; Owen, Adrian M

    2004-09-01

    The motivation to eat in humans is a complex process influenced by intrinsic mechanisms relating to the hunger and satiety cascade, and extrinsic mechanisms based on the appetitive incentive value of individual foods, which can themselves induce desire. This study was designed to investigate the neural basis of these two factors contributing to the control of motivation to eat within the same experimental design using positron emission tomography. Using a novel counterbalanced approach, participants were scanned in two separate sessions, once after fasting and once after food intake, in which they imagined themselves in a restaurant and considered a number of items on a menu, and were asked to choose their most preferred. All items were tailored to each individual and varied in their incentive value. No actual foods were presented. In response to a hungry state, increased activation was shown in the hypothalamus, amygdala and insula cortex as predicted, as well as the medulla, striatum and anterior cingulate cortex. Satiety, in contrast, was associated with increased activation in the lateral orbitofrontal and temporal cortex. Only activity in the vicinity of the amygdala and orbitofrontal cortex was observed in response to the processing of extrinsic appetitive incentive information. These results suggest that the contributions of intrinsic homeostatic influences, and extrinsic incentive factors to the motivation to eat, are somewhat dissociable neurally, with areas of convergence in the amygdala and orbitofrontal cortex. The findings of this study have implications for research into the underlying mechanisms of eating disorders.

  8. Learning sequential control in a Neural Blackboard Architecture for in situ concept reasoning

    NARCIS (Netherlands)

    van der Velde, Frank; van der Velde, Frank; Besold, Tarek R.; Lamb, Luis; Serafini, Luciano; Tabor, Whitney

    2016-01-01

    Simulations are presented and discussed of learning sequential control in a Neural Blackboard Architecture (NBA) for in situ concept-based reasoning. Sequential control is learned in a reservoir network, consisting of columns with neural circuits. This allows the reservoir to control the dynamics of

  9. Neural Control of Rising and Falling Tones in Mandarin Speakers Who Stutter

    Science.gov (United States)

    Howell, Peter; Jiang, Jing; Peng, Danling; Lu, Chunming

    2012-01-01

    Neural control of rising and falling tones in Mandarin people who stutter (PWS) was examined by comparing with that which occurs in fluent speakers [Howell, Jiang, Peng, and Lu (2012). Neural control of fundamental frequency rise and fall in Mandarin tones. "Brain and Language, 121"(1), 35-46]. Nine PWS and nine controls were scanned. Functional…

  10. Control System Design for Cylindrical Tank Process Using Neural Model Predictive Control Technique

    Directory of Open Access Journals (Sweden)

    M. Sridevi

    2010-10-01

    Full Text Available Chemical manufacturing and process industry requires innovative technologies for process identification. This paper deals with model identification and control of cylindrical process. Model identification of the process was done using ARMAX technique. A neural model predictive controller was designed for the identified model. The performance of the controllers was evaluated using MATLAB software. The performance of NMPC controller was compared with Smith Predictor controller and IMC controller based on rise time, settling time, overshoot and ISE and it was found that the NMPC controller is better suited for this process.

  11. Neural Learning Control of Flexible Joint Manipulator with Predefined Tracking Performance and Application to Baxter Robot

    Directory of Open Access Journals (Sweden)

    Min Wang

    2017-01-01

    Full Text Available This paper focuses on neural learning from adaptive neural control (ANC for a class of flexible joint manipulator under the output tracking constraint. To facilitate the design, a new transformed function is introduced to convert the constrained tracking error into unconstrained error variable. Then, a novel adaptive neural dynamic surface control scheme is proposed by combining the neural universal approximation. The proposed control scheme not only decreases the dimension of neural inputs but also reduces the number of neural approximators. Moreover, it can be verified that all the closed-loop signals are uniformly ultimately bounded and the constrained tracking error converges to a small neighborhood around zero in a finite time. Particularly, the reduction of the number of neural input variables simplifies the verification of persistent excitation (PE condition for neural networks (NNs. Subsequently, the proposed ANC scheme is verified recursively to be capable of acquiring and storing knowledge of unknown system dynamics in constant neural weights. By reusing the stored knowledge, a neural learning controller is developed for better control performance. Simulation results on a single-link flexible joint manipulator and experiment results on Baxter robot are given to illustrate the effectiveness of the proposed scheme.

  12. Neural control and precision of flight muscle activation in Drosophila.

    Science.gov (United States)

    Lehmann, Fritz-Olaf; Bartussek, Jan

    2017-01-01

    Precision of motor commands is highly relevant in a large context of various locomotor behaviors, including stabilization of body posture, heading control and directed escape responses. While posture stability and heading control in walking and swimming animals benefit from high friction via ground reaction forces and elevated viscosity of water, respectively, flying animals have to cope with comparatively little aerodynamic friction on body and wings. Although low frictional damping in flight is the key to the extraordinary aerial performance and agility of flying birds, bats and insects, it challenges these animals with extraordinary demands on sensory integration and motor precision. Our review focuses on the dynamic precision with which Drosophila activates its flight muscular system during maneuvering flight, considering relevant studies on neural and muscular mechanisms of thoracic propulsion. In particular, we tackle the precision with which flies adjust power output of asynchronous power muscles and synchronous flight control muscles by monitoring muscle calcium and spike timing within the stroke cycle. A substantial proportion of the review is engaged in the significance of visual and proprioceptive feedback loops for wing motion control including sensory integration at the cellular level. We highlight that sensory feedback is the basis for precise heading control and body stability in flies.

  13. Neural-networks-based feedback linearization versus model predictive control of continuous alcoholic fermentation process

    Energy Technology Data Exchange (ETDEWEB)

    Mjalli, F.S.; Al-Asheh, S. [Chemical Engineering Department, Qatar University, Doha (Qatar)

    2005-10-01

    In this work advanced nonlinear neural networks based control system design algorithms are adopted to control a mechanistic model for an ethanol fermentation process. The process model equations for such systems are highly nonlinear. A neural network strategy has been implemented in this work for capturing the dynamics of the mechanistic model for the fermentation process. The neural network achieved has been validated against the mechanistic model. Two neural network based nonlinear control strategies have also been adopted using the model identified. The performance of the feedback linearization technique was compared to neural network model predictive control in terms of stability and set point tracking capabilities. Under servo conditions, the feedback linearization algorithm gave comparable tracking and stability. The feedback linearization controller achieved the control target faster than the model predictive one but with vigorous and sudden controller moves. (Abstract Copyright [2005], Wiley Periodicals, Inc.)

  14. Real-Time Decentralized Neural Control via Backstepping for a Robotic Arm Powered by Industrial Servomotors.

    Science.gov (United States)

    Vazquez, Luis A; Jurado, Francisco; Castaneda, Carlos E; Santibanez, Victor

    2018-02-01

    This paper presents a continuous-time decentralized neural control scheme for trajectory tracking of a two degrees of freedom direct drive vertical robotic arm. A decentralized recurrent high-order neural network (RHONN) structure is proposed to identify online, in a series-parallel configuration and using the filtered error learning law, the dynamics of the plant. Based on the RHONN subsystems, a local neural controller is derived via backstepping approach. The effectiveness of the decentralized neural controller is validated on a robotic arm platform, of our own design and unknown parameters, which uses industrial servomotors to drive the joints.

  15. Neural and Hormonal Control of Postecdysial Behaviors in Insects

    Science.gov (United States)

    White, Benjamin H.; Ewer, John

    2016-01-01

    The shedding of the old exoskeleton that occurs in insects at the end of a molt (a process called ecdysis) is typically followed by the expansion and tanning of a new one. At the adult molt, these postecdysial processes include expanding and hardening the wings. Here we describe recent advances in understanding the neural and hormonal control of wing expansion and hardening, focusing on work done in Drosophila where genetic manipulations have permitted a detailed investigation of postecdysial processes and their modulation by sensory input. To place this work in context, we briefly review recent progress in understanding the neuroendocrine regulation of ecdysis, which appears to be largely conserved across insect species. Investigations into the neuroendocrine networks that regulate ecdysial and postecdysial behaviors, will provide insights into how stereotyped, yet environmentally-responsive, sequences are generated, as well as into how they develop and evolve. PMID:24160420

  16. Comparison of 24-hour cardiovascular and autonomic function in paraplegia, tetraplegia, and control groups: implications for cardiovascular risk.

    Science.gov (United States)

    Rosado-Rivera, Dwindally; Radulovic, M; Handrakis, John P; Cirnigliaro, Christopher M; Jensen, A Marley; Kirshblum, Steve; Bauman, William A; Wecht, Jill Maria

    2011-01-01

    Fluctuations in 24-hour cardiovascular hemodynamics, specifically heart rate (HR) and blood pressure (BP), are thought to reflect autonomic nervous system (ANS) activity. Persons with spinal cord injury (SCI) represent a model of ANS dysfunction, which may affect 24-hour hemodynamics and predispose these individuals to increased cardiovascular disease risk. To determine 24-hour cardiovascular and ANS function among individuals with tetraplegia (n=20; TETRA: C4-C8), high paraplegia (n=10; HP: T2-T5), low paraplegia (n=9; LP: T7-T12), and non-SCI controls (n=10). Twenty-four-hour ANS function was assessed by time domain parameters of heart rate variability (HRV); the standard deviation of the 5-minute average R-R intervals (SDANN; milliseconds/ms), and the root-mean square of the standard deviation of the R-R intervals (rMSSD; ms). Subjects wore 24-hour ambulatory monitors to record HR, HRV, and BP. Mixed analysis of variance (ANOVA) revealed significantly lower 24-hour BP in the tetraplegic group; however, BP did not differ between the HP, LP, and control groups. Mixed ANOVA suggested significantly elevated 24-hour HR in the HP and LP groups compared to the TETRA and control groups (Pgroups compared to the TETRA and control groups (Pgroup compared to the TETRA and control groups (Pgroup compared to the LP and TETRA groups (Pgroups (P<0.05). Elevated 24-hour HR in persons with paraplegia, in concert with altered HRV dynamics, may impart significant adverse cardiovascular consequences, which are currently unappreciated.

  17. Multilayer neural-net robot controller with guaranteed tracking performance.

    Science.gov (United States)

    Lewis, F L; Yegildirek, A; Liu, K

    1996-01-01

    A multilayer neural-net (NN) controller for a general serial-link rigid robot arm is developed. The structure of the NN controller is derived using a filtered error/passivity approach. No off-line learning phase is needed for the proposed NN controller and the weights are easily initialized. The nonlinear nature of the NN, plus NN functional reconstruction inaccuracies and robot disturbances, mean that the standard delta rule using backpropagation tuning does not suffice for closed-loop dynamic control. Novel online weight tuning algorithms, including correction terms to the delta rule plus an added robust signal, guarantee bounded tracking errors as well as bounded NN weights. Specific bounds are determined, and the tracking error bound can be made arbitrarily small by increasing a certain feedback gain. The correction terms involve a second-order forward-propagated wave in the backpropagation network. New NN properties including the notions of a passive NN, a dissipative NN, and a robust NN are introduced.

  18. Composite learning from adaptive backstepping neural network control.

    Science.gov (United States)

    Pan, Yongping; Sun, Tairen; Liu, Yiqi; Yu, Haoyong

    2017-11-01

    In existing neural network (NN) learning control methods, the trajectory of NN inputs must be recurrent to satisfy a stringent condition termed persistent excitation (PE) so that NN parameter convergence is obtainable. This paper focuses on command-filtered backstepping adaptive control for a class of strict-feedback nonlinear systems with functional uncertainties, where an NN composite learning technique is proposed to guarantee convergence of NN weights to their ideal values without the PE condition. In the NN composite learning, spatially localized NN approximation is employed to handle functional uncertainties, online historical data together with instantaneous data are exploited to generate prediction errors, and both tracking errors and prediction errors are employed to update NN weights. The influence of NN approximation errors on the control performance is also clearly shown. The distinctive feature of the proposed NN composite learning is that NN parameter convergence is guaranteed without the requirement of the trajectory of NN inputs being recurrent. Illustrative results have verified effectiveness and superiority of the proposed method compared with existing NN learning control methods. Copyright © 2017 Elsevier Ltd. All rights reserved.

  19. The neural correlates of impaired inhibitory control in anxiety.

    Science.gov (United States)

    Ansari, Tahereh L; Derakshan, Nazanin

    2011-04-01

    According to Attentional Control Theory (Eysenck et al., 2007) anxiety impairs the inhibition function of working memory by increasing the influence of stimulus-driven processes over efficient top-down control. We investigated the neural correlates of impaired inhibitory control in anxiety using an antisaccade task. Low- and high-anxious participants performed anti- and prosaccade tasks and electrophysiological activity was recorded. Consistent with previous research high-anxious individuals had longer antisaccade latencies in response to the to-be-inhibited target, compared with low-anxious individuals. Central to our predictions, high-anxious individuals showed lower ERP activity, at frontocentral and central recording sites, than low anxious individuals, in the period immediately prior to onset of the to-be-inhibited target on correct antisaccade trials. Our findings indicate that anxiety interferes with the efficient recruitment of top-down mechanisms required for the suppression of prepotent responses. Implications are discussed within current models of attentional control in anxiety (Bishop, 2009; Eysenck et al., 2007). Copyright © 2011 Elsevier Ltd. All rights reserved.

  20. Neurale Netværk anvendt indenfor Proceskontrol. Neural Network for Process Control

    DEFF Research Database (Denmark)

    Madsen, Per Printz

    Dette projekt omhandler anvendelsen af neurale netværksmodeller til proceskontrol. Neurale netværksmodeller er simple modeller af de processer, der forløber i det biologiske neurale netværk. Det biologiske neurale netværk er det netværk af nerveceller, der tilsammen danner centralnervesystemet hos...... beskrivelige inputsignaler. Det biologiske neurale netværk dvs. hjernen er således gennem indlæring i stand til at læse, hvorledes der skal stryes og reguleres på baggrund af disse inputsignaler, så det ønskede resultat opnås. Det er derfor nærliggende at undersøge, hvorvidt neurale netværk er anvendelige...... indenfor proceskontrol i almindelighed. Med anvendelser til proceskontrol menes der her anvendeler til prediction, simulering og regulering af dynamiske systemer. For at teste, hvorvidt neurale netværk er anvendelig til prediction og simulering, er der anvendt en tre-trinsoverheder simulator til...

  1. Synchronization of Switched Neural Networks With Communication Delays via the Event-Triggered Control.

    Science.gov (United States)

    Wen, Shiping; Zeng, Zhigang; Chen, Michael Z Q; Huang, Tingwen

    2017-10-01

    This paper addresses the issue of synchronization of switched delayed neural networks with communication delays via event-triggered control. For synchronizing coupled switched neural networks, we propose a novel event-triggered control law which could greatly reduce the number of control updates for synchronization tasks of coupled switched neural networks involving embedded microprocessors with limited on-board resources. The control signals are driven by properly defined events, which depend on the measurement errors and current-sampled states. By using a delay system method, a novel model of synchronization error system with delays is proposed with the communication delays and event-triggered control in the unified framework for coupled switched neural networks. The criteria are derived for the event-triggered synchronization analysis and control synthesis of switched neural networks via the Lyapunov-Krasovskii functional method and free weighting matrix approach. A numerical example is elaborated on to illustrate the effectiveness of the derived results.

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

    DEFF Research Database (Denmark)

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

    2001-01-01

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

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

    DEFF Research Database (Denmark)

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

    2001-01-01

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

  4. Neural network based adaptive output feedback control: Applications and improvements

    Science.gov (United States)

    Kutay, Ali Turker

    Application of recently developed neural network based adaptive output feedback controllers to a diverse range of problems both in simulations and experiments is investigated in this thesis. The purpose is to evaluate the theory behind the development of these controllers numerically and experimentally, identify the needs for further development in practical applications, and to conduct further research in directions that are identified to ultimately enhance applicability of adaptive controllers to real world problems. We mainly focus our attention on adaptive controllers that augment existing fixed gain controllers. A recently developed approach holds great potential for successful implementations on real world applications due to its applicability to systems with minimal information concerning the plant model and the existing controller. In this thesis the formulation is extended to the multi-input multi-output case for distributed control of interconnected systems and successfully tested on a formation flight wind tunnel experiment. The command hedging method is formulated for the approach to further broaden the class of systems it can address by including systems with input nonlinearities. Also a formulation is adopted that allows the approach to be applied to non-minimum phase systems for which non-minimum phase characteristics are modeled with sufficient accuracy and treated properly in the design of the existing controller. It is shown that the approach can also be applied to augment nonlinear controllers under certain conditions and an example is presented where the nonlinear guidance law of a spinning projectile is augmented. Simulation results on a high fidelity 6 degrees-of-freedom nonlinear simulation code are presented. The thesis also presents a preliminary adaptive controller design for closed loop flight control with active flow actuators. Behavior of such actuators in dynamic flight conditions is not known. To test the adaptive controller design in

  5. Region stability analysis and tracking control of memristive recurrent neural network.

    Science.gov (United States)

    Bao, Gang; Zeng, Zhigang; Shen, Yanjun

    2018-02-01

    Memristor is firstly postulated by Leon Chua and realized by Hewlett-Packard (HP) laboratory. Research results show that memristor can be used to simulate the synapses of neurons. This paper presents a class of recurrent neural network with HP memristors. Firstly, it shows that memristive recurrent neural network has more compound dynamics than the traditional recurrent neural network by simulations. Then it derives that n dimensional memristive recurrent neural network is composed of [Formula: see text] sub neural networks which do not have a common equilibrium point. By designing the tracking controller, it can make memristive neural network being convergent to the desired sub neural network. At last, two numerical examples are given to verify the validity of our result. Copyright © 2017 Elsevier Ltd. All rights reserved.

  6. Self-Tuning Vibration Control of a Rotational Flexible Timoshenko Arm Using Neural Networks

    Directory of Open Access Journals (Sweden)

    Minoru Sasaki

    2012-01-01

    Full Text Available A self-tuning vibration control of a rotational flexible arm using neural networks is presented. To the self-tuning control system, the control scheme consists of gain tuning neural networks and a variable-gain feedback controller. The neural networks are trained so as to make the root moment zero. In the process, the neural networks learn the optimal gain of the feedback controller. The feedback controller is designed based on Lyapunov's direct method. The feedback control of the vibration of the flexible system is derived by considering the time rate of change of the total energy of the system. This approach has the advantage over the conventional methods in the respect that it allows one to deal directly with the system's partial differential equations without resorting to approximations. Numerical and experimental results for the vibration control of a rotational flexible arm are discussed. It verifies that the proposed control system is effective at controlling flexible dynamical systems.

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

    Directory of Open Access Journals (Sweden)

    A. Medina-Santiago

    2014-02-01

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

  8. Neural network based adaptive control of nonlinear plants using random search optimization algorithms

    Science.gov (United States)

    Boussalis, Dhemetrios; Wang, Shyh J.

    1992-01-01

    This paper presents a method for utilizing artificial neural networks for direct adaptive control of dynamic systems with poorly known dynamics. The neural network weights (controller gains) are adapted in real time using state measurements and a random search optimization algorithm. The results are demonstrated via simulation using two highly nonlinear systems.

  9. Spacecraft Neural Network Control System Design using FPGA

    OpenAIRE

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

    2011-01-01

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

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

    Directory of Open Access Journals (Sweden)

    A. R Tahavvor

    2016-09-01

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

  11. Neural network control of mobile robot formations using RISE feedback.

    Science.gov (United States)

    Dierks, Travis; Jagannathan, S

    2009-04-01

    In this paper, an asymptotically stable (AS) combined kinematic/torque control law is developed for leader-follower-based formation control using backstepping in order to accommodate the complete dynamics of the robots and the formation, and a neural network (NN) is introduced along with robust integral of the sign of the error feedback to approximate the dynamics of the follower as well as its leader using online weight tuning. It is shown using Lyapunov theory that the errors for the entire formation are AS and that the NN weights are bounded as opposed to uniformly ultimately bounded stability which is typical with most NN controllers. Additionally, the stability of the formation in the presence of obstacles is examined using Lyapunov methods, and by treating other robots in the formation as obstacles, collisions within the formation do not occur. The asymptotic stability of the follower robots as well as the entire formation during an obstacle avoidance maneuver is demonstrated using Lyapunov methods, and numerical results are provided to verify the theoretical conjectures.

  12. Cardiovascular modeling and diagnostics

    Energy Technology Data Exchange (ETDEWEB)

    Kangas, L.J.; Keller, P.E.; Hashem, S.; Kouzes, R.T. [Pacific Northwest Lab., Richland, WA (United States)

    1995-12-31

    In this paper, a novel approach to modeling and diagnosing the cardiovascular system is introduced. A model exhibits a subset of the dynamics of the cardiovascular behavior of an individual by using a recurrent artificial neural network. Potentially, a model will be incorporated into a cardiovascular diagnostic system. This approach is unique in that each cardiovascular model is developed from physiological measurements of an individual. Any differences between the modeled variables and the variables of an individual at a given time are used for diagnosis. This approach also exploits sensor fusion to optimize the utilization of biomedical sensors. The advantage of sensor fusion has been demonstrated in applications including control and diagnostics of mechanical and chemical processes.

  13. Neural Control of Hemorrhage-Induced Tissue Cytokine Production

    National Research Council Canada - National Science Library

    Molina, Patrica E

    2007-01-01

    ... to cardiovascular responsiveness. Our studies demonstrated that the intact neuroendocrine response is critical to ensure survival and host defense mechanisms from secondary infectious challenges...

  14. Optimization of a neural network based direct inverse control for controlling a quadrotor unmanned aerial vehicle

    Directory of Open Access Journals (Sweden)

    Heryanto M Ary

    2015-01-01

    Full Text Available UAVs are mostly used for surveillance, inspection and data acquisition. We have developed a Quadrotor UAV that is constructed based on a four motors with a lift-generating propeller at each motors. In this paper, we discuss the development of a quadrotor and its neural networks direct inverse control model using the actual flight data. To obtain a better performance of the control system of the UAV, we proposed an Optimized Direct Inverse controller based on re-training the neural networks with the new data generated from optimal maneuvers of the quadrotor. Through simulation of the quadrotor using the developed DIC and Optimized DIC model, results show that both models have the ability to stabilize the quadrotor with a good tracking performance. The optimized DIC model, however, has shown a better performance, especially in the settling time parameter.

  15. Neural network model to control an experimental chaotic pendulum

    NARCIS (Netherlands)

    Bakker, R; Schouten, JC; Takens, F; vandenBleek, CM

    1996-01-01

    A feedforward neural network was trained to predict the motion of an experimental, driven, and damped pendulum operating in a chaotic regime. The network learned the behavior of the pendulum from a time series of the pendulum's angle, the single measured variable. The validity of the neural

  16. Central chemoreceptors and neural mechanisms of cardiorespiratory control

    Directory of Open Access Journals (Sweden)

    T.S. Moreira

    2011-09-01

    Full Text Available The arterial partial pressure (P CO2 of carbon dioxide is virtually constant because of the close match between the metabolic production of this gas and its excretion via breathing. Blood gas homeostasis does not rely solely on changes in lung ventilation, but also to a considerable extent on circulatory adjustments that regulate the transport of CO2 from its sites of production to the lungs. The neural mechanisms that coordinate circulatory and ventilatory changes to achieve blood gas homeostasis are the subject of this review. Emphasis will be placed on the control of sympathetic outflow by central chemoreceptors. High levels of CO2 exert an excitatory effect on sympathetic outflow that is mediated by specialized chemoreceptors such as the neurons located in the retrotrapezoid region. In addition, high CO2 causes an aversive awareness in conscious animals, activating wake-promoting pathways such as the noradrenergic neurons. These neuronal groups, which may also be directly activated by brain acidification, have projections that contribute to the CO2-induced rise in breathing and sympathetic outflow. However, since the level of activity of the retrotrapezoid nucleus is regulated by converging inputs from wake-promoting systems, behavior-specific inputs from higher centers and by chemical drive, the main focus of the present manuscript is to review the contribution of central chemoreceptors to the control of autonomic and respiratory mechanisms.

  17. Neural mechanisms of attentional control in mindfulness meditation

    Directory of Open Access Journals (Sweden)

    Peter eMalinowski

    2013-02-01

    Full Text Available The scientific interest in meditation and mindfulness practice has recently seen an unprecedented surge. After an initial phase of presenting beneficial effects of mindfulness practice in various domains, research is now seeking to unravel the underlying psychological and neurophysiological mechanisms. Advances in understanding these processes are required for improving and fine-tuning mindfulness-based interventions that target specific conditions such as eating disorders or attention deficit hyperactivity disorders. This review presents a theoretical framework that emphasizes the central role of attentional control mechanisms in the development of mindfulness skills. It discusses the phenomenological level of experience during meditation, the different attentional functions that are involved, and relates these to the brain networks that subserve these functions. On the basis of currently available empirical evidence specific processes as to how attention exerts its positive influence are considered and it is concluded that meditation practice appears to positively impact attentional functions by improving resource allocation processes. As a result, attentional resources are allocated more fully during early processing phases which subsequently enhance further processing. Neural changes resulting from a pure form of mindfulness practice that is central to most mindfulness programs are considered from the perspective that they constitute a useful reference point for future research. Furthermore, possible interrelations between the improvement of attentional control and emotion regulation skills are discussed.

  18. Reinforced ART (ReART) for Online Neural Control

    Science.gov (United States)

    Ediriweera, Damjee D.; Marshall, Ian W.

    Fuzzy ART has been proposed for learning stable recognition categories for an arbitrary sequence of analogue input patterns. It uses a match based learning mechanism to categorise inputs based on similarities in their features. However, this approach does not work well for neural control, where inputs have to be categorised based on the classes which they represent, rather than by the features of the input. To address this we propose and investigate ReART, a novel extension to Fuzzy ART. ReART uses a feedback based categorisation mechanism supporting class based input categorisation, online learning, and immunity from the plasticity stability dilemma. ReART is used for online control by integrating it with a separate external function which maps each ReART category to a desired output action. We test the proposal in the context of a simulated wireless data reader intended to be carried by an autonomous mobile vehicle, and show that ReART training time and accuracy are significantly better than both Fuzzy ART and Back Propagation. ReART is also compared to a Naïve Bayesian Classifier. Naïve Bayesian Classification achieves faster learning, but is less accurate in testing compared to both ReART, and Bach Propagation.

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

    Science.gov (United States)

    Ning, Min

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

  20. Real-Time Control Strategy of Elman Neural Network for the Parallel Hybrid Electric Vehicle

    Directory of Open Access Journals (Sweden)

    Ruijun Liu

    2014-01-01

    Full Text Available Through researching the instantaneous control strategy and Elman neural network, the paper established equivalent fuel consumption functions under the charging and discharging conditions of power batteries, deduced the optimal control objective function of instantaneous equivalent consumption, established the instantaneous optimal control model, and designs the Elman neural network controller. Based on the ADVISOR 2002 platform, the instantaneous optimal control strategy and the Elman neural network control strategy were simulated on a parallel HEV. The simulation results were analyzed in the end. The contribution of the paper is that the trained Elman neural network control strategy can reduce the simulation time by 96% and improve the real-time performance of energy control, which also ensures the good performance of power and fuel economy.

  1. Part 2: Prediktion, Simulering og Regulering med Neurale Netværk. Prediction, Simulation and Control using Neural Network

    DEFF Research Database (Denmark)

    Schiøler, Henrik

    til Del 1, idet de to rapporter kan opfattes som en enhed. Herefter introduceres de grundlæggende begreber inden for prediktion, samt for mål og integralteorien. Det beskrives, hvorledes neurale net kan fungere som ulinære prediktionsmodeller og den nødvendige teori for Multi Lags Perceptronen (MLP......) samt alternative strukturer baseret på Parzen Window estimationsmetoden, præsenteres med detaljerne af analysen henlagt til appendices. Herefter demonstreres ved en simpel test, hvorledes de forskellige nettyper fungerer i prediktionsanvendelser. Herefter er neurale net anvendt til simulering behandlet...... på tilsvarende måde, dog i en lidt forkortet udgave. Til sidst behandles, hvorledes de behandlede nettyper anvendes i en regulatorstruktur baseret på såkaldte Sliding mode control. Teorien for de neurale net er her den samme som for simulering. Det konkluderes at de alternative strukturer, baseret på...

  2. Neural Predictors of Decisions to Cognitively Control Emotion.

    Science.gov (United States)

    Doré, Bruce Pierre; Weber, Jochen; Ochsner, Kevin Nicholas

    2017-03-08

    Deciding to control emotional responses is a fundamental means of responding to environmental challenges, but little is known about the neural mechanisms that predict the outcome of such decisions. We used fMRI to test whether human brain responses during initial viewing of negative images could be used to predict decisions to regulate affective responses to those images. Our results revealed the following: (1) decisions to regulate were more frequent in individuals exhibiting higher average levels of activity within the amygdala and regions of PFC known a priori to be involved in the cognitive control of emotion and (2) within-person expression of a distributed brain pattern associated with regulating emotion predicted choosing to regulate responses to particular stimuli beyond the predictive value of stimulus intensity or self-reports of emotion. These results demonstrate the behavioral relevance of variability in brain responses to aversive stimuli and provide a model that leverages this variability to predict behavior. SIGNIFICANCE STATEMENT Everyone experiences stressors, but how we respond to them can range from protracted disability to resilience and growth. One key process underlying this variability is the agentic decision to exert control over emotional responses. We present an fMRI-based model predicting decisions to control emotion, finding that activity in brain regions associated with the generation and regulation of emotion was predictive of which people choose to regulate frequently and a distributed brain pattern associated with regulating emotion was predictive of which stimuli regulation was chosen. These brain variables predicted future decisions to regulate emotion beyond what could be predicted from stimulus and self-report variables. Copyright © 2017 the authors 0270-6474/17/372580-09$15.00/0.

  3. Longitudinal associations among family environment, neural cognitive control, and social competence among adolescents

    Directory of Open Access Journals (Sweden)

    Jungmeen Kim-Spoon

    2017-08-01

    Full Text Available During adolescence, prefrontal cortex regions, important in cognitive control, undergo maturation to adapt to changing environmental demands. Ways through which social-ecological factors contribute to adolescent neural cognitive control have not been thoroughly examined. We hypothesize that household chaos is a context that may modulate the associations among parental control, adolescent neural cognitive control, and developmental changes in social competence. The sample involved 167 adolescents (ages 13–14 at Time 1, 53% male. Parental control and household chaos were measured using adolescents’ questionnaire data, and cognitive control was assessed via behavioral performance and brain imaging at Time 1. Adolescent social competence was reported by adolescents at Time 1 and at Time 2 (one year later. Structural equation modeling analyses indicated that higher parental control predicted better neural cognitive control only among adolescents living in low-chaos households. The association between poor neural cognitive control at Time 1 and social competence at Time 2 (after controlling for social competence at Time 1 was significant only among adolescents living in high-chaos households. Household chaos may undermine the positive association of parental control with adolescent neural cognitive control and exacerbate the detrimental association of poor neural cognitive control with disrupted social competence development.

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

    OpenAIRE

    Liu, Da; Li, Muguo

    2015-01-01

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

  5. Cardiovascular causes of syncope. Identifying and controlling trigger mechanisms

    Energy Technology Data Exchange (ETDEWEB)

    Akhtar, M.; Jazayeri, M.; Sra, J. (Sinai Samaritan Medical Center, Milwaukee, WI (USA))

    1991-08-01

    Syncope usually has a cardiovascular source, so neurologic evaluation has a low diagnostic yield in these patients. Cardiac arrhythmias in persons with or without structural heart disease can produce syncope. Neurocardiogenic dysfunction that results in diminished venous return and hypercontractility is another frequent cause. Postural hypotension or left ventricular outflow obstruction may also be to blame. Careful history taking and physical examination, head-up tilt testing, echocardiography or radionuclide isotope imaging, and electrophysiologic study are often diagnostic. However, syncope remains undiagnosed in some patients, and they may require periodic reassessment. Treatment options are available for most cardiovascular disorders, among them use of pharmacologic agents; catheter, surgical, or radio-frequency modification of certain tachycardias; and permanent pacing. 33 references.

  6. Neural mechanisms of timing control in a coincident timing task.

    Science.gov (United States)

    Masaki, Hiroaki; Sommer, Werner; Takasawa, Noriyoshi; Yamazaki, Katuo

    2012-04-01

    Many ball sports such as tennis or baseball require precise temporal anticipation of both sensory input and motor output (i.e., receptor anticipation and effector anticipation, respectively) and close performance monitoring. We investigated the neural mechanisms underlying timing control and performance monitoring in a coincident timing task involving both types of anticipations. Peak force for two time-to-peak force (TTP) conditions-recorded with a force-sensitive key-was required to coincide with a specific position of a stimulus rotating either slow or fast on a clock face while the contingent negative variation (CNV) and the motor-elicited negativity were recorded. Absolute timing error was generally smaller for short TTP (high velocity) conditions. CNV amplitudes increased with both faster stimulus velocity and longer TTPs possibly reflecting increased motor programming efforts. In addition, the motor-elicited negativity was largest in the slow stimulus/short TTP condition, probably representing some forms of performance monitoring as well as shorter response duration. Our findings indicate that the coincident timing task is a good model for real-life situations of tool use.

  7. Neural control of tuneable skin iridescence in squid.

    Science.gov (United States)

    Wardill, T J; Gonzalez-Bellido, P T; Crook, R J; Hanlon, R T

    2012-10-22

    Fast dynamic control of skin coloration is rare in the animal kingdom, whether it be pigmentary or structural. Iridescent structural coloration results when nanoscale structures disrupt incident light and selectively reflect specific colours. Unlike animals with fixed iridescent coloration (e.g. butterflies), squid iridophores (i.e. aggregations of iridescent cells in the skin) produce dynamically tuneable structural coloration, as exogenous application of acetylcholine (ACh) changes the colour and brightness output. Previous efforts to stimulate iridophores neurally or to identify the source of endogenous ACh were unsuccessful, leaving researchers to question the activation mechanism. We developed a novel neurophysiological preparation in the squid Doryteuthis pealeii and demonstrated that electrical stimulation of neurons in the skin shifts the spectral peak of the reflected light to shorter wavelengths (greater than 145 nm) and increases the peak reflectance (greater than 245%) of innervated iridophores. We show ACh is released within the iridophore layer and that extensive nerve branching is seen within the iridophore. The dynamic colour shift is significantly faster (17 s) than the peak reflectance increase (32 s), revealing two distinct mechanisms. Responses from a structurally altered preparation indicate that the reflectin protein condensation mechanism explains peak reflectance change, while an undiscovered mechanism causes the fast colour shift.

  8. Neural Control of Breathing and CO2 Homeostasis.

    Science.gov (United States)

    Guyenet, Patrice G; Bayliss, Douglas A

    2015-09-02

    Recent advances have clarified how the brain detects CO2 to regulate breathing (central respiratory chemoreception). These mechanisms are reviewed and their significance is presented in the general context of CO2/pH homeostasis through breathing. At rest, respiratory chemoreflexes initiated at peripheral and central sites mediate rapid stabilization of arterial PCO2 and pH. Specific brainstem neurons (e.g., retrotrapezoid nucleus, RTN; serotonergic) are activated by PCO2 and stimulate breathing. RTN neurons detect CO2 via intrinsic proton receptors (TASK-2, GPR4), synaptic input from peripheral chemoreceptors and signals from astrocytes. Respiratory chemoreflexes are arousal state dependent whereas chemoreceptor stimulation produces arousal. When abnormal, these interactions lead to sleep-disordered breathing. During exercise, central command and reflexes from exercising muscles produce the breathing stimulation required to maintain arterial PCO2 and pH despite elevated metabolic activity. The neural circuits underlying central command and muscle afferent control of breathing remain elusive and represent a fertile area for future investigation. Copyright © 2015 Elsevier Inc. All rights reserved.

  9. Gastrointestinal parasites and the neural control of gut functions

    Directory of Open Access Journals (Sweden)

    Marie Christiane Halliez

    2015-11-01

    Full Text Available Gastrointestinal motility and transport of water and electrolytes play key roles in the pathophysiology of diarrhea upon exposure to enteric parasites. These processes are actively modulated by the enteric nervous system (ENS, which includes efferent, and afferent neurons, as well as interneurons. ENS integrity is essential to the maintenance of homeostatic gut responses. A number of gastrointestinal parasites are known to cause disease by altering the enteric nervous system. The mechanisms remain incompletely understood. Cryptosporidium parvum, Giardia duodenalis (syn. G. intestinalis, G. lamblia, Trypanosoma cruzi, Schistosoma sp and others alter gastrointestinal motility, absorption, or secretion at least in part via effects on the ENS. Recent findings also implicate enteric parasites such as Cryptosporidium parvum and Giardia duodenalis in the development of post-infectious complications such as irritable bowel syndrome, which further underscores their effects on the gut-brain axis. This article critically reviews recent advances and the current state of knowledge on the impact of enteric parasitism on the neural control of gut functions, and provides insights into mechanisms underlying these abnormalities.

  10. Neural Control and Adaptive Neural Forward Models for Insect-like, Energy-Efficient, and Adaptable Locomotion of Walking Machines

    Directory of Open Access Journals (Sweden)

    Poramate eManoonpong

    2013-02-01

    Full Text Available Living creatures, like walking animals, have found fascinating solutions for the problem of locomotion control. Their movements show the impression of elegance including versatile, energy-efficient, and adaptable locomotion. During the last few decades, roboticists have tried to imitate such natural properties with artificial legged locomotion systems by using different approaches including machine learning algorithms, classical engineering control techniques, and biologically-inspired control mechanisms. However, their levels of performance are still far from the natural ones. By contrast, animal locomotion mechanisms seem to largely depend not only on central mechanisms (central pattern generators, CPGs and sensory feedback (afferent-based control but also on internal forward models (efference copies. They are used to a different degree in different animals. Generally, CPGs organize basic rhythmic motions which are shaped by sensory feedback while internal models are used for sensory prediction and state estimations. According to this concept, we present here adaptive neural locomotion control consisting of a CPG mechanism with neuromodulation and local leg control mechanisms based on sensory feedback and adaptive neural forward models with efference copies. This neural closed-loop controller enables a walking machine to perform a multitude of different walking patterns including insect-like leg movements and gaits as well as energy-efficient locomotion. In addition, the forward models allow the machine to autonomously adapt its locomotion to deal with a change of terrain, losing of ground contact during stance phase, stepping on or hitting an obstacle during swing phase, leg damage, and even to promote cockroach-like climbing behavior. Thus, the results presented here show that the employed embodied neural closed-loop system can be a powerful way for developing robust and adaptable machines.

  11. Neural control and adaptive neural forward models for insect-like, energy-efficient, and adaptable locomotion of walking machines.

    Science.gov (United States)

    Manoonpong, Poramate; Parlitz, Ulrich; Wörgötter, Florentin

    2013-01-01

    Living creatures, like walking animals, have found fascinating solutions for the problem of locomotion control. Their movements show the impression of elegance including versatile, energy-efficient, and adaptable locomotion. During the last few decades, roboticists have tried to imitate such natural properties with artificial legged locomotion systems by using different approaches including machine learning algorithms, classical engineering control techniques, and biologically-inspired control mechanisms. However, their levels of performance are still far from the natural ones. By contrast, animal locomotion mechanisms seem to largely depend not only on central mechanisms (central pattern generators, CPGs) and sensory feedback (afferent-based control) but also on internal forward models (efference copies). They are used to a different degree in different animals. Generally, CPGs organize basic rhythmic motions which are shaped by sensory feedback while internal models are used for sensory prediction and state estimations. According to this concept, we present here adaptive neural locomotion control consisting of a CPG mechanism with neuromodulation and local leg control mechanisms based on sensory feedback and adaptive neural forward models with efference copies. This neural closed-loop controller enables a walking machine to perform a multitude of different walking patterns including insect-like leg movements and gaits as well as energy-efficient locomotion. In addition, the forward models allow the machine to autonomously adapt its locomotion to deal with a change of terrain, losing of ground contact during stance phase, stepping on or hitting an obstacle during swing phase, leg damage, and even to promote cockroach-like climbing behavior. Thus, the results presented here show that the employed embodied neural closed-loop system can be a powerful way for developing robust and adaptable machines.

  12. Quantized Synchronization of Chaotic Neural Networks With Scheduled Output Feedback Control.

    Science.gov (United States)

    Wan, Ying; Cao, Jinde; Wen, Guanghui

    In this paper, the synchronization problem of master-slave chaotic neural networks with remote sensors, quantization process, and communication time delays is investigated. The information communication channel between the master chaotic neural network and slave chaotic neural network consists of several remote sensors, with each sensor able to access only partial knowledge of output information of the master neural network. At each sampling instants, each sensor updates its own measurement and only one sensor is scheduled to transmit its latest information to the controller's side in order to update the control inputs for the slave neural network. Thus, such communication process and control strategy are much more energy-saving comparing with the traditional point-to-point scheme. Sufficient conditions for output feedback control gain matrix, allowable length of sampling intervals, and upper bound of network-induced delays are derived to ensure the quantized synchronization of master-slave chaotic neural networks. Lastly, Chua's circuit system and 4-D Hopfield neural network are simulated to validate the effectiveness of the main results.In this paper, the synchronization problem of master-slave chaotic neural networks with remote sensors, quantization process, and communication time delays is investigated. The information communication channel between the master chaotic neural network and slave chaotic neural network consists of several remote sensors, with each sensor able to access only partial knowledge of output information of the master neural network. At each sampling instants, each sensor updates its own measurement and only one sensor is scheduled to transmit its latest information to the controller's side in order to update the control inputs for the slave neural network. Thus, such communication process and control strategy are much more energy-saving comparing with the traditional point-to-point scheme. Sufficient conditions for output feedback control

  13. Translating Principles of Neural Plasticity into Research on Speech Motor Control Recovery and Rehabilitation

    Science.gov (United States)

    Ludlow, Christy L.; Hoit, Jeannette; Kent, Raymond; Ramig, Lorraine O.; Shrivastav, Rahul; Strand, Edythe; Yorkston, Kathryn; Sapienza, Christine M.

    2008-01-01

    Purpose: To review the principles of neural plasticity and make recommendations for research on the neural bases for rehabilitation of neurogenic speech disorders. Method: A working group in speech motor control and disorders developed this report, which examines the potential relevance of basic research on the brain mechanisms involved in neural…

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

    Science.gov (United States)

    Long, Lijun; Zhao, Jun

    2017-07-01

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

  15. Comparison of 24-hour cardiovascular and autonomic function in paraplegia, tetraplegia, and control groups: Implications for cardiovascular risk

    Science.gov (United States)

    Rosado-Rivera, Dwindally; Radulovic, M.; Handrakis, John P.; Cirnigliaro, Christopher M.; Jensen, A. Marley; Kirshblum, Steve; Bauman, William A.; Wecht, Jill Maria

    2011-01-01

    Background Fluctuations in 24-hour cardiovascular hemodynamics, specifically heart rate (HR) and blood pressure (BP), are thought to reflect autonomic nervous system (ANS) activity. Persons with spinal cord injury (SCI) represent a model of ANS dysfunction, which may affect 24-hour hemodynamics and predispose these individuals to increased cardiovascular disease risk. Objective To determine 24-hour cardiovascular and ANS function among individuals with tetraplegia (n = 20; TETRA: C4–C8), high paraplegia (n = 10; HP: T2–T5), low paraplegia (n = 9; LP: T7–T12), and non-SCI controls (n = 10). Twenty-four-hour ANS function was assessed by time domain parameters of heart rate variability (HRV); the standard deviation of the 5-minute average R–R intervals (SDANN; milliseconds/ms), and the root-mean square of the standard deviation of the R–R intervals (rMSSD; ms). Subjects wore 24-hour ambulatory monitors to record HR, HRV, and BP. Mixed analysis of variance (ANOVA) revealed significantly lower 24-hour BP in the tetraplegic group; however, BP did not differ between the HP, LP, and control groups. Mixed ANOVA suggested significantly elevated 24-hour HR in the HP and LP groups compared to the TETRA and control groups (P < 0.05); daytime HR was higher in both paraplegic groups compared to the TETRA and control groups (P < 0.01) and nighttime HR was significantly elevated in the LP group compared to the TETRA and control groups (P < 0.01). Twenty-four-hour SDANN was significantly increased in the HP group compared to the LP and TETRA groups (P < 0.05) and rMSSD was significantly lower in the LP compared to the other three groups (P < 0.05). Elevated 24-hour HR in persons with paraplegia, in concert with altered HRV dynamics, may impart significant adverse cardiovascular consequences, which are currently unappreciated. PMID:21903013

  16. Neural Control and Adaptive Neural Forward Models for Insect-like, Energy-Efficient, and Adaptable Locomotion of Walking Machines

    DEFF Research Database (Denmark)

    Manoonpong, Poramate; Parlitz, Ulrich; Wörgötter, Florentin

    2013-01-01

    Living creatures, like walking animals, have found fascinating solutions for the problem of locomotion control. Their movements show the impression of elegance including versatile, energy-efficient, and adaptable locomotion. During the last few decades, roboticists have tried to imitate......, animal locomotion mechanisms seem to largely depend not only on central mechanisms (central pattern generators, CPGs) and sensory feedback (afferent-based control) but also on internal forward models (efference copies). They are used to a different degree in different animals. Generally, CPGs organize...... on sensory feedback and adaptive neural forward models with efference copies. This neural closed-loop controller enables a walking machine to perform a multitude of different walking patterns including insect-like leg movements and gaits as well as energy-efficient locomotion. In addition, the forward models...

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

    Science.gov (United States)

    Ayodele I., Olanipekun

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

  18. Glucocorticoid control of gene transcription in neural tissue

    NARCIS (Netherlands)

    Morsink, Maarten Christian

    2007-01-01

    Glucocorticoid hormones exert modulatory effects on neural function in a delayed genomic fashion. The two receptor types that can bind glucocorticoids, the mineralocorticoid receptor (MR) and the glucocorticoid receptor (GR), are ligand-inducible transcription factors. Therefore, changes in gene

  19. Neuromodulation of the neural circuits controlling the lower urinary tract.

    Science.gov (United States)

    Gad, Parag N; Roy, Roland R; Zhong, Hui; Gerasimenko, Yury P; Taccola, Giuliano; Edgerton, V Reggie

    2016-11-01

    The inability to control timely bladder emptying is one of the most serious challenges among the many functional deficits that occur after a spinal cord injury. We previously demonstrated that electrodes placed epidurally on the dorsum of the spinal cord can be used in animals and humans to recover postural and locomotor function after complete paralysis and can be used to enable voiding in spinal rats. In the present study, we examined the neuromodulation of lower urinary tract function associated with acute epidural spinal cord stimulation, locomotion, and peripheral nerve stimulation in adult rats. Herein we demonstrate that electrically evoked potentials in the hindlimb muscles and external urethral sphincter are modulated uniquely when the rat is stepping bipedally and not voiding, immediately pre-voiding, or when voiding. We also show that spinal cord stimulation can effectively neuromodulate the lower urinary tract via frequency-dependent stimulation patterns and that neural peripheral nerve stimulation can activate the external urethral sphincter both directly and via relays in the spinal cord. The data demonstrate that the sensorimotor networks controlling bladder and locomotion are highly integrated neurophysiologically and behaviorally and demonstrate how these two functions are modulated by sensory input from the tibial and pudental nerves. A more detailed understanding of the high level of interaction between these networks could lead to the integration of multiple neurophysiological strategies to improve bladder function. These data suggest that the development of strategies to improve bladder function should simultaneously engage these highly integrated networks in an activity-dependent manner. Copyright © 2016. Published by Elsevier Inc.

  20. Neural network-based sliding mode control for atmospheric-actuated spacecraft formation using switching strategy

    Science.gov (United States)

    Sun, Ran; Wang, Jihe; Zhang, Dexin; Shao, Xiaowei

    2018-02-01

    This paper presents an adaptive neural networks-based control method for spacecraft formation with coupled translational and rotational dynamics using only aerodynamic forces. It is assumed that each spacecraft is equipped with several large flat plates. A coupled orbit-attitude dynamic model is considered based on the specific configuration of atmospheric-based actuators. For this model, a neural network-based adaptive sliding mode controller is implemented, accounting for system uncertainties and external perturbations. To avoid invalidation of the neural networks destroying stability of the system, a switching control strategy is proposed which combines an adaptive neural networks controller dominating in its active region and an adaptive sliding mode controller outside the neural active region. An optimal process is developed to determine the control commands for the plates system. The stability of the closed-loop system is proved by a Lyapunov-based method. Comparative results through numerical simulations illustrate the effectiveness of executing attitude control while maintaining the relative motion, and higher control accuracy can be achieved by using the proposed neural-based switching control scheme than using only adaptive sliding mode controller.

  1. Online adaptive neural control of a robotic lower limb prosthesis

    Science.gov (United States)

    Spanias, J. A.; Simon, A. M.; Finucane, S. B.; Perreault, E. J.; Hargrove, L. J.

    2018-02-01

    Objective. The purpose of this study was to develop and evaluate an adaptive intent recognition algorithm that continuously learns to incorporate a lower limb amputee’s neural information (acquired via electromyography (EMG)) as they ambulate with a robotic leg prosthesis. Approach. We present a powered lower limb prosthesis that was configured to acquire the user’s neural information and kinetic/kinematic information from embedded mechanical sensors, and identify and respond to the user’s intent. We conducted an experiment with eight transfemoral amputees over multiple days. EMG and mechanical sensor data were collected while subjects using a powered knee/ankle prosthesis completed various ambulation activities such as walking on level ground, stairs, and ramps. Our adaptive intent recognition algorithm automatically transitioned the prosthesis into the different locomotion modes and continuously updated the user’s model of neural data during ambulation. Main results. Our proposed algorithm accurately and consistently identified the user’s intent over multiple days, despite changing neural signals. The algorithm incorporated 96.31% [0.91%] (mean, [standard error]) of neural information across multiple experimental sessions, and outperformed non-adaptive versions of our algorithm—with a 6.66% [3.16%] relative decrease in error rate. Significance. This study demonstrates that our adaptive intent recognition algorithm enables incorporation of neural information over long periods of use, allowing assistive robotic devices to accurately respond to the user’s intent with low error rates.

  2. Position Control of a Pneumatic Muscle Actuator Using RBF Neural Network Tuned PID Controller

    Directory of Open Access Journals (Sweden)

    Jie Zhao

    2015-01-01

    Full Text Available Pneumatic Muscle Actuator (PMA has a broad application prospect in soft robotics. However, PMA has highly nonlinear and hysteretic properties among force, displacement, and pressure, which lead to difficulty in accurate position control. A phenomenological model is developed to portray the hysteretic behavior of PMA. This phenomenological model consists of linear component and hysteretic component force. The latter component is described by Duhem model. An experimental apparatus is built up and sets of experimental data are acquired. Based on the experimental data, parameters of the model are identified. Validation of the model is performed. Then a novel cascade position PID controller is devised for a 1-DOF manipulator actuated by PMA. The outer loop of the controller is to cope with position control whilst the inner loop deals with pressure dynamics within PMA. To enhance the adaptability of the PID algorithm to the high nonlinearities of the manipulator, PID parameters are tuned online using RBF Neural Network. Experiments are performed and comparison between position response of RBF Neural Network based PID controller and that of classic PID controller demonstrates the effectiveness of the novel adaptive controller on the manipulator.

  3. Experiments in Neural-Network Control of a Free-Flying Space Robot

    Science.gov (United States)

    Wilson, Edward

    1995-01-01

    Four important generic issues are identified and addressed in some depth in this thesis as part of the development of an adaptive neural network based control system for an experimental free flying space robot prototype. The first issue concerns the importance of true system level design of the control system. A new hybrid strategy is developed here, in depth, for the beneficial integration of neural networks into the total control system. A second important issue in neural network control concerns incorporating a priori knowledge into the neural network. In many applications, it is possible to get a reasonably accurate controller using conventional means. If this prior information is used purposefully to provide a starting point for the optimizing capabilities of the neural network, it can provide much faster initial learning. In a step towards addressing this issue, a new generic Fully Connected Architecture (FCA) is developed for use with backpropagation. A third issue is that neural networks are commonly trained using a gradient based optimization method such as backpropagation; but many real world systems have Discrete Valued Functions (DVFs) that do not permit gradient based optimization. One example is the on-off thrusters that are common on spacecraft. A new technique is developed here that now extends backpropagation learning for use with DVFs. The fourth issue is that the speed of adaptation is often a limiting factor in the implementation of a neural network control system. This issue has been strongly resolved in the research by drawing on the above new contributions.

  4. Course Control of Underactuated Ship Based on Nonlinear Robust Neural Network Backstepping Method.

    Science.gov (United States)

    Yuan, Junjia; Meng, Hao; Zhu, Qidan; Zhou, Jiajia

    2016-01-01

    The problem of course control for underactuated surface ship is addressed in this paper. Firstly, neural networks are adopted to determine the parameters of the unknown part of ideal virtual backstepping control, even the weight values of neural network are updated by adaptive technique. Then uniform stability for the convergence of course tracking errors has been proven through Lyapunov stability theory. Finally, simulation experiments are carried out to illustrate the effectiveness of proposed control method.

  5. SpikingLab: modelling agents controlled by Spiking Neural Networks in Netlogo.

    Science.gov (United States)

    Jimenez-Romero, Cristian; Johnson, Jeffrey

    2017-01-01

    The scientific interest attracted by Spiking Neural Networks (SNN) has lead to the development of tools for the simulation and study of neuronal dynamics ranging from phenomenological models to the more sophisticated and biologically accurate Hodgkin-and-Huxley-based and multi-compartmental models. However, despite the multiple features offered by neural modelling tools, their integration with environments for the simulation of robots and agents can be challenging and time consuming. The implementation of artificial neural circuits to control robots generally involves the following tasks: (1) understanding the simulation tools, (2) creating the neural circuit in the neural simulator, (3) linking the simulated neural circuit with the environment of the agent and (4) programming the appropriate interface in the robot or agent to use the neural controller. The accomplishment of the above-mentioned tasks can be challenging, especially for undergraduate students or novice researchers. This paper presents an alternative tool which facilitates the simulation of simple SNN circuits using the multi-agent simulation and the programming environment Netlogo (educational software that simplifies the study and experimentation of complex systems). The engine proposed and implemented in Netlogo for the simulation of a functional model of SNN is a simplification of integrate and fire (I&F) models. The characteristics of the engine (including neuronal dynamics, STDP learning and synaptic delay) are demonstrated through the implementation of an agent representing an artificial insect controlled by a simple neural circuit. The setup of the experiment and its outcomes are described in this work.

  6. Cardiovascular and fluid volume control in humans in space

    DEFF Research Database (Denmark)

    Norsk, Peter

    2005-01-01

    expansion, which is accompanied by an increase in renal excretion rates of water and sodium. The mechanisms for the changes in renal excretory rates include a complex interaction of cardiovascular reflexes, neuroendocrine variables, and physical factors. Weightlessness is unique to obtain more information...... on this complex interaction, because it is the only way to completely abolish the effects of gravity over longer periods. Results from space have been unexpected, because astronauts exhibit a fluid and sodium retaining state with activation of the sympathetic nervous system, which subjects during simulations...... is of importance for understanding pathophysiology of heart failure, where gravity plays a strong role in fluid and sodium retention....

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

    Science.gov (United States)

    Long, Lijun; Zhao, Jun

    2015-07-01

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

  8. Global synchronization of memristive neural networks subject to random disturbances via distributed pinning control.

    Science.gov (United States)

    Guo, Zhenyuan; Yang, Shaofu; Wang, Jun

    2016-12-01

    This paper presents theoretical results on global exponential synchronization of multiple memristive neural networks in the presence of external noise by means of two types of distributed pinning control. The multiple memristive neural networks are coupled in a general structure via a nonlinear function, which consists of a linear diffusive term and a discontinuous sign term. A pinning impulsive control law is introduced in the coupled system to synchronize all neural networks. Sufficient conditions are derived for ascertaining global exponential synchronization in mean square. In addition, a pinning adaptive control law is developed to achieve global exponential synchronization in mean square. Both pinning control laws utilize only partial state information received from the neighborhood of the controlled neural network. Simulation results are presented to substantiate the theoretical results. Copyright © 2016 Elsevier Ltd. All rights reserved.

  9. Adaptive Sliding Mode Control of Chaos in Permanent Magnet Synchronous Motor via Fuzzy Neural Networks

    Directory of Open Access Journals (Sweden)

    Tat-Bao-Thien Nguyen

    2014-01-01

    Full Text Available In this paper, based on fuzzy neural networks, we develop an adaptive sliding mode controller for chaos suppression and tracking control in a chaotic permanent magnet synchronous motor (PMSM drive system. The proposed controller consists of two parts. The first is an adaptive sliding mode controller which employs a fuzzy neural network to estimate the unknown nonlinear models for constructing the sliding mode controller. The second is a compensational controller which adaptively compensates estimation errors. For stability analysis, the Lyapunov synthesis approach is used to ensure the stability of controlled systems. Finally, simulation results are provided to verify the validity and superiority of the proposed method.

  10. Adaptive inverse control of neural spatiotemporal spike patterns with a reproducing kernel Hilbert space (RKHS) framework.

    Science.gov (United States)

    Li, Lin; Park, Il Memming; Brockmeier, Austin; Chen, Badong; Seth, Sohan; Francis, Joseph T; Sanchez, Justin C; Príncipe, José C

    2013-07-01

    The precise control of spiking in a population of neurons via applied electrical stimulation is a challenge due to the sparseness of spiking responses and neural system plasticity. We pose neural stimulation as a system control problem where the system input is a multidimensional time-varying signal representing the stimulation, and the output is a set of spike trains; the goal is to drive the output such that the elicited population spiking activity is as close as possible to some desired activity, where closeness is defined by a cost function. If the neural system can be described by a time-invariant (homogeneous) model, then offline procedures can be used to derive the control procedure; however, for arbitrary neural systems this is not tractable. Furthermore, standard control methodologies are not suited to directly operate on spike trains that represent both the target and elicited system response. In this paper, we propose a multiple-input multiple-output (MIMO) adaptive inverse control scheme that operates on spike trains in a reproducing kernel Hilbert space (RKHS). The control scheme uses an inverse controller to approximate the inverse of the neural circuit. The proposed control system takes advantage of the precise timing of the neural events by using a Schoenberg kernel defined directly in the space of spike trains. The Schoenberg kernel maps the spike train to an RKHS and allows linear algorithm to control the nonlinear neural system without the danger of converging to local minima. During operation, the adaptation of the controller minimizes a difference defined in the spike train RKHS between the system and the target response and keeps the inverse controller close to the inverse of the current neural circuit, which enables adapting to neural perturbations. The results on a realistic synthetic neural circuit show that the inverse controller based on the Schoenberg kernel outperforms the decoding accuracy of other models based on the conventional rate

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

    Science.gov (United States)

    Peng, Jinzhu; Dubay, Rickey

    2011-10-01

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

  12. Adaptive Global Sliding Mode Control for MEMS Gyroscope Using RBF Neural Network

    Directory of Open Access Journals (Sweden)

    Yundi Chu

    2015-01-01

    Full Text Available An adaptive global sliding mode control (AGSMC using RBF neural network (RBFNN is proposed for the system identification and tracking control of micro-electro-mechanical system (MEMS gyroscope. Firstly, a new kind of adaptive identification method based on the global sliding mode controller is designed to update and estimate angular velocity and other system parameters of MEMS gyroscope online. Moreover, the output of adaptive neural network control is used to adjust the switch gain of sliding mode control dynamically to approach the upper bound of unknown disturbances. In this way, the switch item of sliding mode control can be converted to the output of continuous neural network which can weaken the chattering in the sliding mode control in contrast to the conventional fixed gain sliding mode control. Simulation results show that the designed control system can get satisfactory tracking performance and effective estimation of unknown parameters of MEMS gyroscope.

  13. Distributed Recurrent Neural Forward Models with Neural Control for Complex Locomotion in Walking Robots

    DEFF Research Database (Denmark)

    Dasgupta, Sakyasingha; Goldschmidt, Dennis; Wörgötter, Florentin

    2015-01-01

    Walking animals, like stick insects, cockroaches or ants, demonstrate a fascinating range of locomotive abilities and complex behaviors. The locomotive behaviors can consist of a variety of walking patterns along with adaptation that allow the animals to deal with changes in environmental...... conditions, like uneven terrains, gaps, obstacles etc. Biological study has revealed that such complex behaviors are a result of a combination of biomechanics and neural mechanism thus representing the true nature of embodied interactions. While the biomechanics helps maintain flexibility and sustain...... a variety of movements, the neural mechanisms generate movements while making appropriate predictions crucial for achieving adaptation. Such predictions or planning ahead can be achieved by way of internal models that are grounded in the overall behavior of the animal. Inspired by these findings, we present...

  14. Adaptive control using a hybrid-neural model: application to a polymerisation reactor

    Directory of Open Access Journals (Sweden)

    Cubillos F.

    2001-01-01

    Full Text Available This work presents the use of a hybrid-neural model for predictive control of a plug flow polymerisation reactor. The hybrid-neural model (HNM is based on fundamental conservation laws associated with a neural network (NN used to model the uncertain parameters. By simulations, the performance of this approach was studied for a peroxide-initiated styrene tubular reactor. The HNM was synthesised for a CSTR reactor with a radial basis function neural net (RBFN used to estimate the reaction rates recursively. The adaptive HNM was incorporated in two model predictive control strategies, a direct synthesis scheme and an optimum steady state scheme. Tests for servo and regulator control showed excellent behaviour following different setpoint variations, and rejecting perturbations. The good generalisation and training capacities of hybrid models, associated with the simplicity and robustness characteristics of the MPC formulations, make an attractive combination for the control of a polymerisation reactor.

  15. Adaptive RBF Neural Network Control for Three-Phase Active Power Filter

    Directory of Open Access Journals (Sweden)

    Juntao Fei

    2013-05-01

    Full Text Available Abstract An adaptive radial basis function (RBF neural network control system for three-phase active power filter (APF is proposed to eliminate harmonics. Compensation current is generated to track command current so as to eliminate the harmonic current of non-linear load and improve the quality of the power system. The asymptotical stability of the APF system can be guaranteed with the proposed adaptive neural network strategy. The parameters of the neural network can be adaptively updated to achieve the desired tracking task. The simulation results demonstrate good performance, for example showing small current tracking error, reduced total harmonic distortion (THD, improved accuracy and strong robustness in the presence of parameters variation and nonlinear load. It is shown that the adaptive RBF neural network control system for three-phase APF gives better control than hysteresis control.

  16. Self-Organizing Neural Circuits for Sensory-Guided Motor Control

    National Research Council Canada - National Science Library

    Grossberg, Stephen

    1999-01-01

    The reported projects developed mathematical models to explain how self-organizing neural circuits that operate under continuous or intermittent sensory guidance achieve flexible and accurate control of human movement...

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

    Directory of Open Access Journals (Sweden)

    Xiao-Li Li

    2014-01-01

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

  18. Heart rate control with adrenergic blockade: Clinical outcomes in cardiovascular medicine

    Science.gov (United States)

    Feldman, David; Elton, Terry S; Menachemi, Doron M; Wexler, Randy K

    2010-01-01

    The sympathetic nervous system is involved in regulating various cardiovascular parameters including heart rate (HR) and HR variability. Aberrant sympathetic nervous system expression may result in elevated HR or decreased HR variability, and both are independent risk factors for development of cardiovascular disease, including heart failure, myocardial infarction, and hypertension. Epidemiologic studies have established that impaired HR control is linked to increased cardiovascular morbidity and mortality. One successful way of decreasing HR and cardiovascular mortality has been by utilizing β-blockers, because their ability to alter cell signaling at the receptor level has been shown to mitigate the pathogenic effects of sympathetic nervous system hyperactivation. Numerous clinical studies have demonstrated that β-blocker-mediated HR control improvements are associated with decreased mortality in postinfarct and heart failure patients. Although improved HR control benefits have yet to be established in hypertension, both traditional and vasodilating β-blockers exert positive HR control effects in this patient population. However, differences exist between traditional and vasodilating β-blockers; the latter reduce peripheral vascular resistance and exert neutral or positive effects on important metabolic parameters. Clinical evidence suggests that attainment of HR control is an important treatment objective for patients with cardiovascular conditions, and vasodilating β-blocker efficacy may aid in accomplishing improved outcomes. PMID:20539841

  19. Adaptive Neural Network Sliding Mode Control for Quad Tilt Rotor Aircraft

    Directory of Open Access Journals (Sweden)

    Yanchao Yin

    2017-01-01

    Full Text Available A novel neural network sliding mode control based on multicommunity bidirectional drive collaborative search algorithm (M-CBDCS is proposed to design a flight controller for performing the attitude tracking control of a quad tilt rotors aircraft (QTRA. Firstly, the attitude dynamic model of the QTRA concerning propeller tension, channel arm, and moment of inertia is formulated, and the equivalent sliding mode control law is stated. Secondly, an adaptive control algorithm is presented to eliminate the approximation error, where a radial basis function (RBF neural network is used to online regulate the equivalent sliding mode control law, and the novel M-CBDCS algorithm is developed to uniformly update the unknown neural network weights and essential model parameters adaptively. The nonlinear approximation error is obtained and serves as a novel leakage term in the adaptations to guarantee the sliding surface convergence and eliminate the chattering phenomenon, which benefit the overall attitude control performance for QTRA. Finally, the appropriate comparisons among the novel adaptive neural network sliding mode control, the classical neural network sliding mode control, and the dynamic inverse PID control are examined, and comparative simulations are included to verify the efficacy of the proposed control method.

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

    Directory of Open Access Journals (Sweden)

    Haizhong Chen

    2013-01-01

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

  1. Model-Following Controller Based on Neural Network for Variable Displacement Pump

    Science.gov (United States)

    Chu, Ming-Hui; Kang, Yuan; Chang, Yih-Fong; Liu, Yuan-Liang; Chang, Chuan-Wei

    The variable displacement axial piston pump (VDAPP) is inherently nonlinear, time variant and subjected to load disturbance. The controls of flow and pressure of VDAPP are achieved by changing the swashplate angle. The swashplate actuators are controlled by an electro-hydraulic proportional valve (EHPV). It is reasonable for swashplate angle of a VDAPP to employ neural network based on adaptive control. In this study, the nonlinear model of the VDAPP with a three-way electro-hydraulic proportional valve is proposed, and a neural network model-following controller is designed to control the swashplate swivel angle. The time response for the swashplate angle is analyzed by simulation and experiment, and a favorable model-following characteristic is achieved. The proposed neural controller can conduct nonlinear control in VDAPP, enhance adaptability and robustness, and improve the performance of the control system.

  2. Research on Environmental Adjustment of Cloud Ranch Based on BP Neural Network PID Control

    Science.gov (United States)

    Ren, Jinzhi; Xiang, Wei; Zhao, Lin; Wu, Jianbo; Huang, Lianzhen; Tu, Qinggang; Zhao, Heming

    2018-01-01

    In order to make the intelligent ranch management mode replace the traditional artificial one gradually, this paper proposes a pasture environment control system based on cloud server, and puts forward the PID control algorithm based on BP neural network to control temperature and humidity better in the pasture environment. First, to model the temperature and humidity (controlled object) of the pasture, we can get the transfer function. Then the traditional PID control algorithm and the PID one based on BP neural network are applied to the transfer function. The obtained step tracking curves can be seen that the PID controller based on BP neural network has obvious superiority in adjusting time and error, etc. This algorithm, calculating reasonable control parameters of the temperature and humidity to control environment, can be better used in the cloud service platform.

  3. Transformation of Neural State Space Models into LFT Models for Robust Control Design

    DEFF Research Database (Denmark)

    Bendtsen, Jan Dimon; Trangbæk, Klaus

    2000-01-01

    This paper considers the extraction of linear state space models and uncertainty models from neural networks trained as state estimators with direct application to robust control. A new method for writing a neural state space model in a linear fractional transformation form in a non-conservative ......-conservative way is proposed, and it is demonstrated how a standard robust control law can be designed for a system described by means of a multi layer perceptron....

  4. Three-Level Direct Torque Control Based on Artificial Neural Network of Double Star Synchronous Machine

    Directory of Open Access Journals (Sweden)

    Elakhdar BENYOUSSEF

    2014-02-01

    Full Text Available This paper presents a direct torque control strategy for double star synchronous machine fed by two three-level inverters. The analysis of the torque and the stator flux linkage reference frame shows that the concept of direct torque control can be extended easily to double star synchronous machine. The proposed approach consists to replace the switching tables by one artificial neural networks controller. The output switching states vectors of the artificial neural networks controller are used to control the two three-level inverters. Simulations results are given to show the effectiveness and the robustness of the suggested control method.

  5. Bifurcation and Hybrid Control for A Simple Hopfield Neural Networks with Delays

    Directory of Open Access Journals (Sweden)

    Zisen Mao

    2013-01-01

    Full Text Available A detailed analysis on the Hopf bifurcation of a delayed Hopfield neural network is given. Moreover, a new hybrid control strategy is proposed, in which time-delayed state feedback and parameter perturbation are used to control the Hopf bifurcation of the model. Numerical simulation results confirm that the new hybrid controller using time delay is efficient in controlling Hopf bifurcation.

  6. Tracking error constrained robust adaptive neural prescribed performance control for flexible hypersonic flight vehicle

    Directory of Open Access Journals (Sweden)

    Zhonghua Wu

    2017-02-01

    Full Text Available A robust adaptive neural control scheme based on a back-stepping technique is developed for the longitudinal dynamics of a flexible hypersonic flight vehicle, which is able to ensure the state tracking error being confined in the prescribed bounds, in spite of the existing model uncertainties and actuator constraints. Minimal learning parameter technique–based neural networks are used to estimate the model uncertainties; thus, the amount of online updated parameters is largely lessened, and the prior information of the aerodynamic parameters is dispensable. With the utilization of an assistant compensation system, the problem of actuator constraint is overcome. By combining the prescribed performance function and sliding mode differentiator into the neural back-stepping control design procedure, a composite state tracking error constrained adaptive neural control approach is presented, and a new type of adaptive law is constructed. As compared with other adaptive neural control designs for hypersonic flight vehicle, the proposed composite control scheme exhibits not only low-computation property but also strong robustness. Finally, two comparative simulations are performed to demonstrate the robustness of this neural prescribed performance controller.

  7. Effect of antioxidant vitamin supplementation on cardiovascular outcomes: a meta-analysis of randomized controlled trials.

    Directory of Open Access Journals (Sweden)

    Yizhou Ye

    Full Text Available BACKGROUND: Antioxidant vitamin (vitamin E, beta-carotene, and vitamin C are widely used for preventing major cardiovascular outcomes. However, the effect of antioxidant vitamin on cardiovascular events remains unclear. METHODOLOGY AND PRINCIPAL FINDINGS: We searched PubMed, EmBase, the Cochrane Central Register of Controlled Trials, and the proceedings of major conferences for relevant literature. Eligible studies were randomized controlled trials that reported on the effects of antioxidant vitamin on cardiovascular outcomes as compared to placebo. Outcomes analyzed were major cardiovascular events, myocardial infarction, stroke, cardiac death, total death, and any possible adverse events. We used the I(2 statistic to measure heterogeneity between trials and calculated risk estimates for cardiovascular outcomes with random-effect meta-analysis. Independent extraction was performed by two reviewers and consensus was reached. Of 293 identified studies, we included 15 trials reporting data on 188209 participants. These studies reported 12749 major cardiovascular events, 6699 myocardial infarction, 3749 strokes, 14122 total death, and 5980 cardiac deaths. Overall, antioxidant vitamin supplementation as compared to placebo had no effect on major cardiovascular events (RR, 1.00; 95%CI, 0.96-1.03, myocardial infarction (RR, 0.98; 95%CI, 0.92-1.04, stroke (RR, 0.99; 95%CI, 0.93-1.05, total death (RR, 1.03; 95%CI, 0.98-1.07, cardiac death (RR, 1.02; 95%CI, 0.97-1.07, revascularization (RR, 1.00; 95%CI, 0.95-1.05, total CHD (RR, 0.96; 95%CI, 0.87-1.05, angina (RR, 0.98; 95%CI, 0.90-1.07, and congestive heart failure (RR, 1.07; 95%CI, 0.96 to 1.19. CONCLUSION/SIGNIFICANCE: Antioxidant vitamin supplementation has no effect on the incidence of major cardiovascular events, myocardial infarction, stroke, total death, and cardiac death.

  8. Rethinking neural efficiency : Effects of controlling for strategy use

    NARCIS (Netherlands)

    Toffanin, Paolo; Johnson, Addie; de Jong, Ritske; Martens, Sander

    2007-01-01

    A sentence verification task (SVT) was used to test whether differences in neural activation patterns that have been attributed to IQ may actually depend on differential strategy use between IQ groups. Electroencephalograms were recorded from 14 low (89

  9. Review article: Autonomous neural inflammatory reflex and control of

    African Journals Online (AJOL)

    Inflammation is common pathology associated with infections and other diseases process that lead to non specific sickness behaviours. Identification of autonomous neural inflammatory reflex that is regulated through autonomic nervous system and their receptors give a way forward on how this can be use as therapeutic ...

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

    Science.gov (United States)

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

    2009-01-01

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

  11. Controlling the elements: an optogenetic approach to understanding the neural circuits of fear.

    Science.gov (United States)

    Johansen, Joshua P; Wolff, Steffen B E; Lüthi, Andreas; LeDoux, Joseph E

    2012-06-15

    Neural circuits underlie our ability to interact in the world and to learn adaptively from experience. Understanding neural circuits and how circuit structure gives rise to neural firing patterns or computations is fundamental to our understanding of human experience and behavior. Fear conditioning is a powerful model system in which to study neural circuits and information processing and relate them to learning and behavior. Until recently, technological limitations have made it difficult to study the causal role of specific circuit elements during fear conditioning. However, newly developed optogenetic tools allow researchers to manipulate individual circuit components such as anatomically or molecularly defined cell populations, with high temporal precision. Applying these tools to the study of fear conditioning to control specific neural subpopulations in the fear circuit will facilitate a causal analysis of the role of these circuit elements in fear learning and memory. By combining this approach with in vivo electrophysiological recordings in awake, behaving animals, it will also be possible to determine the functional contribution of specific cell populations to neural processing in the fear circuit. As a result, the application of optogenetics to fear conditioning could shed light on how specific circuit elements contribute to neural coding and to fear learning and memory. Furthermore, this approach may reveal general rules for how circuit structure and neural coding within circuits gives rise to sensory experience and behavior. Copyright © 2012 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

  12. Imaging the neural circuitry and chemical control of aggressive motivation

    Directory of Open Access Journals (Sweden)

    Blanchard D Caroline

    2008-11-01

    Full Text Available Abstract Background With the advent of functional magnetic resonance imaging (fMRI in awake animals it is possible to resolve patterns of neuronal activity across the entire brain with high spatial and temporal resolution. Synchronized changes in neuronal activity across multiple brain areas can be viewed as functional neuroanatomical circuits coordinating the thoughts, memories and emotions for particular behaviors. To this end, fMRI in conscious rats combined with 3D computational analysis was used to identifying the putative distributed neural circuit involved in aggressive motivation and how this circuit is affected by drugs that block aggressive behavior. Results To trigger aggressive motivation, male rats were presented with their female cage mate plus a novel male intruder in the bore of the magnet during image acquisition. As expected, brain areas previously identified as critical in the organization and expression of aggressive behavior were activated, e.g., lateral hypothalamus, medial basal amygdala. Unexpected was the intense activation of the forebrain cortex and anterior thalamic nuclei. Oral administration of a selective vasopressin V1a receptor antagonist SRX251 or the selective serotonin reuptake inhibitor fluoxetine, drugs that block aggressive behavior, both caused a general suppression of the distributed neural circuit involved in aggressive motivation. However, the effect of SRX251, but not fluoxetine, was specific to aggression as brain activation in response to a novel sexually receptive female was unaffected. Conclusion The putative neural circuit of aggressive motivation identified with fMRI includes neural substrates contributing to emotional expression (i.e. cortical and medial amygdala, BNST, lateral hypothalamus, emotional experience (i.e. hippocampus, forebrain cortex, anterior cingulate, retrosplenial cortex and the anterior thalamic nuclei that bridge the motor and cognitive components of aggressive responding

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

    Science.gov (United States)

    Williams-Hayes, Peggy S.

    2004-01-01

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

  14. Design of an Adaptive-Neural Network Attitude Controller of a Satellite using Reaction Wheels

    Directory of Open Access Journals (Sweden)

    Abbas Ajorkar

    2015-04-01

    Full Text Available In this paper, an adaptive attitude control algorithm is developed based on neural network for a satellite using four reaction wheels in a tetrahedron configuration. Then, an attitude control based on feedback linearization control has been designed and uncertainties in the moment of inertia matrix and disturbances torque have been considered. In order to eliminate the effect of these uncertainties, a multilayer neural network with back-propagation law is designed. In this structure, the parameters of the moment of inertia matrix and external disturbances are estimated and used in feedback linearization control law. Finally, the performance of the designed attitude controller is investigated by several simulations.

  15. Single neural adaptive controller and neural network identifier based on PSO algorithm for spherical actuators with 3D magnet array

    Science.gov (United States)

    Yan, Liang; Zhang, Lu; Zhu, Bo; Zhang, Jingying; Jiao, Zongxia

    2017-10-01

    Permanent magnet spherical actuator (PMSA) is a multi-variable featured and inter-axis coupled nonlinear system, which unavoidably compromises its motion control implementation. Uncertainties such as external load and friction torque of ball bearing and manufacturing errors also influence motion performance significantly. Therefore, the objective of this paper is to propose a controller based on a single neural adaptive (SNA) algorithm and a neural network (NN) identifier optimized with a particle swarm optimization (PSO) algorithm to improve the motion stability of PMSA with three-dimensional magnet arrays. The dynamic model and computed torque model are formulated for the spherical actuator, and a dynamic decoupling control algorithm is developed. By utilizing the global-optimization property of the PSO algorithm, the NN identifier is trained to avoid locally optimal solution and achieve high-precision compensations to uncertainties. The employment of the SNA controller helps to reduce the effect of compensation errors and convert the system to a stable one, even if there is difference between the compensations and uncertainties due to external disturbances. A simulation model is established, and experiments are conducted on the research prototype to validate the proposed control algorithm. The amplitude of the parameter perturbation is set to 5%, 10%, and 15%, respectively. The strong robustness of the proposed hybrid algorithm is validated by the abundant simulation data. It shows that the proposed algorithm can effectively compensate the influence of uncertainties and eliminate the effect of inter-axis couplings of the spherical actuator.

  16. Concise Neural Nonaffine Control of Air-Breathing Hypersonic Vehicles Subject to Parametric Uncertainties

    Directory of Open Access Journals (Sweden)

    Xiangwei Bu

    2017-01-01

    Full Text Available In this paper, a novel simplified neural control strategy is proposed for the longitudinal dynamics of an air-breathing hypersonic vehicle (AHV directly using nonaffine models instead of affine ones. For the velocity dynamics, an adaptive neural controller is devised based on a minimal-learning parameter (MLP technique for the sake of decreasing computational loads. The altitude dynamics is rewritten as a pure feedback nonaffine formulation, for which a novel concise neural control approach is achieved without backstepping. The special contributions are that the control architecture is concise and the computational cost is low. Moreover, the exploited controller possesses good practicability since there is no need for affine models. The semiglobally uniformly ultimate boundedness of all the closed-loop system signals is guaranteed via Lyapunov stability theory. Finally, simulation results are presented to validate the effectiveness of the investigated control methodology in the presence of parametric uncertainties.

  17. Resonant hopping of a robot controlled by an artificial neural oscillator.

    Science.gov (United States)

    Pelc, Evan H; Daley, Monica A; Ferris, Daniel P

    2008-06-01

    The bouncing gaits of terrestrial animals (hopping, running, trotting) can be modeled as a hybrid dynamic system, with spring-mass dynamics during stance and ballistic motion during the aerial phase. We used a simple hopping robot controlled by an artificial neural oscillator to test the ability of the neural oscillator to adaptively drive this hybrid dynamic system. The robot had a single joint, actuated by an artificial pneumatic muscle in series with a tendon spring. We examined how the oscillator-robot system responded to variation in two neural control parameters: descending neural drive and neuromuscular gain. We also tested the ability of the oscillator-robot system to adapt to variations in mechanical properties by changing the series and parallel spring stiffnesses. Across a 100-fold variation in both supraspinal gain and muscle gain, hopping frequency changed by less than 10%. The neural oscillator consistently drove the system at the resonant half-period for the stance phase, and adapted to a new resonant half-period when the muscle series and parallel stiffnesses were altered. Passive cycling of elastic energy in the tendon accounted for 70-79% of the mechanical work done during each hop cycle. Our results demonstrate that hopping dynamics were largely determined by the intrinsic properties of the mechanical system, not the specific choice of neural oscillator parameters. The findings provide the first evidence that an artificial neural oscillator will drive a hybrid dynamic system at partial resonance.

  18. Correlates of preclinical cardiovascular disease in Indigenous and Non-Indigenous Australians: a case control study

    Directory of Open Access Journals (Sweden)

    Shaw A Andrew

    2008-07-01

    Full Text Available Abstract Background The high frequency of premature death from cardiovascular disease in indigenous Australians is often attributed to the high prevalence of risk factors, especially type II diabetes mellitus (DM. We evaluated the relationship of ethnicity to atherosclerotic burden, as evidenced by carotid intima-media thickness (IMT, independent of risk factor status. Methods We studied 227 subjects (147 men; 50 ± 13 y: 119 indigenous subjects with (IDM, n = 54, and without DM (InDM, n = 65, 108 Caucasian subjects with (CDM, n = 52, and without DM (CnDM, n = 56. IMT was measured according to standard methods and compared with clinical data and cardiovascular risk factors. Results In subjects both with and without DM, IMT was significantly greater in indigenous subjects. There were no significant differences in gender, body mass index (BMI, systolic blood pressure (SBP, or diastolic blood pressure (DBP between any of the groups, and subjects with DM showed no difference in plasma HbA1c. Cardiovascular risk factors were significantly more prevalent in indigenous subjects. Nonetheless, ethnicity (β = -0.34; p Conclusion Ethnicity appears to be an independent correlate of preclinical cardiovascular disease, even after correction for the high prevalence of cardiovascular risk factors in indigenous Australians. Standard approaches to control currently known risk factors are vital to reduce the burden of cardiovascular disease, but in themselves may be insufficient to fully address the high prevalence in this population.

  19. Cardiovascular and sympathetic neural responses to handgrip and cold pressor stimuli in humans before, during and after spaceflight

    Science.gov (United States)

    Fu, Qi; Levine, Benjamin D.; Pawelczyk, James A.; Ertl, Andrew C.; Diedrich, Andre; Cox, James F.; Zuckerman, Julie H.; Ray, Chester A.; Smith, Michael L.; Iwase, Satoshi; hide

    2002-01-01

    Astronauts returning to Earth have reduced orthostatic tolerance and exercise capacity. Alterations in autonomic nervous system and neuromuscular function after spaceflight might contribute to this problem. In this study, we tested the hypothesis that exposure to microgravity impairs autonomic neural control of sympathetic outflow in response to peripheral afferent stimulation produced by handgrip and a cold pressor test in humans. We studied five astronauts approximately 72 and 23 days before, and on landing day after the 16 day Neurolab (STS-90) space shuttle mission, and four of the astronauts during flight (day 12 or 13). Heart rate, arterial pressure and peroneal muscle sympathetic nerve activity (MSNA) were recorded before and during static handgrip sustained to fatigue at 40 % of maximum voluntary contraction, followed by 2 min of circulatory arrest pre-, in- and post-flight. The cold pressor test was applied only before (five astronauts) and during flight (day 12 or 13, four astronauts). Mean (+/- S.E.M.) baseline heart rates and arterial pressures were similar among pre-, in- and post-flight measurements. At the same relative fatiguing force, the peak systolic pressure and mean arterial pressure during static handgrip were not different before, during and after spaceflight. The peak diastolic pressure tended to be higher post- than pre-flight (112 +/- 6 vs. 99 +/- 5 mmHg, P = 0.088). Contraction-induced rises in heart rate were similar pre-, in- and post-flight. MSNA was higher post-flight in all subjects before static handgrip (26 +/- 4 post- vs. 15 +/- 4 bursts min(-1) pre-flight, P = 0.017). Contraction-evoked peak MSNA responses were not different before, during, and after spaceflight (41 +/- 4, 38 +/- 5 and 46 +/- 6 bursts min(-1), all P > 0.05). MSNA during post-handgrip circulatory arrest was higher post- than pre- or in-flight (41 +/- 1 vs. 33 +/- 3 and 30 +/- 5 bursts min(-1), P = 0.038 and 0.036). Similarly, responses of MSNA and blood pressure

  20. Exponential stabilization and synchronization for fuzzy model of memristive neural networks by periodically intermittent control.

    Science.gov (United States)

    Yang, Shiju; Li, Chuandong; Huang, Tingwen

    2016-03-01

    The problem of exponential stabilization and synchronization for fuzzy model of memristive neural networks (MNNs) is investigated by using periodically intermittent control in this paper. Based on the knowledge of memristor and recurrent neural network, the model of MNNs is formulated. Some novel and useful stabilization criteria and synchronization conditions are then derived by using the Lyapunov functional and differential inequality techniques. It is worth noting that the methods used in this paper are also applied to fuzzy model for complex networks and general neural networks. Numerical simulations are also provided to verify the effectiveness of theoretical results. Copyright © 2015 Elsevier Ltd. All rights reserved.

  1. Neural network for solving Nash equilibrium problem in application of multiuser power control.

    Science.gov (United States)

    He, Xing; Yu, Junzhi; Huang, Tingwen; Li, Chuandong; Li, Chaojie

    2014-09-01

    In this paper, based on an equivalent mixed linear complementarity problem, we propose a neural network to solve multiuser power control optimization problems (MPCOP), which is modeled as the noncooperative Nash game in modern digital subscriber line (DSL). If the channel crosstalk coefficients matrix is positive semidefinite, it is shown that the proposed neural network is stable in the sense of Lyapunov and global convergence to a Nash equilibrium, and the Nash equilibrium is unique if the channel crosstalk coefficients matrix is positive definite. Finally, simulation results on two numerical examples show the effectiveness and performance of the proposed neural network. Copyright © 2014 Elsevier Ltd. All rights reserved.

  2. 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.

  3. A Predictive Neural Network-Based Cascade Control for pH Reactors

    Directory of Open Access Journals (Sweden)

    Mujahed AlDhaifallah

    2016-01-01

    Full Text Available This paper is concerned with the development of predictive neural network-based cascade control for pH reactors. The cascade structure consists of a master control loop (fuzzy proportional-integral and a slave one (predictive neural network. The master loop is chosen to be more accurate but slower than the slave one. The strong features found in cascade structure have been added to the inherent features in model predictive neural network. The neural network is used to alleviate modeling difficulties found with pH reactor and to predict its behavior. The parameters of predictive algorithm are determined using an optimization algorithm. The effectiveness and feasibility of the proposed design have been demonstrated using MatLab.

  4. A Physiological Neural Network for Saccadic Eye Movement Control

    Science.gov (United States)

    1994-04-01

    posterior eye field (striate, prestriate, and inferior parietal cortices). Both the neural circuit and the ocu- lomotor plant will be described in... motoneuron . There are two types of burst neurons in the PPRF called the long-lead burst neuron (LLBN) and a medium-lead burst neuron (MLBN); during...neurons (EBN) and the inhibitory burst neurons (IBN). The EBN and IBN label describes the synaptic ac- tivity upon the motoneurons ; the EBN excite

  5. Active random noise control using adaptive learning rate neural networks with an immune feedback law

    Science.gov (United States)

    Sasaki, Minoru; Kuribayashi, Takumi; Ito, Satoshi

    2005-12-01

    In this paper an active random noise control using adaptive learning rate neural networks with an immune feedback law is presented. The adaptive learning rate strategy increases the learning rate by a small constant if the current partial derivative of the objective function with respect to the weight and the exponential average of the previous derivatives have the same sign, otherwise the learning rate is decreased by a proportion of its value. The use of an adaptive learning rate attempts to keep the learning step size as large as possible without leading to oscillation. In the proposed method, because of the immune feedback law change a learning rate of the neural networks individually and adaptively, it is expected that a cost function minimize rapidly and training time is decreased. Numerical simulations and experiments of active random noise control with the transfer function of the error path will be performed, to validate the convergence properties of the adaptive learning rate Neural Networks with the immune feedback law. Control results show that adaptive learning rate Neural Networks control structure can outperform linear controllers and conventional neural network controller for the active random noise control.

  6. Predictive and Neural Predictive Control of Uncertain Systems

    Science.gov (United States)

    Kelkar, Atul G.

    2000-01-01

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

  7. Error mapping controller: a closed loop neuroprosthesis controlled by artificial neural networks

    Directory of Open Access Journals (Sweden)

    De Momi Elena

    2006-10-01

    Full Text Available Abstract Background The design of an optimal neuroprostheses controller and its clinical use presents several challenges. First, the physiological system is characterized by highly inter-subjects varying properties and also by non stationary behaviour with time, due to conditioning level and fatigue. Secondly, the easiness to use in routine clinical practice requires experienced operators. Therefore, feedback controllers, avoiding long setting procedures, are required. Methods The error mapping controller (EMC here proposed uses artificial neural networks (ANNs both for the design of an inverse model and of a feedback controller. A neuromuscular model is used to validate the performance of the controllers in simulations. The EMC performance is compared to a Proportional Integral Derivative (PID included in an anti wind-up scheme (called PIDAW and to a controller with an ANN as inverse model and a PID in the feedback loop (NEUROPID. In addition tests on the EMC robustness in response to variations of the Plant parameters and to mechanical disturbances are carried out. Results The EMC shows improvements with respect to the other controllers in tracking accuracy, capability to prolong exercise managing fatigue, robustness to parameter variations and resistance to mechanical disturbances. Conclusion Different from the other controllers, the EMC is capable of balancing between tracking accuracy and mapping of fatigue during the exercise. In this way, it avoids overstressing muscles and allows a considerable prolongation of the movement. The collection of the training sets does not require any particular experimental setting and can be introduced in routine clinical practice.

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

    DEFF Research Database (Denmark)

    Bendtsen, Jan Dimon; Stoustrup, Jakob

    2003-01-01

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

  9. Dynamic control of ROV`s making use of the neural network concept

    Energy Technology Data Exchange (ETDEWEB)

    Ooi, Tadashi; Yoshida, Yuki; Takahashi, Yoshiaki; Kidoushi, Hideki [Ishikawajima-Harima Heavy Industries Co., Ltd., Tokyo (Japan)

    1994-12-31

    An attempt is made to combine the classical controller with the concept of neural network, the result of which is a control system that they have named the Robust Adaptive Neural-net Controller (RANC). The RANC identifies the dynamic characteristics of the remotely operated vehicle (ROV) including its ambient environment involving cyclic disturbances such as forces induced by waves, and organizes automatically an optimized controller. A tank experiment is described in which the RANC is set to maintain a model ROV at a prescribed depth of water under artificially generated wave disturbance.

  10. Behavior Emergence in Autonomous Robot Control by Means of Evolutionary Neural Networks

    Science.gov (United States)

    Neruda, Roman; Slušný, Stanislav; Vidnerová, Petra

    We study the emergence of intelligent behavior of a simple mobile robot. Robot control system is realized by mechanisms based on neural networks and evolutionary algorithms. The evolutionary algorithm is responsible for the adaptation of a neural network parameters based on the robot's performance in a simulated environment. In experiments, we demonstrate the performance of evolutionary algorithm on selected problems, namely maze exploration and discrimination of walls and cylinders. A comparison of different networks architectures is presented and discussed.

  11. Rotor Resistance Online Identification of Vector Controlled Induction Motor Based on Neural Network

    OpenAIRE

    Bo Fan; Zhixin Yang; Wei Xu; Xianbo Wang

    2014-01-01

    Rotor resistance identification has been well recognized as one of the most critical factors affecting the theoretical study and applications of AC motor’s control for high performance variable frequency speed adjustment. This paper proposes a novel model for rotor resistance parameters identification based on Elman neural networks. Elman recurrent neural network is capable of performing nonlinear function approximation and possesses the ability of time-variable characteristic adaptation. Tho...

  12. Orthostatic stress is necessary to maintain the dynamic range of cardiovascular control in space

    Science.gov (United States)

    Baisch, J. F.; Wolfram, G.; Beck, L.; Drummer, C.; Stormer, I.; Buckey, J.; Blomqvist, G.

    2000-01-01

    In the upright position, gravity fills the low-pressure systems of human circulation with blood and interstitial fluid in the sections below the diaphragm. Without gravity one pressure component in the vessels disappears and the relationship between hydrostatic pressure and oncotic pressure, which regulates fluid passage across the capillary endothelium in the terminal vascular bed, shifts constantly. The visible consequences of this are a puffy face and "bird" legs. The plasma volume shrinks in space and the range of cardiovascular control is reduced. When they stand up for the first time after landing, 30-50% of astronauts suffer from orthostatic intolerance. It remains unclear whether microgravity impairs cardiovascular reflexes, or whether it is the altered volume status that causes the cardiovascular instability following space flight. Lower body negative pressure was used in several space missions to stimulate the cardiovascular reflexes before, during and after a space flight. The results show that cardiovascular reflexes are maintained in microgravity. However, the astronauts' volume status changed in space, towards a volume-retracted state, as measurements of fluid-regulating hormones have shown. It can be hypothesized that the control of circulation and body fluid homeostasis in humans is adapted to their upright posture in the Earth's gravitational field. Autonomic control regulates fluid distribution to maintain the blood pressure in that posture, which most of us have to cope with for two-thirds of the day. A determined amount of interstitial volume is necessary to maintain the dynamic range of cardiovascular control in the upright posture; otherwise orthostatic intolerance may occur more often.

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

    Science.gov (United States)

    Xu, Bin; Yang, Chenguang; Pan, Yongping

    2015-10-01

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

  14. Finite-Time Stabilization and Adaptive Control of Memristor-Based Delayed Neural Networks.

    Science.gov (United States)

    Wang, Leimin; Shen, Yi; Zhang, Guodong

    Finite-time stability problem has been a hot topic in control and system engineering. This paper deals with the finite-time stabilization issue of memristor-based delayed neural networks (MDNNs) via two control approaches. First, in order to realize the stabilization of MDNNs in finite time, a delayed state feedback controller is proposed. Then, a novel adaptive strategy is applied to the delayed controller, and finite-time stabilization of MDNNs can also be achieved by using the adaptive control law. Some easily verified algebraic criteria are derived to ensure the stabilization of MDNNs in finite time, and the estimation of the settling time functional is given. Moreover, several finite-time stability results as our special cases for both memristor-based neural networks (MNNs) without delays and neural networks are given. Finally, three examples are provided for the illustration of the theoretical results.Finite-time stability problem has been a hot topic in control and system engineering. This paper deals with the finite-time stabilization issue of memristor-based delayed neural networks (MDNNs) via two control approaches. First, in order to realize the stabilization of MDNNs in finite time, a delayed state feedback controller is proposed. Then, a novel adaptive strategy is applied to the delayed controller, and finite-time stabilization of MDNNs can also be achieved by using the adaptive control law. Some easily verified algebraic criteria are derived to ensure the stabilization of MDNNs in finite time, and the estimation of the settling time functional is given. Moreover, several finite-time stability results as our special cases for both memristor-based neural networks (MNNs) without delays and neural networks are given. Finally, three examples are provided for the illustration of the theoretical results.

  15. Optimal feedback control successfully explains changes in neural modulations during experiments with brain-machine interfaces

    Directory of Open Access Journals (Sweden)

    Miriam eZacksenhouse

    2015-05-01

    Full Text Available Recent experiments with brain-machine-interfaces (BMIs indicate that the extent of neural modulations increased abruptly upon starting to operate the interface, and especially after the monkey stopped moving its hand. In contrast, neural modulations that are correlated with the kinematics of the movement remained relatively unchanged. Here we demonstrate that similar changes are produced by simulated neurons that encode the relevant signals generated by an optimal feedback controller during simulated BMI experiments. The optimal feedback controller relies on state estimation that integrates both visual and proprioceptive feedback with prior estimations from an internal model. The processing required for optimal state estimation and control were conducted in the state-space, and neural recording was simulated by modeling two populations of neurons that encode either only the estimated state or also the control signal. Spike counts were generated as realizations of doubly stochastic Poisson processes with linear tuning curves. The model successfully reconstructs the main features of the kinematics and neural activity during regular reaching movements. Most importantly, the activity of the simulated neurons successfully reproduces the observed changes in neural modulations upon switching to brain control. Further theoretical analysis and simulations indicate that increasing the process noise during normal reaching movement results in similar changes in neural modulations. Thus we conclude that the observed changes in neural modulations during BMI experiments can be attributed to increasing process noise associated with the imperfect BMI filter, and, more directly, to the resulting increase in the variance of the encoded signals associated with state estimation and the required control signal.

  16. Tracking Control for Two Link Robot Arm Using Neural Networkwith Simultaneous Perturbation Learning Rule

    Science.gov (United States)

    Maeda, Yutaka; Onishi, Hidenori

    Neural networks are widely used for many fields including control problems. When we use a neural network as a controller, the direct inverse control scheme is the simplest approach. However, the scheme using the back-propagation method requires information, e.g. Jacobian, of an objective. If the objective is nonlinear, it will be difficult to know the information. Using the simultaneous perturbation learning rule, we can construct a neuro-controller, which is an inverse system of the objective or a proper controller, under the direct inverse control scheme without any information on the objective. In this paper, we report a tracking control for a two link robot arm using a neuro-controller with the simultaneous perturbation learning rule. Details of the proposed control scheme are described. Some simulation results and a result for a real two link robot arm show that the control scheme is feasible and useful.

  17. A definition of normovolaemia and consequences for cardiovascular control during orthostatic and environmental stress

    NARCIS (Netherlands)

    Truijen, J.; Bundgaard-Nielsen, M.; van Lieshout, J.J.

    2010-01-01

    The Frank-Starling mechanism describes the relationship between stroke volume and preload to the heart, or the volume of blood that is available to the heart-the central blood volume. Understanding the role of the central blood volume for cardiovascular control has been complicated by the fact that

  18. Homocysteine status and cardiovascular risk factors in patients with psoriasis: a case-control study.

    LENUS (Irish Health Repository)

    Tobin, A-M

    2012-02-01

    BACKGROUND: Psoriasis is a hyperproliferative, cutaneous disorder with the potential to lower levels of folate. This may result in raised levels of homocysteine, an independent risk factor for the development of cardiovascular disease. OBJECTIVE: A study was conducted to compare levels of red-cell folate (RCF) and homocysteine in patients with psoriasis and in healthy controls. Levels of homocysteine were also examined in the context of other major cardiovascular risk factors. METHODS: In total, 20 patients with psoriasis and 20 controls had their RCF, homo-cysteine and other conventional cardiovascular risk factors assessed. RESULTS: Patients with psoriasis had a trend towards lower levels of RCF. Significantly raised levels of homocysteine were found in patients with psoriasis compared with controls (P = 0.007). There was no correlation between homocysteine levels, RCF levels or disease activity as measured by the Psoriasis Area and Severity Index. Patients with psoriasis had higher body mass index (P < 0.004) and higher systolic blood pressure (P < 0.001) than controls. This may contribute to the excess cardiovascular mortality observed in patients with psoriasis.

  19. Self-Efficacy and Perceived Control in the Prevention of Cardiovascular Disease

    NARCIS (Netherlands)

    Carpi Ballester, Amparo; Gonzalez Navarro, Pilar; Zurriaga Llorens, Rosario; Marzo Campos, Juan Carlos; Buunk, Abraham P.

    2010-01-01

    From the Theory of Planed Behaviour (TPB), the aim of this study is to analyse the effect of self-efficacy and perceived control on intention and preventive behaviors of cardiovascular disease. To this end, 359 participants were evaluated in an empirical study. Data were analysed using the

  20. Bidirectional asymmetry in the neurovisceral communication for the cardiovascular control: New insights

    Directory of Open Access Journals (Sweden)

    Prieto I

    2017-07-01

    Full Text Available The cardiovascular control involves a bidirectional functional connection between the brain and heart. We hypothesize that this connection could be extended to other organs using endocrine and autonomic nervous systems (ANS as communication pathways. This implies a neuroendocrine interaction controlling particularly the cardiovascular function where the enzymatic cascade of the renin-angiotensin system (RAS plays an essential role. It acts not only through its classic endocrine connection but also the ANS. In addition, the brain is functionally, anatomically, and neurochemically asymmetric. Moreover, this asymmetry goes even beyond the brain and it includes both sides of the peripheral nervous and neuroendocrine systems. We revised the available information and analyze the asymmetrical neuroendocrine bidirectional interaction for the cardiovascular control. Negative and positive correlations involving the RAS have been observed between brain, heart, kidney, gut, and plasma in physiologic and pathologic conditions. The central role of the peptides and enzymes of the RAS within this neurovisceral communication, as well as the importance of the asymmetrical distribution of the various RAS components in the pathologies involving this connection, are particularly discussed. In conclusion, there are numerous evidences supporting the existence of a neurovisceral connection with multiorgan involvement that controls, among others, the cardiovascular function. This connection is asymmetrically organized.

  1. Adaptive Sliding Mode Control of MEMS Gyroscope Based on Neural Network Approximation

    Directory of Open Access Journals (Sweden)

    Yuzheng Yang

    2014-01-01

    Full Text Available An adaptive sliding controller using radial basis function (RBF network to approximate the unknown system dynamics microelectromechanical systems (MEMS gyroscope sensor is proposed. Neural controller is proposed to approximate the unknown system model and sliding controller is employed to eliminate the approximation error and attenuate the model uncertainties and external disturbances. Online neural network (NN weight tuning algorithms, including correction terms, are designed based on Lyapunov stability theory, which can guarantee bounded tracking errors as well as bounded NN weights. The tracking error bound can be made arbitrarily small by increasing a certain feedback gain. Numerical simulation for a MEMS angular velocity sensor is investigated to verify the effectiveness of the proposed adaptive neural control scheme and demonstrate the satisfactory tracking performance and robustness.

  2. NEURAL CASCADED WITH FUZZY SCHEME FOR CONTROL OF A HYDROELECTRIC POWER PLANT

    OpenAIRE

    A. Selwin Mich Priyadharson; T. Ramesh Kumar; M. S. Saravanan; C. ThilipKumar; D. Dileepan

    2014-01-01

    A novel design for flow and level control in a hydroelectric power plant using Programmable Logic Controller (PLC)-Human Machine Interface (HMI) and neural cascaded with fuzzy scheme is proposed. This project will focus on design and development of flow and level controller for small scale hydro generating units by implementing gate control based on PLC-HMI with the proposed scheme. The existing control schemes have so many difficulties to manage intrinsic time delay, nonlinearity due to unce...

  3. An Artificial Neural Network Based Robot Controller that Uses Rat’s Brain Signals

    Directory of Open Access Journals (Sweden)

    Marsel Mano

    2013-04-01

    Full Text Available Brain machine interface (BMI has been proposed as a novel technique to control prosthetic devices aimed at restoring motor functions in paralyzed patients. In this paper, we propose a neural network based controller that maps rat’s brain signals and transforms them into robot movement. First, the rat is trained to move the robot by pressing the right and left lever in order to get food. Next, we collect brain signals with four implanted electrodes, two in the motor cortex and two in the somatosensory cortex area. The collected data are used to train and evaluate different artificial neural controllers. Trained neural controllers are employed online to map brain signals and transform them into robot motion. Offline and online classification results of rat’s brain signals show that the Radial Basis Function Neural Networks (RBFNN outperforms other neural networks. In addition, online robot control results show that even with a limited number of electrodes, the robot motion generated by RBFNN matched the motion generated by the left and right lever position.

  4. Modulation of grasping force in prosthetic hands using neural network-based predictive control.

    Science.gov (United States)

    Pasluosta, Cristian F; Chiu, Alan W L

    2015-01-01

    This chapter describes the implementation of a neural network-based predictive control system for driving a prosthetic hand. Nonlinearities associated with the electromechanical aspects of prosthetic devices present great challenges for precise control of this type of device. Model-based controllers may overcome this issue. Moreover, given the complexity of these kinds of electromechanical systems, neural network-based modeling arises as a good fit for modeling the fingers' dynamics. The results of simulations mimicking potential situations encountered during activities of daily living demonstrate the feasibility of this technique.

  5. Robust Quasi-LPV Control Based on Neural State Space Models

    DEFF Research Database (Denmark)

    Bendtsen, Jan Dimon; Trangbæk, Klaus

    2002-01-01

    In this paper we derive a synthesis result for robust LPV output feedback controllers for nonlinear systems modelled by neural state space models. This result is achieved by writing the neural state space model on a linear fractional transformation form in a non-conservative way, separating...... that there is some uncertainty on the identified nonlinearities. The control law is therefore made robust to noise perturbations. After formulating the controller synthesis as a set of LMIs with added constraints, some implementation issues are addressed and a simulation example is presented....

  6. Modelado y simulación del sistema de control cardiovascular en pacientes con lesiones coronarias

    OpenAIRE

    Vallverdú Ferrer, Montserrat

    1993-01-01

    Se presenta un modelo del sistema cardiovascular desarrollado con suficiente detalle para analizar la regulación del sistema nervioso central (snc) de control, cuando el sistema cardiovascular esta sometido a la acción de diferentes señales de las presiones intratoracica e intraabdominal. Como señales de salida a este sistema se consideran las señales que pueden ser medidas experimentalmente en las sesiones de cateterismo: presiones de la aurícula derecha y aortica, flujo sanguíneo coronario ...

  7. Hypertension control and other Cardiovascular Risk Factors among ...

    African Journals Online (AJOL)

    Results: Hypertension prevalence was 54.7%. Treatment and control rates of hypertension were 81.7% and 34% respectively. Hypertensive patients were older, more overweight/obese, had a longer duration of diabetes and elevated serum creatinine. The prevalence of Dyslipidemia, overweight and obesity were 88%, ...

  8. Hypertension control and other cardiovascular risk factors among ...

    African Journals Online (AJOL)

    Results: Hypertension prevalence was 54.7%. Treatment and control rates of hypertension were 81.7% and 34% respectively. Hypertensive patients were older, more overweight/obese, had a longer duration of diabetes and elevated serum creatinine. The prevalence of Dyslipidemia, overweight and obesity were 88%, ...

  9. Heart rate control with adrenergic blockade: Clinical outcomes in cardiovascular medicine

    Directory of Open Access Journals (Sweden)

    David Feldman

    2010-05-01

    Full Text Available David Feldman1, Terry S Elton2, Doron M Menachemi3, Randy K Wexler41Heart Failure/Transplant and VAD Programs, Minneapolis Heart Institute, Minneapolis, Minnesota, USA; 2Division of Pharmacology, College of Pharmacology, The Ohio State University, Columbus, Ohio, USA; 3Heart Failure Services, Edith Wolfson Medical Center, The Heart Institute, Sakler School of Medicine, Tel-Aviv University, Holon, Israel; 4Department of Clinical Family Medicine, The Ohio State University, Columbus, Ohio, USAAbstract: The sympathetic nervous system is involved in regulating various cardiovascular parameters including heart rate (HR and HR variability. Aberrant sympathetic nervous system expression may result in elevated HR or decreased HR variability, and both are independent risk factors for development of cardiovascular disease, including heart failure, myocardial infarction, and hypertension. Epidemiologic studies have established that impaired HR control is linked to increased cardiovascular morbidity and mortality. One successful way of decreasing HR and cardiovascular mortality has been by utilizing β-blockers, because their ability to alter cell signaling at the receptor level has been shown to mitigate the pathogenic effects of sympathetic nervous system hyperactivation. Numerous clinical studies have demonstrated that β-blocker-mediated HR control improvements are associated with decreased mortality in postinfarct and heart failure patients. Although improved HR control benefits have yet to be established in hypertension, both traditional and vasodilating β-blockers exert positive HR control effects in this patient population. However, differences exist between traditional and vasodilating β-blockers; the latter reduce peripheral vascular resistance and exert neutral or positive effects on important metabolic parameters. Clinical evidence suggests that attainment of HR control is an important treatment objective for patients with cardiovascular

  10. Axonal Control of the Adult Neural Stem Cell Niche

    Science.gov (United States)

    Tong, Cheuk Ka; Chen, Jiadong; Cebrián-Silla, Arantxa; Mirzadeh, Zaman; Obernier, Kirsten; Guinto, Cristina D.; Tecott, Laurence H.; García-Verdugo, Jose Manuel; Kriegstein, Arnold; Alvarez-Buylla, Arturo

    2014-01-01

    SUMMARY The ventricular-subventricular zone (V-SVZ) is an extensive germinal niche containing neural stem cells (NSC) in the walls of the lateral ventricles of the adult brain. How the adult brain’s neural activity influences the behavior of adult NSCs remains largely unknown. We show that serotonergic (5HT) axons originating from a small group of neurons in the raphe form an extensive plexus on most of the ventricular walls. Electron microscopy revealed intimate contacts between 5HT axons and NSCs (B1) or ependymal cells (E1) and these cells were labeled by a transsynaptic viral tracer injected into the raphe. B1 cells express the 5HT receptors 2C and 5A. Electrophysiology showed that activation of these receptors in B1 cells induced small inward currents. Intraventricular infusion of 5HT2C agonist or antagonist increased or decreased V-SVZ proliferation, respectively. These results indicate that supraependymal 5HT axons directly interact with NSCs to regulate neurogenesis via 5HT2C. PMID:24561083

  11. Skeletal Muscle Pump Drives Control of Cardiovascular and Postural Systems

    Science.gov (United States)

    Verma, Ajay K.; Garg, Amanmeet; Xu, Da; Bruner, Michelle; Fazel-Rezai, Reza; Blaber, Andrew P.; Tavakolian, Kouhyar

    2017-03-01

    The causal interaction between cardio-postural-musculoskeletal systems is critical in maintaining postural stability under orthostatic challenge. The absence or reduction of such interactions could lead to fainting and falls often experienced by elderly individuals. The causal relationship between systolic blood pressure (SBP), calf electromyography (EMG), and resultant center of pressure (COPr) can quantify the behavior of cardio-postural control loop. Convergent cross mapping (CCM) is a non-linear approach to establish causality, thus, expected to decipher nonlinear causal cardio-postural-musculoskeletal interactions. Data were acquired simultaneously from young participants (25 ± 2 years, n = 18) during a 10-minute sit-to-stand test. In the young population, skeletal muscle pump was found to drive blood pressure control (EMG → SBP) as well as control the postural sway (EMG → COPr) through the significantly higher causal drive in the direction towards SBP and COPr. Furthermore, the effect of aging on muscle pump activation associated with blood pressure regulation was explored. Simultaneous EMG and SBP were acquired from elderly group (69 ± 4 years, n = 14). A significant (p = 0.002) decline in EMG → SBP causality was observed in the elderly group, compared to the young group. The results highlight the potential of causality to detect alteration in blood pressure regulation with age, thus, a potential clinical utility towards detection of fall proneness.

  12. Racial disparities in cardiovascular risk factor control in an underinsured population with Type 2 diabetes.

    Science.gov (United States)

    Wang, Y; Katzmarzyk, P T; Horswell, R; Zhao, W; Li, W; Johnson, J; Ryan, D H; Hu, G

    2014-10-01

    To investigate the race-specific trend in attainment of the American Diabetes Association cardiovascular risk factor control goals (HbA1c LDL cholesterol LDL cholesterol levels. Logistic regression was used to test the difference between African-American and white patients. The percentage of patients who met all three American Diabetes Association goals increased from 8.2% in 2001 to 10.2% in 2009 (increased by 24.4%) in this cohort. Compared with African-American patients, white patients had better attainment of the following American Diabetes Association goals: HbA1c (61.4 vs. 55.1%), blood pressure (25.8 vs. 20.4%), LDL cholesterol (40.1 vs. 37.7%) and all three goals (7.3 vs. 5.1%). African-American and white patients generally had a better cardiovascular disease risk factor profile during follow-up when we assessed attainment of the American Diabetes Association goals by means of HbA1c , blood pressure and LDL cholesterol. During 2001-2009, the present low-income cohort of people with Type 2 diabetes generally experienced improved control of cardiovascular disease risk factors. White patients had better attainment of the American Diabetes Association cardiovascular risk factor control goals than their African-American counterparts. © 2014 The Authors. Diabetic Medicine © 2014 Diabetes UK.

  13. A walking robot called human: lessons to be learned from neural control of locomotion

    NARCIS (Netherlands)

    Duysens, J.; van de Crommert, H.W.A.A.; Smits-Engelsman, B.C.M.; van der Helm, F.C.T.

    2002-01-01

    From what we know at present with respect to the neural control of walking, it can be concluded that an optimal biologically inspired robot could have the following features. The limbs should include several joints in which position changes can be obtained by actuators across the joints. The control

  14. Neural network for quality control of submunitions produced by injection loading

    Energy Technology Data Exchange (ETDEWEB)

    Smith, R.E.; Parkinson, W.J.; Hinde, R.F. Jr.; Wantuck, P.J. [Los Alamos National Lab., NM (United States). Engineering Sciences and Applications Div.; Newman, K.E. [Naval Surface Warfare Center, Yorktown, VA (United States)

    1998-12-01

    Injection loading of submunitions for smart weapons is a novel automated processing technique that can benefit from adaptive process control. This paper describes how the quality of submunitions could be controlled by using a neural network code in real time. Future work is planned to demonstrate fewer rejects and pollution reduction during submunition manufacturing.

  15. Control of nonlinear chemical processes using neural models and feedback linearization

    NARCIS (Netherlands)

    te Braake, Hubert A.B.; van Can, Eric J.L.; Scherpen, Jacquelien M.A.; Verbruggen, Henk B.

    1998-01-01

    Black-box modeling techniques based on artificial neural networks are opening new horizons for the modeling and control nonlinear processes in biotechnology and the chemical process industries. The link between dynamic process models and actual process control is provided by the concept of

  16. A Dung Beetle-like Leg and its Adaptive Neural Control

    DEFF Research Database (Denmark)

    Di Canio, Giuliano; Stoyanov, Stoyan; Larsen, Jørgen Christian

    2016-01-01

    also apply adaptive neural control, based on a central pattern generator (CPG) circuit with synaptic plasticity, to autonomously generate a proper stepping frequency of the leg. The controller can also adapt the leg movement to deal with external perturbations within a few steps....

  17. Neuromechanic: a computational platform for simulation and analysis of the neural control of movement

    Science.gov (United States)

    Bunderson, Nathan E.; Bingham, Jeffrey T.; Sohn, M. Hongchul; Ting, Lena H.; Burkholder, Thomas J.

    2015-01-01

    Neuromusculoskeletal models solve the basic problem of determining how the body moves under the influence of external and internal forces. Existing biomechanical modeling programs often emphasize dynamics with the goal of finding a feed-forward neural program to replicate experimental data or of estimating force contributions or individual muscles. The computation of rigid-body dynamics, muscle forces, and activation of the muscles are often performed separately. We have developed an intrinsically forward computational platform (Neuromechanic, www.neuromechanic.com) that explicitly represents the interdependencies among rigid body dynamics, frictional contact, muscle mechanics, and neural control modules. This formulation has significant advantages for optimization and forward simulation, particularly with application to neural controllers with feedback or regulatory features. Explicit inclusion of all state dependencies allows calculation of system derivatives with respect to kinematic states as well as muscle and neural control states, thus affording a wealth of analytical tools, including linearization, stability analyses and calculation of initial conditions for forward simulations. In this review, we describe our algorithm for generating state equations and explain how they may be used in integration, linearization and stability analysis tools to provide structural insights into the neural control of movement. PMID:23027632

  18. Using reinforcement learning to provide stable brain-machine interface control despite neural input reorganization.

    Directory of Open Access Journals (Sweden)

    Eric A Pohlmeyer

    Full Text Available Brain-machine interface (BMI systems give users direct neural control of robotic, communication, or functional electrical stimulation systems. As BMI systems begin transitioning from laboratory settings into activities of daily living, an important goal is to develop neural decoding algorithms that can be calibrated with a minimal burden on the user, provide stable control for long periods of time, and can be responsive to fluctuations in the decoder's neural input space (e.g. neurons appearing or being lost amongst electrode recordings. These are significant challenges for static neural decoding algorithms that assume stationary input/output relationships. Here we use an actor-critic reinforcement learning architecture to provide an adaptive BMI controller that can successfully adapt to dramatic neural reorganizations, can maintain its performance over long time periods, and which does not require the user to produce specific kinetic or kinematic activities to calibrate the BMI. Two marmoset monkeys used the Reinforcement Learning BMI (RLBMI to successfully control a robotic arm during a two-target reaching task. The RLBMI was initialized using random initial conditions, and it quickly learned to control the robot from brain states using only a binary evaluative feedback regarding whether previously chosen robot actions were good or bad. The RLBMI was able to maintain control over the system throughout sessions spanning multiple weeks. Furthermore, the RLBMI was able to quickly adapt and maintain control of the robot despite dramatic perturbations to the neural inputs, including a series of tests in which the neuron input space was deliberately halved or doubled.

  19. Using reinforcement learning to provide stable brain-machine interface control despite neural input reorganization.

    Science.gov (United States)

    Pohlmeyer, Eric A; Mahmoudi, Babak; Geng, Shijia; Prins, Noeline W; Sanchez, Justin C

    2014-01-01

    Brain-machine interface (BMI) systems give users direct neural control of robotic, communication, or functional electrical stimulation systems. As BMI systems begin transitioning from laboratory settings into activities of daily living, an important goal is to develop neural decoding algorithms that can be calibrated with a minimal burden on the user, provide stable control for long periods of time, and can be responsive to fluctuations in the decoder's neural input space (e.g. neurons appearing or being lost amongst electrode recordings). These are significant challenges for static neural decoding algorithms that assume stationary input/output relationships. Here we use an actor-critic reinforcement learning architecture to provide an adaptive BMI controller that can successfully adapt to dramatic neural reorganizations, can maintain its performance over long time periods, and which does not require the user to produce specific kinetic or kinematic activities to calibrate the BMI. Two marmoset monkeys used the Reinforcement Learning BMI (RLBMI) to successfully control a robotic arm during a two-target reaching task. The RLBMI was initialized using random initial conditions, and it quickly learned to control the robot from brain states using only a binary evaluative feedback regarding whether previously chosen robot actions were good or bad. The RLBMI was able to maintain control over the system throughout sessions spanning multiple weeks. Furthermore, the RLBMI was able to quickly adapt and maintain control of the robot despite dramatic perturbations to the neural inputs, including a series of tests in which the neuron input space was deliberately halved or doubled.

  20. Self-Organizing Neural-Net Control of Ship's Horizontal Motion

    Energy Technology Data Exchange (ETDEWEB)

    Yang, X J; Zhao, X R [Automation College of Harbin Engineering University, Harbin 150001 (China)

    2006-10-15

    This paper describes the concept and an example of an adaptive neural-net controller system for ship's horizontal motion. The system consists of two parts, a real-world part and an imaginary-world part. The real-world part is a feedback control system for the actual ship. In the imaginary-world part, the model of ship and the controller are adjusted continuously in order to deal with changes of dynamic properties caused by disturbances and so on. In this paper, the adaptability of the controller system is investigated by controlling ship's horizontal motion including roll, yaw and sway. The results of simulation indicate that with selforganizing neural-net control, the mean square error of roll angle and yaw angle reduce to 0.92{sup 0}, and 0.74{sup 0} respectively. The control effect of SONC is better than conventional LQG controller.

  1. Experimental Studies of Neural Network Control for One-Wheel Mobile Robot

    Directory of Open Access Journals (Sweden)

    P. K. Kim

    2012-01-01

    Full Text Available This paper presents development and control of a disc-typed one-wheel mobile robot, called GYROBO. Several models of the one-wheel mobile robot are designed, developed, and controlled. The current version of GYROBO is successfully balanced and controlled to follow the straight line. GYROBO has three actuators to balance and move. Two actuators are used for balancing control by virtue of gyro effect and one actuator for driving movements. Since the space is limited and weight balance is an important factor for the successful balancing control, careful mechanical design is considered. To compensate for uncertainties in robot dynamics, a neural network is added to the nonmodel-based PD-controlled system. The reference compensation technique (RCT is used for the neural network controller to help GYROBO to improve balancing and tracking performances. Experimental studies of a self-balancing task and a line tracking task are conducted to demonstrate the control performances of GYROBO.

  2. Modeling and simulation of permanent magnet synchronous motor based on neural network control strategy

    Science.gov (United States)

    Luo, Bingyang; Chi, Shangjie; Fang, Man; Li, Mengchao

    2017-03-01

    Permanent magnet synchronous motor is used widely in industry, the performance requirements wouldn't be met by adopting traditional PID control in some of the occasions with high requirements. In this paper, a hybrid control strategy - nonlinear neural network PID and traditional PID parallel control are adopted. The high stability and reliability of traditional PID was combined with the strong adaptive ability and robustness of neural network. The permanent magnet synchronous motor will get better control performance when switch different working modes according to different controlled object conditions. As the results showed, the speed response adopting the composite control strategy in this paper was faster than the single control strategy. And in the case of sudden disturbance, the recovery time adopting the composite control strategy designed in this paper was shorter, the recovery ability and the robustness were stronger.

  3. Cardiovascular Disease and Diabetes: Modifying Risk Factors Other Than Glucose Control

    OpenAIRE

    Basa, Amelita L. P.; Garber, Alan J.

    2001-01-01

    Patients with type 2 diabetes have a significantly increased risk of developing cardiovascular disease. Atherosclerosis kills more diabetic patients than all other causes combined. Multiple risk factors tend to cluster in some patients in a syndrome termed insulin resistance syndrome or “Syndrome X.” Increasing evidence has changed the recommended management of diabetes from simple glucose control to aggressive lipid management and control of the other components of the metabolic syndrome to ...

  4. D-FNN Based Modeling and BP Neural Network Decoupling Control of PVC Stripping Process

    Directory of Open Access Journals (Sweden)

    Shu-zhi Gao

    2014-01-01

    Full Text Available PVC stripping process is a kind of complicated industrial process with characteristics of highly nonlinear and time varying. Aiming at the problem of establishing the accurate mathematics model due to the multivariable coupling and big time delay, the dynamic fuzzy neural network (D-FNN is adopted to establish the PVC stripping process model based on the actual process operation datum. Then, the PVC stripping process is decoupled by the distributed neural network decoupling module to obtain two single-input-single-output (SISO subsystems (slurry flow to top tower temperature and steam flow to bottom tower temperature. Finally, the PID controller based on BP neural networks is used to control the decoupled PVC stripper system. Simulation results show the effectiveness of the proposed integrated intelligent control method.

  5. Towards building hybrid biological/in silico neural networks for motor neuroprosthetic control

    Directory of Open Access Journals (Sweden)

    Mehmet eKocaturk

    2015-08-01

    Full Text Available In this article, we introduce the Bioinspired Neuroprosthetic Design Environment (BNDE as a practical platform for the development of novel brain machine interface (BMI controllers which are based on spiking model neurons. We built the BNDE around a hard real-time system so that it is capable of creating simulated synapses from extracellularly recorded neurons to model neurons. In order to evaluate the practicality of the BNDE for neuroprosthetic control experiments, a novel, adaptive BMI controller was developed and tested using real-time closed-loop simulations. The present controller consists of two in silico medium spiny neurons which receive simulated synaptic inputs from recorded motor cortical neurons. In the closed-loop simulations, the recordings from the cortical neurons were imitated using an external, hardware-based neural signal synthesizer. By implementing a reward-modulated spike timing-dependent plasticity rule, the controller achieved perfect target reach accuracy for a two target reaching task in one dimensional space. The BNDE combines the flexibility of software-based spiking neural network (SNN simulations with powerful online data visualization tools and is a low-cost, PC-based and all-in-one solution for developing neurally-inspired BMI controllers. We believe the BNDE is the first implementation which is capable of creating hybrid biological/in silico neural networks for motor neuroprosthetic control and utilizes multiple CPU cores for computationally intensive real-time SNN simulations.

  6. Gastrin release: Antrum microdialysis reveals a complex neural control

    DEFF Research Database (Denmark)

    Ericsson, P; Håkanson, R; Rehfeld, Jens F.

    2010-01-01

    in serum regardless of the prandial state. The rats were conscious during microdialysis except when subjected to electrical vagal stimulation. Acid blockade (omeprazole treatment of freely fed rats for 4 days), or bilateral sectioning of the abdominal vagal trunks (fasted, 3 days post-op.), raised...... the gastrin concentration in blood as well as microdialysate. The high gastrin concentration following omeprazole treatment was not affected by vagotomy. Vagal excitation stimulated the G cells: electrical vagal stimulation and pylorus ligation (fasted rats) raised the gastrin concentration transiently...... microdialysate gastrin concentration in omeprazole-treated rats by 65%. We conclude that activated gastrin release, unlike basal gastrin release, is highly dependent on a neural input: 1) Vagal excitation has a transient stimulating effect on the G cells. The transient nature of the response suggests...

  7. Dynamic Learning from Adaptive Neural Control of Uncertain Robots with Guaranteed Full-State Tracking Precision

    Directory of Open Access Journals (Sweden)

    Min Wang

    2017-01-01

    Full Text Available A dynamic learning method is developed for an uncertain n-link robot with unknown system dynamics, achieving predefined performance attributes on the link angular position and velocity tracking errors. For a known nonsingular initial robotic condition, performance functions and unconstrained transformation errors are employed to prevent the violation of the full-state tracking error constraints. By combining two independent Lyapunov functions and radial basis function (RBF neural network (NN approximator, a novel and simple adaptive neural control scheme is proposed for the dynamics of the unconstrained transformation errors, which guarantees uniformly ultimate boundedness of all the signals in the closed-loop system. In the steady-state control process, RBF NNs are verified to satisfy the partial persistent excitation (PE condition. Subsequently, an appropriate state transformation is adopted to achieve the accurate convergence of neural weight estimates. The corresponding experienced knowledge on unknown robotic dynamics is stored in NNs with constant neural weight values. Using the stored knowledge, a static neural learning controller is developed to improve the full-state tracking performance. A comparative simulation study on a 2-link robot illustrates the effectiveness of the proposed scheme.

  8. Minimal-Learning-Parameter Technique Based Adaptive Neural Sliding Mode Control of MEMS Gyroscope

    Directory of Open Access Journals (Sweden)

    Bin Xu

    2017-01-01

    Full Text Available This paper investigates an adaptive neural sliding mode controller for MEMS gyroscopes with minimal-learning-parameter technique. Considering the system uncertainty in dynamics, neural network is employed for approximation. Minimal-learning-parameter technique is constructed to decrease the number of update parameters, and in this way the computation burden is greatly reduced. Sliding mode control is designed to cancel the effect of time-varying disturbance. The closed-loop stability analysis is established via Lyapunov approach. Simulation results are presented to demonstrate the effectiveness of the method.

  9. A Mediterranean Diet to Improve Cardiovascular and Cognitive Health: Protocol for a Randomised Controlled Intervention Study.

    Science.gov (United States)

    Wade, Alexandra T; Davis, Courtney R; Dyer, Kathryn A; Hodgson, Jonathan M; Woodman, Richard J; Keage, Hannah A D; Murphy, Karen J

    2017-02-16

    The Mediterranean diet has demonstrated efficacy for improving cardiovascular and cognitive health. However, a traditional Mediterranean diet delivers fewer serves of dairy and less dietary calcium than is currently recommended in Australia, which may limit long-term sustainability. The present study aims to evaluate whether a Mediterranean diet with adequate dairy and calcium can improve cardiovascular and cognitive function in an at-risk population, and thereby reduce risk of cardiovascular disease (CVD) and cognitive decline. A randomised, controlled, parallel, crossover design trial will compare a Mediterranean diet supplemented with dairy foods against a low-fat control diet. Forty participants with systolic blood pressure above 120 mmHg and at least two other risk factors of CVD will undertake each dietary intervention for eight weeks, with an eight-week washout period between interventions. Systolic blood pressure will be the primary measure of interest. Secondary outcomes will include measures of cardiometabolic health, dietary compliance, cognitive function, assessed using the Cambridge Neuropsychological Test Automated Battery (CANTAB), psychological well-being and dementia risk. This research will provide empirical evidence as to whether the Mediterranean diet can be modified to provide recommended dairy and calcium intakes while continuing to deliver positive effects for cardiovascular and cognitive health. The findings will hold relevance for the field of preventative healthcare and may contribute to revisions of national dietary guidelines.

  10. Impact of combined exercise training on cardiovascular autonomic control and mortality in diabetic ovariectomized rats.

    Science.gov (United States)

    Sanches, Iris C; Conti, Filipe F; Bernardes, Nathalia; Brito, Janaina de O; Galdini, Elia G; Cavaglieri, Cláudia R; Irigoyen, Maria-Cláudia; De Angelis, Kátia

    2015-09-15

    The purpose of this study was to compare the effects of aerobic, resistance, or combined exercise training on cardiovascular autonomic control and mortality in diabetic ovariectomized rats. Female Wistar rats were divided into one of five groups: euglycemic sedentary (ES), diabetic ovariectomized sedentary (DOS), diabetic ovariectomized aerobic-trained (DOTA), diabetic ovariectomized resistance-trained (DOTR), or diabetic ovariectomized aerobic+resistance-trained (DOTC). Arterial pressure (AP) was directly recorded and baroreflex sensitivity was evaluated by heart rate responses to AP changes. Cardiovascular autonomic modulation was evaluated by spectral analyses. No differences were observed in body weight and glycemia between diabetic rats. Animals in the DOTC and DOTA groups exhibited an increase in running time, whereas animals in the DOTC and DOTR groups showed greater strength. Trained groups exhibited improvement in total power and the high-frequency band of pulse interval and reduced mortality (vs. DOS). Animals in the DOTC (bradycardic and tachycardic responses) and DOTA (tachycardic responses) groups exhibited attenuation in baroreflex dysfunction that was observed in DOS and DOTR animals, and an improvement in AP variance. In conclusion, all training protocols led to reduced mortality, which may be due to an increase in physical capacity and to cardiovascular and autonomic benefits following training, regardless of any improvement in glycemic control. In this model, the aerobic and combined trainings seem to promote additional cardiovascular autonomic benefits when compared with resistance training alone. Copyright © 2015 the American Physiological Society.

  11. On-Line Tracking Controller for Brushless DC Motor Drives Using Artificial Neural Networks

    Science.gov (United States)

    Rubaai, Ahmed

    1996-01-01

    A real-time control architecture is developed for time-varying nonlinear brushless dc motors operating in a high performance drives environment. The developed control architecture possesses the capabilities of simultaneous on-line identification and control. The dynamics of the motor are modeled on-line and controlled using an artificial neural network, as the system runs. The control architecture combines the experience and dependability of adaptive tracking systems with potential and promise of the neural computing technology. The sensitivity of real-time controller to parametric changes that occur during training is investigated. Such changes are usually manifested by rapid changes in the load of the brushless motor drives. This sudden change in the external load is simulated for the sigmoidal and sinusoidal reference tracks. The ability of the neuro-controller to maintain reasonable tracking accuracy in the presence of external noise is also verified for a number of desired reference trajectories.

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

    Science.gov (United States)

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

    2014-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Wang Chao

    2016-03-01

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

  14. Bilingualism increases neural response consistency and attentional control: Evidence for sensory and cognitive coupling

    Science.gov (United States)

    Krizman, Jennifer; Skoe, Erika; Marian, Viorica; Kraus, Nina

    2014-01-01

    Auditory processing is presumed to be influenced by cognitive processes – including attentional control – in a top-down manner. In bilinguals, activation of both languages during daily communication hones inhibitory skills, which subsequently bolster attentional control. We hypothesize that the heightened attentional demands of bilingual communication strengthens connections between cognitive (i.e., attentional control) and auditory processing, leading to greater across-trial consistency in the auditory evoked response (i.e., neural consistency) in bilinguals. To assess this, we collected passively-elicited auditory evoked responses to the syllable [da] and separately obtained measures of attentional control and language ability in adolescent Spanish-English bilinguals and English monolinguals. Bilinguals demonstrated enhanced attentional control and more consistent brainstem and cortical responses. In bilinguals, but not monolinguals, brainstem consistency tracked with language proficiency and attentional control. We interpret these enhancements in neural consistency as the outcome of strengthened attentional control that emerged from experience communicating in two languages. PMID:24413593

  15. Dynamic neural networking as a basis for plasticity in the control of heart rate.

    Science.gov (United States)

    Kember, G; Armour, J A; Zamir, M

    2013-01-21

    A model is proposed in which the relationship between individual neurons within a neural network is dynamically changing to the effect of providing a measure of "plasticity" in the control of heart rate. The neural network on which the model is based consists of three populations of neurons residing in the central nervous system, the intrathoracic extracardiac nervous system, and the intrinsic cardiac nervous system. This hierarchy of neural centers is used to challenge the classical view that the control of heart rate, a key clinical index, resides entirely in central neuronal command (spinal cord, medulla oblongata, and higher centers). Our results indicate that dynamic networking allows for the possibility of an interplay among the three populations of neurons to the effect of altering the order of control of heart rate among them. This interplay among the three levels of control allows for different neural pathways for the control of heart rate to emerge under different blood flow demands or disease conditions and, as such, it has significant clinical implications because current understanding and treatment of heart rate anomalies are based largely on a single level of control and on neurons acting in unison as a single entity rather than individually within a (plastically) interconnected network. Copyright © 2012 Elsevier Ltd. All rights reserved.

  16. Adipokines as Possible New Predictors of Cardiovascular Diseases: A Case Control Study

    Directory of Open Access Journals (Sweden)

    Laura Pala

    2012-01-01

    Full Text Available Background and Aims. The secretion of several adipocytokines, such as adiponectin, retinol-binding protein 4 (RBP4, adipocyte fatty acid binding protein (aFABP, and visfatin, is altered in subjects with abdominal adiposity; these endocrine alterations could contribute to increased cardiovascular risk. The aim of the study was to assess the relationship among adiponectin, RBP4, aFABP, and visfatin, and incident cardiovascular disease. Methods and Results. A case-control study, nested within a prospective cohort, on 2945 subjects enrolled for a diabetes screening program was performed. We studied 18 patients with incident fatal or nonfatal IHD (Ischemic Heart Disease or CVD (Cerebrovascular Disease, compared with 18 matched control subjects. Circulating adiponectin levels were significantly lower in cases of IHD with respect to controls. Circulating RBP4 levels were significantly increased in CVD and decreased in IHD with respect to controls. Circulating aFABP4 levels were significantly increased in CVD, while no difference was associated with IHD. Circulating visfatin levels were significantly lower in cases of both CVD and IHD with respect to controls, while no difference was associated with CVD. Conclusions. The present study confirms that low adiponectin is associated with increased incidents of IHD, but not CVD, and suggests, for the first time, a major effect of visfatin, aFABP, and RBP4 in the development of cardiovascular disease.

  17. Population prevalence and control of cardiovascular risk factors: what electronic medical records tell us.

    Science.gov (United States)

    Catalán-Ramos, Arantxa; Verdú, Jose M; Grau, María; Iglesias-Rodal, Manuel; del Val García, José L; Consola, Alicia; Comin, Eva

    2014-01-01

    To analyze the prevalence, control, and management of hypertension, hypercholesterolemia, and diabetes mellitus type 2 (DM2). Cross-sectional analysis of all individuals attended in the Catalan primary care centers between 2006 and 2009. History of cardiovascular diseases, diagnosis and treatment of hypertension, hypercholesterolemia, DM2, lipid profile, glycemia and blood pressure data were extracted from electronic medical records. Age-standardized prevalence and levels of management and control were estimated. Individuals aged 35-74 years using primary care databases. A total of 2,174,515 individuals were included (mean age 52 years [SD 11], 47% men). Hypertension was the most prevalent cardiovascular risk factor (39% in women, 41% in men) followed by hypercholesterolemia (38% and 40%) and DM2 (12% and 16%), respectively. Diuretics and angiotensin-converting enzyme inhibitors were most often prescribed for hypertension control (oral hypoglycemic agents alone (70%), or combined with insulin (15%). Hypertension was the most prevalent cardiovascular risk factor in the Catalan population attended at primary care centers. About two thirds of individuals with hypertension or DM2 were adequately controlled; hypercholesterolemia control was particularly low. Copyright © 2013 Elsevier España, S.L. All rights reserved.

  18. Intelligent control aspects of fuzzy logic and neural nets

    CERN Document Server

    Harris, C J; Brown, M

    1993-01-01

    With increasing demands for high precision autonomous control over wide operating envelopes, conventional control engineering approaches are unable to adequately deal with system complexity, nonlinearities, spatial and temporal parameter variations, and with uncertainty. Intelligent Control or self-organising/learning control is a new emerging discipline that is designed to deal with problems. Rather than being model based, it is experiential based. Intelligent Control is the amalgam of the disciplines of Artificial Intelligence, Systems Theory and Operations Research. It uses most recent expe

  19. Neural network-based adaptive dynamic surface control for permanent magnet synchronous motors.

    Science.gov (United States)

    Yu, Jinpeng; Shi, Peng; Dong, Wenjie; Chen, Bing; Lin, Chong

    2015-03-01

    This brief considers the problem of neural networks (NNs)-based adaptive dynamic surface control (DSC) for permanent magnet synchronous motors (PMSMs) with parameter uncertainties and load torque disturbance. First, NNs are used to approximate the unknown and nonlinear functions of PMSM drive system and a novel adaptive DSC is constructed to avoid the explosion of complexity in the backstepping design. Next, under the proposed adaptive neural DSC, the number of adaptive parameters required is reduced to only one, and the designed neural controllers structure is much simpler than some existing results in literature, which can guarantee that the tracking error converges to a small neighborhood of the origin. Then, simulations are given to illustrate the effectiveness and potential of the new design technique.

  20. An Inventory Controlled Supply Chain Model Based on Improved BP Neural Network

    Directory of Open Access Journals (Sweden)

    Wei He

    2013-01-01

    Full Text Available Inventory control is a key factor for reducing supply chain cost and increasing customer satisfaction. However, prediction of inventory level is a challenging task for managers. As one of the widely used techniques for inventory control, standard BP neural network has such problems as low convergence rate and poor prediction accuracy. Aiming at these problems, a new fast convergent BP neural network model for predicting inventory level is developed in this paper. By adding an error offset, this paper deduces the new chain propagation rule and the new weight formula. This paper also applies the improved BP neural network model to predict the inventory level of an automotive parts company. The results show that the improved algorithm not only significantly exceeds the standard algorithm but also outperforms some other improved BP algorithms both on convergence rate and prediction accuracy.

  1. Adaptive Neural Network Algorithm for Power Control in Nuclear Power Plants

    Science.gov (United States)

    Masri Husam Fayiz, Al

    2017-01-01

    The aim of this paper is to design, test and evaluate a prototype of an adaptive neural network algorithm for the power controlling system of a nuclear power plant. The task of power control in nuclear reactors is one of the fundamental tasks in this field. Therefore, researches are constantly conducted to ameliorate the power reactor control process. Currently, in the Department of Automation in the National Research Nuclear University (NRNU) MEPhI, numerous studies are utilizing various methodologies of artificial intelligence (expert systems, neural networks, fuzzy systems and genetic algorithms) to enhance the performance, safety, efficiency and reliability of nuclear power plants. In particular, a study of an adaptive artificial intelligent power regulator in the control systems of nuclear power reactors is being undertaken to enhance performance and to minimize the output error of the Automatic Power Controller (APC) on the grounds of a multifunctional computer analyzer (simulator) of the Water-Water Energetic Reactor known as Vodo-Vodyanoi Energetichesky Reaktor (VVER) in Russian. In this paper, a block diagram of an adaptive reactor power controller was built on the basis of an intelligent control algorithm. When implementing intelligent neural network principles, it is possible to improve the quality and dynamic of any control system in accordance with the principles of adaptive control. It is common knowledge that an adaptive control system permits adjusting the controller’s parameters according to the transitions in the characteristics of the control object or external disturbances. In this project, it is demonstrated that the propitious options for an automatic power controller in nuclear power plants is a control system constructed on intelligent neural network algorithms.

  2. The role of nanotechnology and nano and micro-electronics in monitoring and control of cardiovascular diseases and neurological disorders

    Science.gov (United States)

    Varadan, Vijay K.

    2007-04-01

    Nanotechnology has been broadly defined as the one for not only the creation of functional materials and devices as well as systems through control of matter at the scale of 1-100 nm, but also the exploitation of novel properties and phenomena at the same scale. Growing needs in the point-of-care (POC) that is an increasing market for improving patient's quality of life, are driving the development of nanotechnologies for diagnosis and treatment of various life threatening diseases. This paper addresses the recent development of nanodiagnostic sensors and nanotherapeutic devices with functionalized carbon nanotube and/or nanowire on a flexible organic thin film electronics to monitor and control of the three leading diseases namely 1) neurodegenerative diseases, 2) cardiovascular diseases, and 3) diabetes and metabolic diseases. The sensors developed include implantable and biocompatible devices, light weight wearable devices in wrist-watches, hats, shoes and clothes. The nanotherapeutics devices include nanobased drug delivery system. Many of these sensors are integrated with the wireless systems for the remote physiological monitoring. The author's research team has also developed a wireless neural probe using nanowires and nanotubes for monitoring and control of Parkinson's disease. Light weight and compact EEG, EOG and EMG monitoring system in a hat developed is capable of monitoring real time epileptic patients and patients with neurological and movement disorders using the Internet and cellular network. Physicians could be able to monitor these signals in realtime using portable computers or cell phones and will give early warning signal if these signals cross a pre-determined threshold level. In addition the potential impact of nanotechnology for applications in medicine is that, the devices can be designed to interact with cells and tissues at the molecular level, which allows high degree of functionality. Devices engineered at nanometer scale imply a

  3. [Research on UKF control of epileptic-form spikes in neural mass models].

    Science.gov (United States)

    Liu, Xian; Ma, Baiwang; Ji, June; Li, Xiaoli

    2013-12-01

    Neural mass models are able to produce epileptic electroencephalogram (EEG) signals in different stages of seizures. The models play important roles in studying the mechanism analysis and control of epileptic seizures. In this study, the closed-loop feedback control was used to suppress the epileptic-form spikes in the neural mass models. It was expected to provide certain theory basis for the choice of stimulus position and parameter in the clinical treatment. With the influence of measurement noise taken into account, an unscented Kalman filter (UKF) was added to the feedback loop to estimate the system state and an UKF controller was constructed via the estimated state. The control action was imposed on the hyper-excitable population and all populations respectively in simulations. It was shown that both UKF control schemes suppressed the epileptic-form spikes in the model. However, the control energy needed in the latter scheme was less than that needed in the former one.

  4. Nonlinear identification and control a neural network approach

    CERN Document Server

    Liu, G P

    2001-01-01

    The series Advances in Industrial Control aims to report and encourage technology transfer in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. New theory, new controllers, actuators, sensors, new industrial processes, computer methods, new applications, new philosophies . . . , new challenges. Much of this development work resides in industrial reports, feasibility study papers and the reports of advanced collaborative projects. The series otTers an opportunity for researchers to present an extended exposition of such new work in all aspects of industrial control for wider and rapid dissemination. The time for nonlinear control to enter routine application seems to be approaching. Nonlinear control has had a long gestation period but much ofthe past has been concerned with methods that involve formal nonlinear functional model representations. It seems more likely that the breakthough will come through the use of other more flexible and ame...

  5. Neural interactions mediating conflict control and its training-induced plasticity.

    Science.gov (United States)

    Hu, Min; Wang, Xiangpeng; Zhang, Wenwen; Hu, Xueping; Chen, Antao

    2017-12-01

    Cognitive control is of great plasticity. Training programs targeted on improving it have been suggested to yield neural changes in the brain. However, until recently, the relationship between training-induced brain changes and improvements in cognitive control is still an open issue. Besides, although the literature has attributed the operation of cognitive control to interactions between large-scale networks, the neural pathways directly associated with it remain unclear. The current study aimed to examine these issues by focusing on conflict processing. In particular, we employed a training program with a randomized controlled design. The main findings were as follows: 1) In behavior, the training group showed reduced conflict effect after training, relative to the control group; 2) In the pretest stage, the behavioral conflict effect was negatively correlated with a number of neural pathways, including the connectivity from the cingulo-opercular network (CON) to the cerebellum and to sub-regions of the dorsal visual network; 3) increase in the connectivity strength of several network interactions, such as the connectivity from the CON to the cerebellum and to the primary visual network, was associated with behavioral gains; 4) there were also nonlinear correlations between behavioral and neural changes. These findings highlighted a critical role of the modulation of CON on other networks in mediating conflict processing and its plasticity, and raised the significance of investigating nonlinear relationship in the field of cognitive training. Copyright © 2017 Elsevier Inc. All rights reserved.

  6. Germinal Center Optimization Applied to Neural Inverse Optimal Control for an All-Terrain Tracked Robot

    Directory of Open Access Journals (Sweden)

    Carlos Villaseñor

    2017-12-01

    Full Text Available Nowadays, there are several meta-heuristics algorithms which offer solutions for multi-variate optimization problems. These algorithms use a population of candidate solutions which explore the search space, where the leadership plays a big role in the exploration-exploitation equilibrium. In this work, we propose to use a Germinal Center Optimization algorithm (GCO which implements temporal leadership through modeling a non-uniform competitive-based distribution for particle selection. GCO is used to find an optimal set of parameters for a neural inverse optimal control applied to all-terrain tracked robot. In the Neural Inverse Optimal Control (NIOC scheme, a neural identifier, based on Recurrent High Orden Neural Network (RHONN trained with an extended kalman filter algorithm, is used to obtain a model of the system, then, a control law is design using such model with the inverse optimal control approach. The RHONN identifier is developed without knowledge of the plant model or its parameters, on the other hand, the inverse optimal control is designed for tracking velocity references. Applicability of the proposed scheme is illustrated using simulations results as well as real-time experimental results with an all-terrain tracked robot.

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

    Science.gov (United States)

    Schreiner, John N.

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

  8. Cardiovascular risk factor control is insufficient in young patients with coronary artery disease

    Directory of Open Access Journals (Sweden)

    Christiansen MK

    2016-05-01

    Full Text Available Morten Krogh Christiansen, Jesper Møller Jensen, Anders Krogh Brøndberg, Hans Erik Bøtker, Henrik Kjærulf JensenDepartment of Cardiology, Aarhus University Hospital, Aarhus, DenmarkBackground: Control of cardiovascular risk factor is important in secondary prevention of coronary artery disease (CAD but it is unknown whether treatment targets are achieved in young patients. We aimed to examine the prevalence and control of risk factors in this subset of patients.Methods: We performed a cross-sectional, single-center study on patients with documented CAD before age 40. All patients treated between 2002 and 2014 were invited to participate at least 6 months after the last coronary intervention. We included 143 patients and recorded the family history of cardiovascular disease, physical activity level, smoking status, body mass index, waist circumference, blood pressure, cholesterol levels, metabolic status, and current medical therapy. Risk factor control and treatment targets were evaluated according to the shared guidelines from the European Society of Cardiology.Results: The most common insufficiently controlled risk factors were overweight (113 [79.0%], low-density lipoprotein cholesterol above target (77 [57.9%], low physical activity level (78 [54.6%], hypertriglyceridemia (67 [46.9%], and current smoking (53 [37.1%]. Almost one-half of the patients fulfilled the criteria of metabolic syndrome. The median (interquartile range number of uncontrolled modifiable risk factors was 2 (2;4 and only seven (4.9% patients fulfilled all modifiable health measure targets.Conclusion: Among the youngest patients with CAD, there remains a potential to improve the cardiovascular risk profile.Keywords: coronary artery disease, cardiovascular diseases/prevention and control, health behavior, risk factors, young adult, middle aged

  9. A hyperstable neural network for the modelling and control of ...

    Indian Academy of Sciences (India)

    Liu 1994, Gupta & Sinha 1996, Zhu et al 1999). 2. Controller design. It is assumed that the plant in ..... 15: 193±199. Gupta M M, Sinha N K (eds) 1996 Intelligent control systems (New York: IEEE Press). Harris C J, Billings S A 1985 Self-tuning control: Theory and applications rev. 2nd edn (London: Peter Peregrinus).

  10. Cardiovascular risk factor control is insufficient in young patients with coronary artery disease.

    Science.gov (United States)

    Christiansen, Morten Krogh; Jensen, Jesper Møller; Brøndberg, Anders Krogh; Bøtker, Hans Erik; Jensen, Henrik Kjærulf

    2016-01-01

    Control of cardiovascular risk factor is important in secondary prevention of coronary artery disease (CAD) but it is unknown whether treatment targets are achieved in young patients. We aimed to examine the prevalence and control of risk factors in this subset of patients. We performed a cross-sectional, single-center study on patients with documented CAD before age 40. All patients treated between 2002 and 2014 were invited to participate at least 6 months after the last coronary intervention. We included 143 patients and recorded the family history of cardiovascular disease, physical activity level, smoking status, body mass index, waist circumference, blood pressure, cholesterol levels, metabolic status, and current medical therapy. Risk factor control and treatment targets were evaluated according to the shared guidelines from the European Society of Cardiology. The most common insufficiently controlled risk factors were overweight (113 [79.0%]), low-density lipoprotein cholesterol above target (77 [57.9%]), low physical activity level (78 [54.6%]), hypertriglyceridemia (67 [46.9%]), and current smoking (53 [37.1%]). Almost one-half of the patients fulfilled the criteria of metabolic syndrome. The median (interquartile range) number of uncontrolled modifiable risk factors was 2 (2;4) and only seven (4.9%) patients fulfilled all modifiable health measure targets. Among the youngest patients with CAD, there remains a potential to improve the cardiovascular risk profile.

  11. Embryonic cerebrospinal fluid in brain development: neural progenitor control.

    Science.gov (United States)

    Gato, Angel; Alonso, M Isabel; Martín, Cristina; Carnicero, Estela; Moro, José Antonio; De la Mano, Aníbal; Fernández, José M F; Lamus, Francisco; Desmond, Mary E

    2014-08-28

    Due to the effort of several research teams across the world, today we have a solid base of knowledge on the liquid contained in the brain cavities, its composition, and biological roles. Although the cerebrospinal fluid (CSF) is among the most relevant parts of the central nervous system from the physiological point of view, it seems that it is not a permanent and stable entity because its composition and biological properties evolve across life. So, we can talk about different CSFs during the vertebrate life span. In this review, we focus on the CSF in an interesting period, early in vertebrate development before the formation of the choroid plexus. This specific entity is called "embryonic CSF." Based on the structure of the compartment, CSF composition, origin and circulation, and its interaction with neuroepithelial precursor cells (the target cells) we can conclude that embryonic CSF is different from the CSF in later developmental stages and from the adult CSF. This article presents arguments that support the singularity of the embryonic CSF, mainly focusing on its influence on neural precursor behavior during development and in adult life.

  12. A Review of Neural Network Based Machine Learning Approaches for Rotor Angle Stability Control

    OpenAIRE

    Yousefian, Reza; Kamalasadan, Sukumar

    2017-01-01

    This paper reviews the current status and challenges of Neural Networks (NNs) based machine learning approaches for modern power grid stability control including their design and implementation methodologies. NNs are widely accepted as Artificial Intelligence (AI) approaches offering an alternative way to control complex and ill-defined problems. In this paper various application of NNs for power system rotor angle stabilization and control problem is discussed. The main focus of this paper i...

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

    Directory of Open Access Journals (Sweden)

    Cong-Hui Huang

    2014-12-01

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

  14. Design of Optimal Hybrid Position/Force Controller for a Robot Manipulator Using Neural Networks

    Directory of Open Access Journals (Sweden)

    Vikas Panwar

    2007-01-01

    Full Text Available The application of quadratic optimization and sliding-mode approach is considered for hybrid position and force control of a robot manipulator. The dynamic model of the manipulator is transformed into a state-space model to contain two sets of state variables, where one describes the constrained motion and the other describes the unconstrained motion. The optimal feedback control law is derived solving matrix differential Riccati equation, which is obtained using Hamilton Jacobi Bellman optimization. The optimal feedback control law is shown to be globally exponentially stable using Lyapunov function approach. The dynamic model uncertainties are compensated with a feedforward neural network. The neural network requires no preliminary offline training and is trained with online weight tuning algorithms that guarantee small errors and bounded control signals. The application of the derived control law is demonstrated through simulation with a 4-DOF robot manipulator to track an elliptical planar constrained surface while applying the desired force on the surface.

  15. The Improvment DSTATCOM to Enhance the Quality of Power Using Fuzzy- Neural Controller

    Directory of Open Access Journals (Sweden)

    Gazanfar Shahgholian

    2011-07-01

    Full Text Available In this article the performance of a DSTATCOM as an efficient parallel compensator, for obtaining the power indexes has been considered, Then, to improve the performance of DSTATVOM, each of its PI controller has been substituted by a nonlinear fuzzy-neural controller, based on trial error and derivation of system error. The cause of detorioration of power quality and the method of their compensation, in a distribution network, has been analysed, using Matlab. The simulation results show that, using the fuzzy-neural controller in place of linear controller in DSTATCOM controller, the ability of compensation in the active and reactive power voltage sag, swell and fliker, and reduction of low current and voltage harmonics has been improved cansiderably.

  16. Neural Network Control-Based Drive Design of Servomotor and Its Application to Automatic Guided Vehicle

    Directory of Open Access Journals (Sweden)

    Ming-Shyan Wang

    2015-01-01

    Full Text Available An automatic guided vehicle (AGV is extensively used for productions in a flexible manufacture system with high efficiency and high flexibility. A servomotor-based AGV is designed and implemented in this paper. In order to steer the AGV to go along a predefined path with corner or arc, the conventional proportional-integral-derivative (PID control is used in the system. However, it is difficult to tune PID gains at various conditions. As a result, the neural network (NN control is considered to assist the PID control for gain tuning. The experimental results are first provided to verify the correctness of the neural network plus PID control for 400 W-motor control system. Secondly, the AGV includes two sets of the designed motor systems and CAN BUS transmission so that it can move along the straight line and curve paths shown in the taped videos.

  17. The Neural Basis of Sustained and Transient Attentional Control in Young Adults with ADHD

    Science.gov (United States)

    Banich, Marie T.; Burgess, Gregory C.; Depue, Brendan E.; Ruzic, Luka; Bidwell, L. Cinnamon; Hitt-Laustsen, Sena; Du, Yiping P.; Willcutt, Erik G.

    2009-01-01

    Differences in neural activation during performance on an attentionally demanding Stroop task were examined between 23 young adults with ADHD carefully selected to not be co-morbid for other psychiatric disorders and 23 matched controls. A hybrid blocked/single-trial design allowed for examination of more sustained vs. more transient aspects of…

  18. Robust adaptive fuzzy neural tracking control for a class of unknown ...

    Indian Academy of Sciences (India)

    difficulties encountered in handling chaotic systems have posed a real need for using some intelligent approaches. The application of neural network and fuzzy logic controllers to chaotic systems was proposed [18–23], which appears to be quite promising. Recently,. FNN incorporated the advantages of fuzzy inference and ...

  19. Adaptive Neural Output Feedback Control for Uncertain Robot Manipulators with Input Saturation

    Directory of Open Access Journals (Sweden)

    Rong Mei

    2017-01-01

    Full Text Available This paper presents an adaptive neural output feedback control scheme for uncertain robot manipulators with input saturation using the radial basis function neural network (RBFNN and disturbance observer. First, the RBFNN is used to approximate the system uncertainty, and the unknown approximation error of the RBFNN and the time-varying unknown external disturbance of robot manipulators are integrated as a compounded disturbance. Then, the state observer and the disturbance observer are proposed to estimate the unmeasured system state and the unknown compounded disturbance based on RBFNN. At the same time, the adaptation technique is employed to tackle the control input saturation problem. Utilizing the estimate outputs of the RBFNN, the state observer, and the disturbance observer, the adaptive neural output feedback control scheme is developed for robot manipulators using the backstepping technique. The convergence of all closed-loop signals is rigorously proved via Lyapunov analysis and the asymptotically convergent tracking error is obtained under the integrated effect of the system uncertainty, the unmeasured system state, the unknown external disturbance, and the input saturation. Finally, numerical simulation results are presented to illustrate the effectiveness of the proposed adaptive neural output feedback control scheme for uncertain robot manipulators.

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

    NARCIS (Netherlands)

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

    2000-01-01

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

  1. Functional dissociations in top-down control dependent neural repetition priming.

    NARCIS (Netherlands)

    Klaver, P.; Schnaidt, M.; Fell, J.; Ruhlmann, J.; Elger, C.E.; Fernandez, G.

    2007-01-01

    Little is known about the neural mechanisms underlying top-down control of repetition priming. Here, we use functional brain imaging to investigate these mechanisms. Study and repetition tasks used a natural/man-made forced choice task. In the study phase subjects were required to respond to either

  2. Reactive Neural Control for Phototaxis and Obstacle Avoidance Behavior of Walking Machines

    DEFF Research Database (Denmark)

    Manoonpong, Poramate; Pasemann, Frank; Wörgötter, Florentin

    2007-01-01

    as a sensory fusion unit. It filters sensory noise and shapes sensory data to drive the corresponding reactive behavior. On the other hand, modular neural control based on a central pattern generator is applied for locomotion of walking machines. It coordinates leg movements and can generate omnidirectional...

  3. A physiological neural controller of a muscle fiber oculomotor plant in horizontal monkey saccades.

    Science.gov (United States)

    Ghahari, Alireza; Enderle, John D

    2014-01-01

    A neural network model of biophysical neurons in the midbrain is presented to drive a muscle fiber oculomotor plant during horizontal monkey saccades. Neural circuitry, including omnipause neuron, premotor excitatory and inhibitory burst neurons, long lead burst neuron, tonic neuron, interneuron, abducens nucleus, and oculomotor nucleus, is developed to examine saccade dynamics. The time-optimal control strategy by realization of agonist and antagonist controller models is investigated. In consequence, each agonist muscle fiber is stimulated by an agonist neuron, while an antagonist muscle fiber is unstimulated by a pause and step from the antagonist neuron. It is concluded that the neural network is constrained by a minimum duration of the agonist pulse and that the most dominant factor in determining the saccade magnitude is the number of active neurons for the small saccades. For the large saccades, however, the duration of agonist burst firing significantly affects the control of saccades. The proposed saccadic circuitry establishes a complete model of saccade generation since it not only includes the neural circuits at both the premotor and motor stages of the saccade generator, but also uses a time-optimal controller to yield the desired saccade magnitude.

  4. Containment control of networked autonomous underwater vehicles: A predictor-based neural DSC design.

    Science.gov (United States)

    Peng, Zhouhua; Wang, Dan; Wang, Wei; Liu, Lu

    2015-11-01

    This paper investigates the containment control problem of networked autonomous underwater vehicles in the presence of model uncertainty and unknown ocean disturbances. A predictor-based neural dynamic surface control design method is presented to develop the distributed adaptive containment controllers, under which the trajectories of follower vehicles nearly converge to the dynamic convex hull spanned by multiple reference trajectories over a directed network. Prediction errors, rather than tracking errors, are used to update the neural adaptation laws, which are independent of the tracking error dynamics, resulting in two time-scales to govern the entire system. The stability property of the closed-loop network is established via Lyapunov analysis, and transient property is quantified in terms of L2 norms of the derivatives of neural weights, which are shown to be smaller than the classical neural dynamic surface control approach. Comparative studies are given to show the substantial improvements of the proposed new method. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

  5. Reach and grasp by people with tetraplegia using a neurally controlled robotic arm.

    Science.gov (United States)

    Hochberg, Leigh R; Bacher, Daniel; Jarosiewicz, Beata; Masse, Nicolas Y; Simeral, John D; Vogel, Joern; Haddadin, Sami; Liu, Jie; Cash, Sydney S; van der Smagt, Patrick; Donoghue, John P

    2012-05-16

    Paralysis following spinal cord injury, brainstem stroke, amyotrophic lateral sclerosis and other disorders can disconnect the brain from the body, eliminating the ability to perform volitional movements. A neural interface system could restore mobility and independence for people with paralysis by translating neuronal activity directly into control signals for assistive devices. We have previously shown that people with long-standing tetraplegia can use a neural interface system to move and click a computer cursor and to control physical devices. Able-bodied monkeys have used a neural interface system to control a robotic arm, but it is unknown whether people with profound upper extremity paralysis or limb loss could use cortical neuronal ensemble signals to direct useful arm actions. Here we demonstrate the ability of two people with long-standing tetraplegia to use neural interface system-based control of a robotic arm to perform three-dimensional reach and grasp movements. Participants controlled the arm and hand over a broad space without explicit training, using signals decoded from a small, local population of motor cortex (MI) neurons recorded from a 96-channel microelectrode array. One of the study participants, implanted with the sensor 5 years earlier, also used a robotic arm to drink coffee from a bottle. Although robotic reach and grasp actions were not as fast or accurate as those of an able-bodied person, our results demonstrate the feasibility for people with tetraplegia, years after injury to the central nervous system, to recreate useful multidimensional control of complex devices directly from a small sample of neural signals.

  6. Topological defects control collective dynamics in neural progenitor cell cultures

    Science.gov (United States)

    Kawaguchi, Kyogo; Kageyama, Ryoichiro; Sano, Masaki

    2017-04-01

    Cultured stem cells have become a standard platform not only for regenerative medicine and developmental biology but also for biophysical studies. Yet, the characterization of cultured stem cells at the level of morphology and of the macroscopic patterns resulting from cell-to-cell interactions remains largely qualitative. Here we report on the collective dynamics of cultured murine neural progenitor cells (NPCs), which are multipotent stem cells that give rise to cells in the central nervous system. At low densities, NPCs moved randomly in an amoeba-like fashion. However, NPCs at high density elongated and aligned their shapes with one another, gliding at relatively high velocities. Although the direction of motion of individual cells reversed stochastically along the axes of alignment, the cells were capable of forming an aligned pattern up to length scales similar to that of the migratory stream observed in the adult brain. The two-dimensional order of alignment within the culture showed a liquid-crystalline pattern containing interspersed topological defects with winding numbers of +1/2 and -1/2 (half-integer due to the nematic feature that arises from the head-tail symmetry of cell-to-cell interaction). We identified rapid cell accumulation at +1/2 defects and the formation of three-dimensional mounds. Imaging at the single-cell level around the defects allowed us to quantify the velocity field and the evolving cell density; cells not only concentrate at +1/2 defects, but also escape from -1/2 defects. We propose a generic mechanism for the instability in cell density around the defects that arises from the interplay between the anisotropic friction and the active force field.

  7. Cognitive-affective neural plasticity following active-controlled mindfulness intervention

    DEFF Research Database (Denmark)

    Allen, Micah Galen

    Mindfulness meditation is a set of attention-based, regulatory and self-inquiry training regimes. Although the impact of mindfulness meditation training (MT) on self-regulation is well established, the neural mechanisms supporting such plasticity are poorly understood. MT is thought to act through...... prefrontal cortex (mPFC), and right anterior insula during negative valence processing. Our findings highlight the importance of active control in MT research, indicate unique neural mechanisms for progressive stages of mindfulness training, and suggest that optimal application of MT may differ depending...

  8. Contextual and Developmental Differences in the Neural Architecture of Cognitive Control.

    Science.gov (United States)

    Petrican, Raluca; Grady, Cheryl L

    2017-08-09

    Because both development and context impact functional brain architecture, the neural connectivity signature of a cognitive or affective predisposition may similarly vary across different ages and circumstances. To test this hypothesis, we investigated the effects of age and cognitive versus social-affective context on the stable and time-varying neural architecture of inhibition, the putative core cognitive control component, in a subsample (N = 359, 22-36 years, 174 men) of the Human Connectome Project. Among younger individuals, a neural signature of superior inhibition emerged in both stable and dynamic connectivity analyses. Dynamically, a context-free signature emerged as stronger segregation of internal cognition (default mode) and environmentally driven control (salience, cingulo-opercular) systems. A dynamic social-affective context-specific signature was observed most clearly in the visual system. Stable connectivity analyses revealed both context-free (greater default mode segregation) and context-specific (greater frontoparietal segregation for higher cognitive load; greater attentional and environmentally driven control system segregation for greater reward value) signatures of inhibition. Superior inhibition in more mature adulthood was typified by reduced segregation in the default network with increasing reward value and increased ventral attention but reduced cingulo-opercular and subcortical system segregation with increasing cognitive load. Failure to evidence this neural profile after the age of 30 predicted poorer life functioning. Our results suggest that distinguishable neural mechanisms underlie individual differences in cognitive control during different young adult stages and across tasks, thereby underscoring the importance of better understanding the interplay among dispositional, developmental, and contextual factors in shaping adaptive versus maladaptive patterns of thought and behavior.SIGNIFICANCE STATEMENT The brain's functional

  9. neural network based load frequency control for restructuring power

    African Journals Online (AJOL)

    2012-03-01

    Mar 1, 2012 ... power system is chosen and load frequency con- trol of this system is made by a ANN controller and a conventional PI controller. Basically, power system consists of a governor, a turbine, and a generator with feedback of reg- ulation constant. System also includes step load change input to the generator.

  10. The myth of nitric oxide in central cardiovascular control by the nucleus tractus solitarii

    Directory of Open Access Journals (Sweden)

    Talman W.T.

    1997-01-01

    Full Text Available Considerable evidence suggests that nitroxidergic mechanisms in the nucleus tractus solitarii (NTS participate in cardiovascular reflex control. Much of that evidence, being based on responses to nitric oxide precursors or inhibitors of nitric oxide synthesis, has been indirect and circumstantial. We sought to directly determine cardiovascular responses to nitric oxide donors microinjected into the NTS and to determine if traditional receptor mechanisms might account for responses to certain of these donors in the central nervous system. Anesthetized adult Sprague Dawley rats that were instrumented for recording arterial pressure and heart rate were used in the physiological studies. Microinjection of nitric oxide itself into the NTS did not produce any cardiovascular responses and injection of sodium nitroprusside elicited minimal depressor responses. The S-nitrosothiols, S-nitrosoglutathione (GSNO, S-nitrosoacetylpenicillamine (SNAP, and S-nitroso-D-cysteine (D-SNC produced no significant cardiovascular responses while injection of S-nitroso-L-cysteine (L-SNC elicited brisk, dose-dependent depressor and bradycardic responses. In contrast, injection of glyceryl trinitrate elicited minimal pressor responses without associated changes in heart rate. It is unlikely that the responses to L-SNC were dependent on release of nitric oxide in that 1 the responses were not affected by injection of oxyhemoglobin or an inhibitor of nitric oxide synthesis prior to injection of L-SNC and 2 L- and D-SNC released identical amounts of nitric oxide when exposed to brain tissue homogenates. Although GSNO did not independently affect blood pressure, its injection attenuated responses to subsequent injection of L-SNC. Furthermore, radioligand binding studies suggested that in rat brain synaptosomes there is a saturable binding site for GSNO that is displaced from that site by L-SNC. The studies suggest that S-nitrosocysteine, not nitric oxide, may be an

  11. Elman neural network for modeling and predictive control of delayed dynamic systems

    Directory of Open Access Journals (Sweden)

    Wysocki Antoni

    2016-03-01

    Full Text Available The objective of this paper is to present a modified structure and a training algorithm of the recurrent Elman neural network which makes it possible to explicitly take into account the time-delay of the process and a Model Predictive Control (MPC algorithm for such a network. In MPC the predicted output trajectory is repeatedly linearized on-line along the future input trajectory, which leads to a quadratic optimization problem, nonlinear optimization is not necessary. A strongly nonlinear benchmark process (a simulated neutralization reactor is considered to show advantages of the modified Elman neural network and the discussed MPC algorithm. The modified neural model is more precise and has a lower number of parameters in comparison with the classical Elman structure. The discussed MPC algorithm with on-line linearization gives similar trajectories as MPC with nonlinear optimization repeated at each sampling instant.

  12. Gender difference in blood pressure control and cardiovascular risk factors in Americans with diagnosed hypertension.

    Science.gov (United States)

    Ong, Kwok Leung; Tso, Annette W K; Lam, Karen S L; Cheung, Bernard M Y

    2008-04-01

    Hypertension is an important risk factor for cardiovascular disease, which is the leading cause of death in women. We, therefore, analyzed gender-specific trends in the control of blood pressure and prevalence of 5 other cardiovascular risk factors (central obesity, elevated total cholesterol, low high-density lipoprotein cholesterol, hyperglycemia, and smoking) among adults with diagnosed hypertension in the United States. We included 3475 participants aged >or=18 years with diagnosed hypertension in the National Health and Nutrition Examination Survey 1999-2004. The age-adjusted prevalence of uncontrolled blood pressure was 50.8+/-2.1% in men and 55.9+/-1.5% in women, which were not significantly different and had not changed significantly with time. Central obesity, elevated total cholesterol level, and low high-density lipoprotein cholesterol were significantly more prevalent in women than in men (79.0+/-1.0%, 61.3+/-1.6%, and 39.7+/-1.6% versus 63.9+/-1.6%, 48.1+/-1.8%, and 35.6+/-1.7%, respectively; Por=3 of the 6 risk factors studied was higher in women than in men (52.5+/-1.4% versus 40.9+/-1.8%; Pblood pressure control in women with diagnosed hypertension was not significantly inferior compared with men and had not changed significantly in 1999-2004. However, women had higher prevalence of other concomitant cardiovascular risk factors. Although there is room for improvement in blood pressure control, our study has highlighted the importance of addressing concomitant cardiovascular risk factors in women with hypertension.

  13. Prescribed performance synchronization controller design of fractional-order chaotic systems: An adaptive neural network control approach

    Directory of Open Access Journals (Sweden)

    Yuan Li

    2017-03-01

    Full Text Available In this study, an adaptive neural network synchronization (NNS approach, capable of guaranteeing prescribed performance (PP, is designed for non-identical fractional-order chaotic systems (FOCSs. For PP synchronization, we mean that the synchronization error converges to an arbitrary small region of the origin with convergence rate greater than some function given in advance. Neural networks are utilized to estimate unknown nonlinear functions in the closed-loop system. Based on the integer-order Lyapunov stability theorem, a fractional-order adaptive NNS controller is designed, and the PP can be guaranteed. Finally, simulation results are presented to confirm our results.

  14. Prescribed performance synchronization controller design of fractional-order chaotic systems: An adaptive neural network control approach

    Science.gov (United States)

    Li, Yuan; Lv, Hui; Jiao, Dongxiu

    2017-03-01

    In this study, an adaptive neural network synchronization (NNS) approach, capable of guaranteeing prescribed performance (PP), is designed for non-identical fractional-order chaotic systems (FOCSs). For PP synchronization, we mean that the synchronization error converges to an arbitrary small region of the origin with convergence rate greater than some function given in advance. Neural networks are utilized to estimate unknown nonlinear functions in the closed-loop system. Based on the integer-order Lyapunov stability theorem, a fractional-order adaptive NNS controller is designed, and the PP can be guaranteed. Finally, simulation results are presented to confirm our results.

  15. Estimating neural background input with controlled and fast perturbations: A bandwidth comparison between inhibitory opsins and neural circuits

    Directory of Open Access Journals (Sweden)

    David Eriksson

    2016-08-01

    Full Text Available To test the importance of a certain cell type or brain area it is common to make a lack of function experiment in which the neuronal population of interest is inhibited. Here we review physiological and methodological constraints for making controlled perturbations using the corticothalamic circuit as an example. The brain with its many types of cells and rich interconnectivity offers many paths through which a perturbation can spread within a short time. To understand the side effects of the perturbation one should record from those paths. We find that ephaptic effects, gap-junctions, and fast chemical synapses are so fast that they can react to the perturbation during the few milliseconds it takes for an opsin to change the membrane potential. The slow chemical synapses, astrocytes, extracellular ions and vascular signals, will continue to give their physiological input for around 20 milliseconds before they also react to the perturbation. Although we show that some pathways can react within milliseconds the strength/speed reported in this review should be seen as an upper bound since we have omitted how polysynaptic signals are attenuated. Thus the number of additional recordings that has to be made to control for the perturbation side effects is expected to be fewer than proposed here. To summarize, the reviewed literature not only suggests that it is possible to make controlled lack of function experiments, but, it also suggests that such a lack of function experiment can be used to measure the context of local neural computations.

  16. Gas Turbine Engine Control Design Using Fuzzy Logic and Neural Networks

    Directory of Open Access Journals (Sweden)

    M. Bazazzadeh

    2011-01-01

    Full Text Available This paper presents a successful approach in designing a Fuzzy Logic Controller (FLC for a specific Jet Engine. At first, a suitable mathematical model for the jet engine is presented by the aid of SIMULINK. Then by applying different reasonable fuel flow functions via the engine model, some important engine-transient operation parameters (such as thrust, compressor surge margin, turbine inlet temperature, etc. are obtained. These parameters provide a precious database, which train a neural network. At the second step, by designing and training a feedforward multilayer perceptron neural network according to this available database; a number of different reasonable fuel flow functions for various engine acceleration operations are determined. These functions are used to define the desired fuzzy fuel functions. Indeed, the neural networks are used as an effective method to define the optimum fuzzy fuel functions. At the next step, we propose a FLC by using the engine simulation model and the neural network results. The proposed control scheme is proved by computer simulation using the designed engine model. The simulation results of engine model with FLC illustrate that the proposed controller achieves the desired performance and stability.

  17. Neural representation of reward probability: evidence from the illusion of control.

    Science.gov (United States)

    Kool, Wouter; Getz, Sarah J; Botvinick, Matthew M

    2013-06-01

    To support reward-based decision-making, the brain must encode potential outcomes both in terms of their incentive value and their probability of occurrence. Recent research has made it clear that the brain bears multiple representations of reward magnitude, meaning that a single choice option may be represented differently-and even inconsistently-in different brain areas. There are some hints that the same may be true for reward probability. Preliminary evidence hints that, even as systematic distortions of probability are expressed in behavior, these may not always be uniformly reflected at the neural level: Some neural representations of probability may be immune from such distortions. This study provides new evidence consistent with this possibility. Participants in a behavioral experiment displayed a classic "illusion of control," providing higher estimates of reward probability for gambles they had chosen than for identical gambles that were imposed on them. However, an fMRI study of the same task revealed that neural prediction error signals, arising when gamble outcomes were revealed, were unaffected by the illusion of control. The resulting behavioral-neural dissociation reinforces the case for multiple, inconsistent internal representations of reward probability, while also prompting a reinterpretation of the illusion of control effect itself.

  18. Neural Network Course Changing and Track Keeping Controller for a Submarine

    Directory of Open Access Journals (Sweden)

    Dur Muhammad Pathan

    2012-10-01

    Full Text Available This paper presents the performance of ANN (Artificial Neural Networks technique for the development of controller for heading motions of submarine. A MLP (Multi-Layer Preceptron FFNN (Feed-Forward Neural Network is used for development of controller. Supervised type of learning is used for training of network by using back-propagation Algorithm. The training is performed by providing a nonlinear sliding mode controller as a supervisor. The development of controller is based on nonlinear decoupled heading model of a submarine without consideration of external environmental disturbances. To demonstrate the robustness of controller the performance of controller is tested in different operating conditions: course changing, track keeping and under the influence of sea currents. Simulations results show that in all cases, the heading error comes to zero, which indicates that the actual heading converges to the desired heading in finite time. The maximum error is observed 0.5o for 45o command angle, in presence of sea currents. The result demonstrates that the performance neural network controller has been robust.

  19. Neural Adaptive Decentralized Coordinated Control with Fault-Tolerant Capability for DFIGs under Stochastic Disturbances

    Directory of Open Access Journals (Sweden)

    Xiao-ming Li

    2017-01-01

    Full Text Available At present, most methodologies proposed to control over double fed induction generators (DFIGs are based on single machine model, where the interactions from network have been neglected. Considering this, this paper proposes a decentralized coordinated control of DFIG based on the neural interaction measurement observer. An artificial neural network is employed to approximate the nonlinear model of DFIG, and the approximation error due to neural approximation has been considered. A robust stabilization technique is also proposed to override the effect of approximation error. A H2 controller and a H∞ controller are employed to achieve specified engineering purposes, respectively. Then, the controller design is formulated as a mixed H2/H∞ optimization with constrains of regional pole placement and proportional plus integral (PI structure, which can be solved easily by using linear matrix inequality (LMI technology. The results of simulations are presented and discussed, which show the capabilities of DFIG with the proposed control strategy to fault-tolerant control of the maximum power point tracking (MPPT under slight sensor faults, low voltage ride-through (LVRT, and its contribution to power system transient stability support.

  20. Motivation and cognitive control: from behavior to neural mechanism.

    Science.gov (United States)

    Botvinick, Matthew; Braver, Todd

    2015-01-03

    Research on cognitive control and executive function has long recognized the relevance of motivational factors. Recently, however, the topic has come increasingly to center stage, with a surge of new studies examining the interface of motivation and cognitive control. In the present article we survey research situated at this interface, considering work from cognitive and social psychology and behavioral economics, but with a particular focus on neuroscience research. We organize existing findings into three core areas, considering them in the light of currently vying theoretical perspectives. Based on the accumulated evidence, we advocate for a view of control function that treats it as a domain of reward-based decision making. More broadly, we argue that neuroscientific evidence plays a critical role in understanding the mechanisms by which motivation and cognitive control interact. Opportunities for further cross-fertilization between behavioral and neuroscientific research are highlighted.

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

    Directory of Open Access Journals (Sweden)

    Xiaohu Li

    2013-01-01

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

  2. Neural control and precision of flight muscle activation in Drosophila

    OpenAIRE

    Lehmann, Fritz-Olaf; Bartussek, Jan

    2016-01-01

    Precision of motor commands is highly relevant in a large context of various locomotor behaviors, including stabilization of body posture, heading control and directed escape responses. While posture stability and heading control in walking and swimming animals benefit from high friction via ground reaction forces and elevated viscosity of water, respectively, flying animals have to cope with comparatively little aerodynamic friction on body and wings. Although low frictional damping in fligh...

  3. Adaptive Neural Back-Stepping Control with Constrains for a Flexible Air-Breathing Hypersonic Vehicle

    Directory of Open Access Journals (Sweden)

    Pengfei Wang

    2015-01-01

    Full Text Available The design of an adaptive neural back-stepping control for a flexible air-breathing hypersonic vehicle (AHV in the presence of input constraint and aerodynamic uncertainty is discussed. Based on functional decomposition, the dynamics can be decomposed into the velocity subsystem and the altitude subsystem. To guarantee the exploited controller’s robustness with respect to parametric uncertainties, neural network (NN is applied to approximate the lumped uncertainty of each subsystem of AHV model. The exceptional contribution is that novel auxiliary systems are introduced to compensate both the tracking errors and desired control laws, based on which the explored controller can still provide effective tracking of velocity and altitude commands when the actuators are saturated. Finally, simulation studies are made to illustrate the effectiveness of the proposed control approach in spite of the flexible effects, system uncertainties, and varying disturbances.

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

    Directory of Open Access Journals (Sweden)

    Xin Zhang

    2014-01-01

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

  5. Simulation, State Estimation and Control of Nonlinear Superheater Attemporator using Neural Networks

    DEFF Research Database (Denmark)

    Bendtsen, Jan Dimon; Sørensen, O.

    2000-01-01

    This paper considers the use of neural networks for nonlinear state estimation, system identification and control. As a case study we use data taken from a nonlinear injection valve for a superheater attemporator at a power plant. One neural network is trained as a nonlinear simulation model......-by-sample linearizations and state estimates provided by the observer network. Simulation studies show that the nonlinear observer-based control loop performs better than a similar control loop based on a linear observer....... of the process, then another network is trained to act as a combined state and parameter estimator for the process. The observer network incorporates smoothing of the parameter estimates in the form of regularization. A pole placement controller is designed which takes advantage of the sample...

  6. Simulation, State Estimation and Control of Nonlinear Superheater Attemporator using Neural Networks

    DEFF Research Database (Denmark)

    Bendtsen, Jan Dimon; Sørensen, O.

    1999-01-01

    This paper considers the use of neural networks for nonlinear state estimation, system identification and control. As a case study we use data taken from a nonlinear injection valve for a superheater attemporator at a power plant. One neural network is trained as a nonlinear simulation model......-by-sample linearizations and state estimates provided by the observer network. Simulation studies show that the nonlinear observer-based control loop performs better than a similar control loop based on a linear observer....... of the process, then another network is trained to act as a combined state and parameter estimator for the process. The observer network incorporates smoothing of the parameter estimates in the form of regularization. A pole placement controller is designed which takes advantage of the sample...

  7. Neural network based semi-active control strategy for structural vibration mitigation with magnetorheological damper

    DEFF Research Database (Denmark)

    Bhowmik, Subrata

    2011-01-01

    This paper presents a neural network based semi-active control method for a rotary type magnetorheological (MR) damper. The characteristics of the MR damper are described by the classic Bouc-Wen model, and the performance of the proposed control method is evaluated in terms of a base exited shear...... frame structure. As demonstrated in the literature effective damping of flexible structures is obtained by a suitable combination of pure friction and negative damper stiffness. This damper model is rate-independent and fully described by the desired shape of the hysteresis loops or force...... mode of the structure. The neural network control is then developed to reproduce the desired force based on damper displacement and velocity as network input, and it is therefore referred to as an amplitude dependent model reference control method. An inverse model of the MR damper is needed...

  8. Neural networkbased semi-active control strategy for structural vibration mitigation with magnetorheological damper

    DEFF Research Database (Denmark)

    Bhowmik, Subrata

    2011-01-01

    This paper presents a neural network based semi-active control method for a rotary type magnetorheological (MR) damper. The characteristics of the MR damper are described by the classic Bouc-Wen model, and the performance of the proposed control method is evaluated in terms of a base exited shear...... frame structure. As demonstrated in the literature effective damping of flexible structures is obtained by a suitable combination of pure friction and negative damper stiffness. This damper model is rate-independent and fully described by the desired shape of the hysteresis loops or force...... mode of the structure. The neural network control is then developed to reproduce the desired force based on damper displacement and velocity as network input, and it is therefore referred to as an amplitude dependent model reference control method. An inverse model of the MR damper is needed...

  9. Artificial neural networks in variable process control: application in particleboard manufacture

    Energy Technology Data Exchange (ETDEWEB)

    Esteban, L. G.; Garcia Fernandez, F.; Palacios, P. de; Conde, M.

    2009-07-01

    Artificial neural networks are an efficient tool for modelling production control processes using data from the actual production as well as simulated or design of experiments data. In this study two artificial neural networks were combined with the control process charts and it was checked whether the data obtained by the networks were valid for variable process control in particleboard manufacture. The networks made it possible to obtain the mean and standard deviation of the internal bond strength of the particleboard within acceptable margins using known data of thickness, density, moisture content, swelling and absorption. The networks obtained met the acceptance criteria for test values from non-standard test methods, as well as the criteria for using these values in statistical process control. (Author) 47 refs.

  10. Two neural network algorithms for designing optimal terminal controllers with open final time

    Science.gov (United States)

    Plumer, Edward S.

    1992-01-01

    Multilayer neural networks, trained by the backpropagation through time algorithm (BPTT), have been used successfully as state-feedback controllers for nonlinear terminal control problems. Current BPTT techniques, however, are not able to deal systematically with open final-time situations such as minimum-time problems. Two approaches which extend BPTT to open final-time problems are presented. In the first, a neural network learns a mapping from initial-state to time-to-go. In the second, the optimal number of steps for each trial run is found using a line-search. Both methods are derived using Lagrange multiplier techniques. This theoretical framework is used to demonstrate that the derived algorithms are direct extensions of forward/backward sweep methods used in N-stage optimal control. The two algorithms are tested on a Zermelo problem and the resulting trajectories compare favorably to optimal control results.

  11. Biomechanics as a window into the neural control of movement

    Directory of Open Access Journals (Sweden)

    Latash Mark L.

    2016-09-01

    Full Text Available Biomechanics and motor control are discussed as parts of a more general science, physics of living systems. Major problems of biomechanics deal with exact definition of variables and their experimental measurement. In motor control, major problems are associated with formulating currently unknown laws of nature specific for movements by biological objects. Mechanics-based hypotheses in motor control, such as those originating from notions of a generalized motor program and internal models, are non-physical. The famous problem of motor redundancy is wrongly formulated; it has to be replaced by the principle of abundance, which does not pose computational problems for the central nervous system. Biomechanical methods play a central role in motor control studies. This is illustrated with studies with the reconstruction of hypothetical control variables and those exploring motor synergies within the framework of the uncontrolled manifold hypothesis. Biomechanics and motor control have to merge into physics of living systems, and the earlier this process starts the better.

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

    DEFF Research Database (Denmark)

    Yao, Wei; Fang, Jiakun; Zhao, Ping

    2013-01-01

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

  13. SPEED CONTROL FOR THREE PHASE INDUCTION MOTOR USING ADALINE NEURAL NETWORKS

    Directory of Open Access Journals (Sweden)

    Bogdan Codreş

    2015-12-01

    Full Text Available The speed control of the three phase induction motor is still a challenging problem. Although the results obtained by means of the conventional control are very good, many researches in this area are ongoing. The authors propose a different control approach based on artificial intelligence. The control signals for speed, torque and flux regulation are computed using three ADALINE (Adaptive Linear Neuron neural networks. The numerical simulations are made in Simulink and the obtained results are compared with the conventional drive approach (cascaded PI controller

  14. Integrated chassis control for vehicle rollover prevention with neural network time-to-rollover warning metrics

    OpenAIRE

    Bing Zhu; Qi Piao; Jian Zhao; Litong Guo

    2016-01-01

    The rollover of road vehicles is one of the most serious problems related to transportation safety. In this article, a novel rollover prevention control system composed of rollover warning and integrated chassis control algorithm is proposed. First, a conventional time-to-rollover warning algorithm was presented based on the 3-degree of freedom vehicle model. In order to improve the precision of vehicle rollover prediction, a back-propagation neural network was adopted to regulate time to rol...

  15. On Improved Least Squares Regression and Artificial Neural Network Meta-Models for Simulation via Control Variates

    Science.gov (United States)

    2016-09-15

    neural network using applications across varied industries . Alam et al. [2] showed the factorial design did not perform as well as other designs (mentioned...composite design with a neural network using applications across varied industries . Alam et al. [2] showed the central composite design did not perform as...ON IMPROVED LEAST SQUARES REGRESSION & ARTIFICIAL NEURAL NETWORK META-MODELS FOR SIMULATION VIA CONTROL VARIATES DISSERTATION Michael P. Gibb

  16. Neural correlates of disbalanced motor control in major depression.

    Science.gov (United States)

    Walther, S; Höfle, O; Federspiel, A; Horn, H; Hügli, S; Wiest, R; Strik, W; Müller, T J

    2012-01-01

    Motor retardation is a common symptom of major depressive disorder (MDD). Despite the existence of various assessment methods, little is known on the pathobiology of motor retardation. We aimed to elucidate aspects of motor control investigating the association of objective motor activity and resting state cerebral blood flow (CBF). Nineteen control subjects and 20 MDD patients were investigated using arterial spin labeling (ASL) at 3T in the morning to quantify resting state CBF. Afterwards wrist actigraphy was recorded for 24h. CBF, group and activity level (AL) were entered into a whole brain general linear model. MDD patients had reduced AL. Both groups had linear associations of AL and CBF in bilateral rostral prefrontal cortex. Groups differed in four clusters associated with motor control. In controls a positive association was found in the left caudal cingulate zone (CCZ) and an inverse association in the right external globus pallidus (GPe). MDD patients had positive associations in the right orbitofrontal cortex and inverse associations in the left supplemental motor area. Patients were on antidepressant medication. The pattern of associations between CBF and AL suggest disbalanced motor control in MDD. Findings are in line with the hypothesis of dopamine deficits contributing to motor retardation in MDD. Copyright © 2011 Elsevier B.V. All rights reserved.

  17. Diagonal recurrent neural network based adaptive control of nonlinear dynamical systems using lyapunov stability criterion.

    Science.gov (United States)

    Kumar, Rajesh; Srivastava, Smriti; Gupta, J R P

    2017-03-01

    In this paper adaptive control of nonlinear dynamical systems using diagonal recurrent neural network (DRNN) is proposed. The structure of DRNN is a modification of fully connected recurrent neural network (FCRNN). Presence of self-recurrent neurons in the hidden layer of DRNN gives it an ability to capture the dynamic behaviour of the nonlinear plant under consideration (to be controlled). To ensure stability, update rules are developed using lyapunov stability criterion. These rules are then used for adjusting the various parameters of DRNN. The responses of plants obtained with DRNN are compared with those obtained when multi-layer feed forward neural network (MLFFNN) is used as a controller. Also, in example 4, FCRNN is also investigated and compared with DRNN and MLFFNN. Robustness of the proposed control scheme is also tested against parameter variations and disturbance signals. Four simulation examples including one-link robotic manipulator and inverted pendulum are considered on which the proposed controller is applied. The results so obtained show the superiority of DRNN over MLFFNN as a controller. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

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

    Science.gov (United States)

    Chen, Weisheng; Hua, Shaoyong; Zhang, Huaguang

    2015-02-01

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

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

    Directory of Open Access Journals (Sweden)

    Владлен Николаевич Кузнецов

    2015-06-01

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

  20. Control of glycemia and cardiovascular risk factors in patients with type 2 diabetes in primary care in Catalonia (Spain)

    National Research Council Canada - National Science Library

    Vinagre, Irene; Mata-Cases, Manel; Hermosilla, Eduard; Morros, Rosa; Fina, Francesc; Rosell, Magdalena; Castell, Conxa; Franch-Nadal, Josep; Bolíbar, Bonaventura; Mauricio, Didac

    2012-01-01

    The objective of this study was to analyze the clinical characteristics and levels of glycemic and cardiovascular risk factor control in patients with type 2 diabetes that are in primary health care...

  1. Intelligent control a hybrid approach based on fuzzy logic, neural networks and genetic algorithms

    CERN Document Server

    Siddique, Nazmul

    2014-01-01

    Intelligent Control considers non-traditional modelling and control approaches to nonlinear systems. Fuzzy logic, neural networks and evolutionary computing techniques are the main tools used. The book presents a modular switching fuzzy logic controller where a PD-type fuzzy controller is executed first followed by a PI-type fuzzy controller thus improving the performance of the controller compared with a PID-type fuzzy controller.  The advantage of the switching-type fuzzy controller is that it uses one rule-base thus minimises the rule-base during execution. A single rule-base is developed by merging the membership functions for change of error of the PD-type controller and sum of error of the PI-type controller. Membership functions are then optimized using evolutionary algorithms. Since the two fuzzy controllers were executed in series, necessary further tuning of the differential and integral scaling factors of the controller is then performed. Neural-network-based tuning for the scaling parameters of t...

  2. Neural emotion regulation circuitry underlying anxiolytic effects of perceived control over pain.

    Science.gov (United States)

    Salomons, Tim V; Nusslock, Robin; Detloff, Allison; Johnstone, Tom; Davidson, Richard J

    2015-02-01

    Anxiolytic effects of perceived control have been observed across species. In humans, neuroimaging studies have suggested that perceived control and cognitive reappraisal reduce negative affect through similar mechanisms. An important limitation of extant neuroimaging studies of perceived control in terms of directly testing this hypothesis, however, is the use of within-subject designs, which confound participants' affective response to controllable and uncontrollable stress. To compare neural and affective responses when participants were exposed to either uncontrollable or controllable stress, two groups of participants received an identical series of stressors (thermal pain stimuli). One group ("controllable") was led to believe they had behavioral control over the pain stimuli, whereas another ("uncontrollable") believed they had no control. Controllable pain was associated with decreased state anxiety, decreased activation in amygdala, and increased activation in nucleus accumbens. In participants who perceived control over the pain, reduced state anxiety was associated with increased functional connectivity between each of these regions and ventral lateral/ventral medial pFC. The location of pFC findings is consistent with regions found to be critical for the anxiolytic effects of perceived control in rodents. Furthermore, interactions observed between pFC and both amygdala and nucleus accumbens are remarkably similar to neural mechanisms of emotion regulation through reappraisal in humans. These results suggest that perceived control reduces negative affect through a general mechanism involved in the cognitive regulation of emotion.

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

    Science.gov (United States)

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

    2015-02-01

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

  4. Neural control of computer cursor velocity by decoding motor cortical spiking activity in humans with tetraplegia

    Science.gov (United States)

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

    2008-12-01

    Computer-mediated connections between human motor cortical neurons and assistive devices promise to improve or restore lost function in people with paralysis. Recently, a pilot clinical study of an intracortical neural interface system demonstrated that a tetraplegic human was able to obtain continuous two-dimensional control of a computer cursor using neural activity recorded from his motor cortex. This control, however, was not sufficiently accurate for reliable use in many common computer control tasks. Here, we studied several central design choices for such a system including the kinematic representation for cursor movement, the decoding method that translates neuronal ensemble spiking activity into a control signal and the cursor control task used during training for optimizing the parameters of the decoding method. In two tetraplegic participants, we found that controlling a cursor's velocity resulted in more accurate closed-loop control than controlling its position directly and that cursor velocity control was achieved more rapidly than position control. Control quality was further improved over conventional linear filters by using a probabilistic method, the Kalman filter, to decode human motor cortical activity. Performance assessment based on standard metrics used for the evaluation of a wide range of pointing devices demonstrated significantly improved cursor control with velocity rather than position decoding. Disclosure. JPD is the Chief Scientific Officer and a director of Cyberkinetics Neurotechnology Systems (CYKN); he holds stock and receives compensation. JDS has been a consultant for CYKN. LRH receives clinical trial support from CYKN.

  5. Quaternion-based adaptive output feedback attitude control of spacecraft using Chebyshev neural networks.

    Science.gov (United States)

    Zou, An-Min; Dev Kumar, Krishna; Hou, Zeng-Guang

    2010-09-01

    This paper investigates the problem of output feedback attitude control of an uncertain spacecraft. Two robust adaptive output feedback controllers based on Chebyshev neural networks (CNN) termed adaptive neural networks (NN) controller-I and adaptive NN controller-II are proposed for the attitude tracking control of spacecraft. The four-parameter representations (quaternion) are employed to describe the spacecraft attitude for global representation without singularities. The nonlinear reduced-order observer is used to estimate the derivative of the spacecraft output, and the CNN is introduced to further improve the control performance through approximating the spacecraft attitude motion. The implementation of the basis functions of the CNN used in the proposed controllers depends only on the desired signals, and the smooth robust compensator using the hyperbolic tangent function is employed to counteract the CNN approximation errors and external disturbances. The adaptive NN controller-II can efficiently avoid the over-estimation problem (i.e., the bound of the CNNs output is much larger than that of the approximated unknown function, and hence, the control input may be very large) existing in the adaptive NN controller-I. Both adaptive output feedback controllers using CNN can guarantee that all signals in the resulting closed-loop system are uniformly ultimately bounded. For performance comparisons, the standard adaptive controller using the linear parameterization of spacecraft attitude motion is also developed. Simulation studies are presented to show the advantages of the proposed CNN-based output feedback approach over the standard adaptive output feedback approach.

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

    Science.gov (United States)

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

    2012-01-01

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

  7. Neural control of fast nonlinear systems--application to a turbocharged SI engine with VCT.

    Science.gov (United States)

    Colin, Guillaume; Chamaillard, Yann; Bloch, Gérard; Corde, Gilles

    2007-07-01

    Today, (engine) downsizing using turbocharging appears as a major way in reducing fuel consumption and pollutant emissions of spark ignition (SI) engines. In this context, an efficient control of the air actuators [throttle, turbo wastegate, and variable camshaft timing (VCT)] is needed for engine torque control. This paper proposes a nonlinear model-based control scheme which combines separate, but coordinated, control modules. Theses modules are based on different control strategies: internal model control (IMC), model predictive control (MPC), and optimal control. It is shown how neural models can be used at different levels and included in the control modules to replace physical models, which are too complex to be online embedded, or to estimate nonmeasured variables. The results obtained from two different test benches show the real-time applicability and good control performance of the proposed methods.

  8. Neural control of finger movement via intracortical brain–machine interface

    Science.gov (United States)

    Irwin, Z. T.; Schroeder, K. E.; Vu, P. P.; Bullard, A. J.; Tat, D. M.; Nu, C. S.; Vaskov, A.; Nason, S. R.; Thompson, D. E.; Bentley, J. N.; Patil, P. G.; Chestek, C. A.

    2017-12-01

    Objective. Intracortical brain–machine interfaces (BMIs) are a promising source of prosthesis control signals for individuals with severe motor disabilities. Previous BMI studies have primarily focused on predicting and controlling whole-arm movements; precise control of hand kinematics, however, has not been fully demonstrated. Here, we investigate the continuous decoding of precise finger movements in rhesus macaques. Approach. In order to elicit precise and repeatable finger movements, we have developed a novel behavioral task paradigm which requires the subject to acquire virtual fingertip position targets. In the physical control condition, four rhesus macaques performed this task by moving all four fingers together in order to acquire a single target. This movement was equivalent to controlling the aperture of a power grasp. During this task performance, we recorded neural spikes from intracortical electrode arrays in primary motor cortex. Main results. Using a standard Kalman filter, we could reconstruct continuous finger movement offline with an average correlation of ρ  =  0.78 between actual and predicted position across four rhesus macaques. For two of the monkeys, this movement prediction was performed in real-time to enable direct brain control of the virtual hand. Compared to physical control, neural control performance was slightly degraded; however, the monkeys were still able to successfully perform the task with an average target acquisition rate of 83.1%. The monkeys’ ability to arbitrarily specify fingertip position was also quantified using an information throughput metric. During brain control task performance, the monkeys achieved an average 1.01 bits s‑1 throughput, similar to that achieved in previous studies which decoded upper-arm movements to control computer cursors using a standard Kalman filter. Significance. This is, to our knowledge, the first demonstration of brain control of finger-level fine motor skills. We

  9. Neural control of finger movement via intracortical brain-machine interface.

    Science.gov (United States)

    Irwin, Z T; Schroeder, K E; Vu, P P; Bullard, A J; Tat, D M; Nu, C S; Vaskov, A; Nason, S R; Thompson, D E; Bentley, J N; Patil, P G; Chestek, C A

    2017-12-01

    Intracortical brain-machine interfaces (BMIs) are a promising source of prosthesis control signals for individuals with severe motor disabilities. Previous BMI studies have primarily focused on predicting and controlling whole-arm movements; precise control of hand kinematics, however, has not been fully demonstrated. Here, we investigate the continuous decoding of precise finger movements in rhesus macaques. In order to elicit precise and repeatable finger movements, we have developed a novel behavioral task paradigm which requires the subject to acquire virtual fingertip position targets. In the physical control condition, four rhesus macaques performed this task by moving all four fingers together in order to acquire a single target. This movement was equivalent to controlling the aperture of a power grasp. During this task performance, we recorded neural spikes from intracortical electrode arrays in primary motor cortex. Using a standard Kalman filter, we could reconstruct continuous finger movement offline with an average correlation of ρ  =  0.78 between actual and predicted position across four rhesus macaques. For two of the monkeys, this movement prediction was performed in real-time to enable direct brain control of the virtual hand. Compared to physical control, neural control performance was slightly degraded; however, the monkeys were still able to successfully perform the task with an average target acquisition rate of 83.1%. The monkeys' ability to arbitrarily specify fingertip position was also quantified using an information throughput metric. During brain control task performance, the monkeys achieved an average 1.01 bits s -1 throughput, similar to that achieved in previous studies which decoded upper-arm movements to control computer cursors using a standard Kalman filter. This is, to our knowledge, the first demonstration of brain control of finger-level fine motor skills. We believe that these results represent an important step

  10. Circadian rhythm of temperature preference and its neural control in Drosophila

    Science.gov (United States)

    Kaneko, Haruna; Head, Lauren M.; Ling, Jinli; Tang, Xin; Liu, Yilin; Hardin, Paul E.; Emery, Patrick; Hamada, Fumika N.

    2012-01-01

    A daily body temperature rhythm (BTR) is critical for the maintenance of homeostasis in mammals. While mammals use internal energy to regulate body temperature, ectotherms typically regulate body temperature behaviorally [1]. Some ectotherms maintain homeostasis via a daily temperature preference rhythm (TPR) [2], but the underlying mechanisms are largely unknown. Here, we show that Drosophila exhibit a daily circadian clock dependent TPR that resembles mammalian BTR. Pacemaker neurons critical for locomotor activity are not necessary for TPR; instead, the dorsal neuron 2s (DN2s), whose function was previously unknown, is sufficient. This indicates that TPR, like BTR, is controlled independently from locomotor activity. Therefore, the mechanisms controlling temperature fluctuations in fly TPR and mammalian BTR may share parallel features. Taken together, our results reveal the existence of a novel DN2- based circadian neural circuit that specifically regulates TPR; thus, understanding the mechanisms of TPR will shed new light on the function and neural control of circadian rhythms. PMID:22981774

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

    Science.gov (United States)

    Singh, H P; Sukavanam, N

    2012-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Y. D. Song

    2013-01-01

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

  13. Metabolic and cardiovascular risk factor control in a diabetic cohort. Four-year results.

    Science.gov (United States)

    Llamazares Iglesias, Ofelia; Sastre Marcos, Julia; Peña Cortés, Virginia; Luque Pazos, Alessandra; Cánovas Gaillemin, Bárbara; Vicente Delgado, Almudena; Marco Martínez, Amparo; López López, José

    2012-02-01

    To assess control of blood glucose and other cardiovascular risk factors in diabetic patients monitored at an outpatient endocrinology clinic. To ascertain treatment used and its changes over time. A cohort of 424 randomly selected diabetic patients (both type 1 and type 2) was monitored from 2004 to 2008. Final cohort size was 343 patients. Data were collected about epidemiological characteristics, cardiovascular risk factors, chronic complications, glycemic, lipid and blood pressure control, and treatment at baseline and 4 years. After 4 years, the proportion of patients achieving glycosylated hemoglobin levels less than 7% remained stable (type 1: 18.5% in 2004 vs 21.7% in 2008, type 2: 26.6% vs 26.5%). The degree of achievement of lipid and blood pressure (BP) control levels increased in both groups. The complexity of treatment schemes used to achieve these results significantly increased. Stabilization of glycemic control after 4 years of follow-up was a positive result, considering the long course of diabetes, progressive pancreatic function impairment, and complexity of our cohort. Treatment optimization significantly improved BP and lipid control in the study group. Copyright © 2011 SEEN. Published by Elsevier Espana. All rights reserved.

  14. The neural control of fast vs. slow vergence eye movements.

    Science.gov (United States)

    Cullen, Kathleen E; Van Horn, Marion R

    2011-06-01

    When looking between targets located in three-dimensional space, information about relative depth is sent from the visual cortex to the motor control centers in the brainstem, which are responsible for generating appropriate motor commands to move the eyes. Surprisingly, how the neurons in the brainstem use the depth information supplied by the visual cortex to precisely aim each eye on a visual target remains highly controversial. This review will consider the results of recent studies that have focused on determining how individual neurons contribute to realigning gaze when we look between objects located at different depths. In particular, the results of new experiments provide compelling evidence that the majority of saccadic neurons dynamically encode the movement of an individual eye, and show that the time-varying discharge of the saccadic neuron population encodes the drive required to account for vergence facilitation during disconjugate saccades. Notably, these results suggest that an additional input (i.e. from a separate vergence subsystem) is not required to shape the activity of motoneurons during disconjugate saccades. Furthermore, whereas motoneurons drive both fast and slow vergence movements, saccadic neurons discharge only during fast vergence movements, emphasizing the existence of distinct premotor pathways for controlling fast vs. slow vergence. Taken together, these recent findings contradict the traditional view that the brain is circuited with independent pathways for conjugate and vergence control, and thus provide an important new insight into how the brain controls three-dimensional gaze shifts. © 2011 The Authors. European Journal of Neuroscience © 2011 Federation of European Neuroscience Societies and Blackwell Publishing Ltd.

  15. Neural mechanisms underlying cognitive control of men with lifelong antisocial behavior.

    Science.gov (United States)

    Schiffer, Boris; Pawliczek, Christina; Mu Ller, Bernhard; Forsting, Michael; Gizewski, Elke; Leygraf, Norbert; Hodgins, Sheilagh

    2014-04-30

    Results of meta-analyses suggested subtle deficits in cognitive control among antisocial individuals. Because almost all studies focused on children with conduct problems or adult psychopaths, however, little is known about cognitive control mechanisms among the majority of persistent violent offenders who present an antisocial personality disorder (ASPD). The present study aimed to determine whether offenders with ASPD, relative to non-offenders, display dysfunction in the neural mechanisms underlying cognitive control and to assess the extent to which these dysfunctions are associated with psychopathic traits and trait impulsivity. Participants comprised 21 violent offenders and 23 non-offenders who underwent event-related functional magnetic resonance imaging while performing a non-verbal Stroop task. The offenders, relative to the non-offenders, exhibited reduced response time interference and a different pattern of conflict- and error-related activity in brain areas involved in cognitive control, attention, language, and emotion processing, that is, the anterior cingulate, dorsolateral prefrontal, superior temporal and postcentral cortices, putamen, thalamus, and amygdala. Moreover, between-group differences in behavioural and neural responses revealed associations with core features of psychopathy and attentional impulsivity. Thus, the results of the present study confirmed the hypothesis that offenders with ASPD display alterations in the neural mechanisms underlying cognitive control and that those alterations relate, at least in part, to personality characteristics. Copyright © 2014. Published by Elsevier Ireland Ltd.

  16. An Inverse Neural Controller Based on the Applicability Domain of RBF Network Models

    Directory of Open Access Journals (Sweden)

    Alex Alexandridis

    2018-01-01

    Full Text Available This paper presents a novel methodology of generic nature for controlling nonlinear systems, using inverse radial basis function neural network models, which may combine diverse data originating from various sources. The algorithm starts by applying the particle swarm optimization-based non-symmetric variant of the fuzzy means (PSO-NSFM algorithm so that an approximation of the inverse system dynamics is obtained. PSO-NSFM offers models of high accuracy combined with small network structures. Next, the applicability domain concept is suitably tailored and embedded into the proposed control structure in order to ensure that extrapolation is avoided in the controller predictions. Finally, an error correction term, estimating the error produced by the unmodeled dynamics and/or unmeasured external disturbances, is included to the control scheme to increase robustness. The resulting controller guarantees bounded input-bounded state (BIBS stability for the closed loop system when the open loop system is BIBS stable. The proposed methodology is evaluated on two different control problems, namely, the control of an experimental armature-controlled direct current (DC motor and the stabilization of a highly nonlinear simulated inverted pendulum. For each one of these problems, appropriate case studies are tested, in which a conventional neural controller employing inverse models and a PID controller are also applied. The results reveal the ability of the proposed control scheme to handle and manipulate diverse data through a data fusion approach and illustrate the superiority of the method in terms of faster and less oscillatory responses.

  17. A Pressure Control Method for Emulsion Pump Station Based on Elman Neural Network

    Directory of Open Access Journals (Sweden)

    Chao Tan

    2015-01-01

    Full Text Available In order to realize pressure control of emulsion pump station which is key equipment of coal mine in the safety production, the control requirements were analyzed and a pressure control method based on Elman neural network was proposed. The key techniques such as system framework, pressure prediction model, pressure control model, and the flowchart of proposed approach were presented. Finally, a simulation example was carried out and comparison results indicated that the proposed approach was feasible and efficient and outperformed others.

  18. The effects of inhibitory control training for preschoolers on reasoning ability and neural activity

    DEFF Research Database (Denmark)

    Liu, Qian; Zhu, Xinyi; Ziegler, Albert

    2015-01-01

    Inhibitory control (including response inhibition and interference control) develops rapidly during the preschool period and is important for early cognitive development. This study aimed to determine the training and transfer effects on response inhibition in young children. Children in the trai......Inhibitory control (including response inhibition and interference control) develops rapidly during the preschool period and is important for early cognitive development. This study aimed to determine the training and transfer effects on response inhibition in young children. Children....... Furthermore, gender differences in the training-induced changes in neural activity were found in preschoolers....

  19. Impact of a Population Health Management Intervention on Disparities in Cardiovascular Disease Control.

    Science.gov (United States)

    James, Aisha; Berkowitz, Seth A; Ashburner, Jeffrey M; Chang, Yuchiao; Horn, Daniel M; O'Keefe, Sandra M; Atlas, Steven J

    2018-01-08

    Healthcare systems use population health management programs to improve the quality of cardiovascular disease care. Adding a dedicated population health coordinator (PHC) who identifies and reaches out to patients not meeting cardiovascular care goals to these programs may help reduce disparities in cardiovascular care. To determine whether a program that used PHCs decreased racial/ethnic disparities in LDL cholesterol and blood pressure (BP) control. Retrospective difference-in-difference analysis. Twelve thousdand five hundred fifty-five primary care patients with cardiovascular disease (cohort for LDL analysis) and 41,183 with hypertension (cohort for BP analysis). From July 1, 2014-December 31, 2014, 18 practices used an information technology (IT) system to identify patients not meeting LDL and BP goals; 8 practices also received a PHC. We examined whether having the PHC plus IT system, compared with having the IT system alone, decreased racial/ethnic disparities, using difference-in-difference analysis of data collected before and after program implementation. Meeting guideline concordant LDL and BP goals. At baseline, there were racial/ethnic disparities in meeting LDL (p = 0.007) and BP (p = 0.0003) goals. Comparing practices with and without a PHC, and accounting for pre-intervention LDL control, non-Hispanic white patients in PHC practices had improved odds of LDL control (OR 1.20 95% CI 1.09-1.32) compared with those in non-PHC practices. Non-Hispanic black (OR 1.15 95% CI 0.80-1.65) and Hispanic (OR 1.29 95% CI 0.66-2.53) patients saw similar, but non-significant, improvements in LDL control. For BP control, non-Hispanic white patients in PHC practices (versus non-PHC) improved (OR 1.13 95% CI 1.05-1.22). Non-Hispanic black patients (OR 1.17 95% CI 0.94-1.45) saw similar, but non-statistically significant, improvements in BP control, but Hispanic (OR 0.90 95% CI 0.59-1.36) patients did not. Interaction testing confirmed that disparities did not

  20. Developmental plasticity in the neural control of breathing.

    Science.gov (United States)

    Bavis, Ryan W; MacFarlane, Peter M

    2017-01-01

    The respiratory control system undergoes a diversity of morphological and physiological transformational stages during intrauterine development as it prepares to transition into an air-breathing lifestyle. Following birth, the respiratory system continues to develop and may pass through critical periods of heightened vulnerability to acute environmental stressors. Over a similar time course, however, the developing respiratory control system exhibits substantial capacity to undergo plasticity in response to chronic or repeated environmental stimuli. A hallmark of developmental plasticity is that it requires an interaction between a stimulus (e.g., hypoxia, hyperoxia, or psychosocial stress) and a unique window of development; the same stimulus experienced beyond the boundaries of this critical window of plasticity (e.g., at maturity), therefore, will have little if any appreciable effect on the phenotype. However, there are major gaps in our understanding of the mechanistic basis of developmental plasticity. Filling these gaps in our knowledge may be crucial to advancing our understanding of the developmental origin of adult health and disease. In this review, we: i) begin by clarifying some ambiguities in the definitions of plasticity and related terms that have arisen in recent years; ii) describe various levels of the respiratory control system where plasticity can (or has been identified to) occur; iii) emphasize the importance of understanding the mechanistic basis of developmental plasticity; iv) consider factors that influence whether developmental plasticity is permanent or whether function can be restored; v) discuss genetic and sex-based variation in the expression of developmental plasticity; and vi) provide a translational perspective to developmental plasticity. Copyright © 2016 Elsevier Inc. All rights reserved.

  1. Sliding Mode and Neural Network Control of Sensorless PMSM Controlled System for Power Consumption and Performance Improvement

    Directory of Open Access Journals (Sweden)

    Ming-Shyan Wang

    2017-11-01

    Full Text Available This paper deals with the design of sliding mode control and neural network compensation for a sensorless permanent magnet synchronous motor (PMSM controlled system that is able to improve both power consumption and speed response performance. The position sensor of PMSM is unreliable in harsh environments. Therefore, the sensorless control technique is widely proposed in industry. A sliding mode observer can estimate the rotor angle and has the robustness to load disturbance and parameter variations. However, the sliding mode observer is not conducive to standstill and low speed conditions because the amplitude of the back EMF is almost zero. As a result, this paper combines an iterative sliding mode observer (ISMO and neural networks (NNs as an angle compensator to improve the above problems. A dsPIC30F6010A-based PMSM sensorless drive system is implemented to validate the proposed algorithm. The simulation and experimental results prove its effectiveness.

  2. Noise controlled synchronization in potassium coupled neural models

    DEFF Research Database (Denmark)

    Postnov, Dmitry E; Ryazanova, Ludmila S; Zhirin, Roman A

    2007-01-01

    The paper applies biologically plausible models to investigate how noise input to small ensembles of neurons, coupled via the extracellular potassium concentration, can influence their firing patterns. Using the noise intensity and the volume of the extracellular space as control parameters, we...... show that potassium induced depolarization underlies the formation of noise-induced patterns such as delayed firing and synchronization. These phenomena are associated with the appearance of new time scales in the distribution of interspike intervals that may be significant for the spatio...

  3. Prevention and Control of Cardiovascular Disease in the Rapidly Changing Economy of China.

    Science.gov (United States)

    Wu, Yangfeng; Benjamin, Emelia J; MacMahon, Stephen

    2016-06-14

    With one-fifth of the world's total population, China's prevention and control of cardiovascular disease (CVD) may affect the success of worldwide efforts to achieve sustainable CVD reduction. Understanding China's current cardiovascular epidemic requires awareness of the economic development in the past decades. The rapid economic transformations (industrialization, marketization, urbanization, globalization, and informationalization) contributed to the aging demography, unhealthy lifestyles, and environmental changes. The latter have predisposed to increasing cardiovascular risk factors and the CVD pandemic. Rising CVD rates have had a major economic impact, which has challenged the healthcare system and the whole society. With recognition of the importance of health, initial political steps and national actions have been taken to address the CVD epidemic. Looking to the future, we recommend that 4 priorities should be taken: pursue multisectorial government and nongovernment strategies targeting the underlying causes of CVD (the whole-of-government and whole-of-society policy); give priority to prevention; reform the healthcare system to fit the nature of noncommunicable diseases; and conduct research for evidence-based, low-cost, simple, sustainable, and scalable interventions. By pursuing the 4 priorities, the pandemic of CVD and other major noncommunicable diseases in China will be reversed and the global sustainable development goal achieved. © 2016 American Heart Association, Inc.

  4. Heart matters: Gender and racial differences cardiovascular disease risk factor control among veterans.

    Science.gov (United States)

    Goldstein, Karen M; Melnyk, S Dee; Zullig, Leah L; Stechuchak, Karen M; Oddone, Eugene; Bastian, Lori A; Rakley, Susan; Olsen, Maren K; Bosworth, Hayden B

    2014-01-01

    Cardiovascular disease (CVD) is the leading cause of mortality for U.S. women. Racial minorities are a particularly vulnerable population. The increasing female veteran population has an higher prevalence of certain cardiovascular risk factors compared with non-veteran women; however, little is known about gender and racial differences in cardiovascular risk factor control among veterans. We used analysis of variance, adjusting for age, to compare gender and racial differences in three risk factors that predispose to CVD (diabetes, hypertension, and hyperlipidemia) in a cohort of high-risk veterans eligible for enrollment in a clinical trial, including 23,955 men and 1,010 women. Low-density lipoprotein (LDL) values were higher in women veterans than men with age-adjusted estimated mean values of 111.7 versus 97.6 mg/dL (p LDL values, and hemoglobin A1c levels, although the differences were only significant among men. Female veterans have higher LDL cholesterol levels than male veterans and African-American veterans have higher BP, LDL cholesterol, and A1c levels than Whites after adjusting for age. Further examination of CVD gender and racial disparities in this population may help to develop targeted treatments and strategies applicable to the general population. Published by Elsevier Inc.

  5. Recomendaciones educativas para la prevención y el control de las enfermedades cardiovasculares

    Directory of Open Access Journals (Sweden)

    Alfredo Darío Espinosa Brito

    2011-02-01

    Full Text Available Cada vez parece más evidente que para brindar un cuidado efectivo a las personas con enfermedades cardiovasculares, o en riesgo de padecerlas, tanto los pacientes, como sus familiares y los profesionales sanitarios, deben expandir sus competencias profesionales, para lograr resultados favorables. En este trabajo se plantean y justifican cuatro recomendaciones educativas relacionadas con el paciente, la familia y los profesionales de la salud, así como se proponen acciones para llevarlas a la práctica en el contexto latinoamericano actual. Se sugiere capacitar al personal sanitario para brindar una asistencia integral y un cuidado centrado en el paciente, a través de una educación continuada apropiada, para poder educar, al mismo tiempo, a los pacientes y sus familiares. También se aboga por promover sistemas organizados para educar a las personas en riesgo y a los pacientes con enfermedades cardiovasculares y de este modo favorecer su propio control y autocuidado. Finalmente, se plantea la educación a la población en la identificación temprana de los síntomas de las urgencias cardiovasculares para obtener mejores resultados en la asistencia médica en esos casos.

  6. Peripheral chemoreceptor control of cardiovascular function at rest and during exercise in heart failure patients.

    Science.gov (United States)

    Edgell, Heather; McMurtry, M Sean; Haykowsky, Mark J; Paterson, Ian; Ezekowitz, Justin A; Dyck, Jason R B; Stickland, Michael K

    2015-04-01

    Peripheral chemoreceptor activity/sensitivity is enhanced in chronic heart failure (HF), and sensitivity is linked to greater mortality. This study aimed to determine the role of the peripheral chemoreceptor in cardiovascular control at rest and during exercise in HF patients and controls. Clinically stable HF patients (n = 11; ejection fraction: 39 ± 5%) and risk-matched controls (n = 10; ejection fraction: 65 ± 2%) performed randomized trials with or without dopamine infusion (2 μg·min(-1)·kg(-1)) at rest and during 40% maximal voluntary contraction handgrip (HG) exercise, and a resting trial of 2 min of inspired 100% oxygen. Both dopamine and hyperoxia were used to inhibit the peripheral chemoreceptor. At rest in HF patients, dopamine decreased ventilation (P = 0.02), decreased total peripheral resistance index (P = 0.003), and increased cardiac and stroke indexes (P ≤ 0.01), yet there was no effect of dopamine on these variables in controls (P ≥ 0.7). Hyperoxia lowered ventilation in HF (P = 0.01), but not in controls (P = 0.9), indicating suppression of the peripheral chemoreceptors in HF. However, no decrease of total peripheral resistance index was observed in HF. As expected, HG increased heart rate, ventilation, and brachial conductance of the nonexercising arm in controls and HF patients. During dopamine infusion, there were no changes in mean arterial pressure, heart rate, or ventilation responses to HG in either group (P ≥ 0.26); however, brachial conductance increased with dopamine in the control group (P = 0.004), but decreased in HF (P = 0.02). Our findings indicate that the peripheral chemoreceptor contributes to cardiovascular control at rest in HF patients and during exercise in risk-matched controls. Copyright © 2015 the American Physiological Society.

  7. Robust fuzzy neural network sliding mode control scheme for IPMSM drives

    Science.gov (United States)

    Leu, V. Q.; Mwasilu, F.; Choi, H. H.; Lee, J.; Jung, J. W.

    2014-07-01

    This article proposes a robust fuzzy neural network sliding mode control (FNNSMC) law for interior permanent magnet synchronous motor (IPMSM) drives. The proposed control strategy not only guarantees accurate and fast command speed tracking but also it ensures the robustness to system uncertainties and sudden speed and load changes. The proposed speed controller encompasses three control terms: a decoupling control term which compensates for nonlinear coupling factors using nominal parameters, a fuzzy neural network (FNN) control term which approximates the ideal control components and a sliding mode control (SMC) term which is proposed to compensate for the errors of that approximation. Next, an online FNN training methodology, which is developed using the Lyapunov stability theorem and the gradient descent method, is proposed to enhance the learning capability of the FNN. Moreover, the maximum torque per ampere (MTPA) control is incorporated to maximise the torque generation in the constant torque region and increase the efficiency of the IPMSM drives. To verify the effectiveness of the proposed robust FNNSMC, simulations and experiments are performed by using MATLAB/Simulink platform and a TI TMS320F28335 DSP on a prototype IPMSM drive setup, respectively. Finally, the simulated and experimental results indicate that the proposed design scheme can achieve much better control performances (e.g. more rapid transient response and smaller steady-state error) when compared to the conventional SMC method, especially in the case that there exist system uncertainties.

  8. Exercise-induced neuronal plasticity in central autonomic networks: role in cardiovascular control.

    Science.gov (United States)

    Michelini, Lisete C; Stern, Javier E

    2009-09-01

    It is now well established that brain plasticity is an inherent property not only of the developing but also of the adult brain. Numerous beneficial effects of exercise, including improved memory, cognitive function and neuroprotection, have been shown to involve an important neuroplastic component. However, whether major adaptive cardiovascular adjustments during exercise, needed to ensure proper blood perfusion of peripheral tissues, also require brain neuroplasticity, is presently unknown. This review will critically evaluate current knowledge on proposed mechanisms that are likely to underlie the continuous resetting of baroreflex control of heart rate during/after exercise and following exercise training. Accumulating evidence indicates that not only somatosensory afferents (conveyed by skeletal muscle receptors, baroreceptors and/or cardiopulmonary receptors) but also projections arising from central command neurons (in particular, peptidergic hypothalamic pre-autonomic neurons) converge into the nucleus tractus solitarii (NTS) in the dorsal brainstem, to co-ordinate complex cardiovascular adaptations during dynamic exercise. This review focuses in particular on a reciprocally interconnected network between the NTS and the hypothalamic paraventricular nucleus (PVN), which is proposed to act as a pivotal anatomical and functional substrate underlying integrative feedforward and feedback cardiovascular adjustments during exercise. Recent findings supporting neuroplastic adaptive changes within the NTS-PVN reciprocal network (e.g. remodelling of afferent inputs, structural and functional neuronal plasticity and changes in neurotransmitter content) will be discussed within the context of their role as important underlying cellular mechanisms supporting the tonic activation and improved efficacy of these central pathways in response to circulatory demand at rest and during exercise, both in sedentary and in trained individuals. We hope this review will stimulate

  9. Autonomic nervous system control of the cardiovascular and respiratory systems in asthma.

    Science.gov (United States)

    Lewis, M J; Short, A L; Lewis, K E

    2006-10-01

    Patients with asthma have exaggerated bronchoconstriction of their airways in response to certain indirect (e.g. cold air, allergens, dust, exercise) or direct (e.g. inhaled methacholine) stimuli. This 'hyper-reactivity' usually co-exists with airway inflammation, although the pathophysiological mechanisms underlying these changes are not fully understood. It is likely that this hyper-reactivity is associated with abnormal autonomic nervous system (ANS) control. In particular, the parasympathetic (vagal) component of the ANS appears to be implicated in the pathogenesis of asthma. In addition, several studies have suggested the existence of differential alteration in ANS function following exercise in asthmatics compared with non-asthmatic individuals. Several early studies suggested that the altered autonomic control of airway calibre in asthma might be reflected by a parallel change in heart rate. Cardiac vagal reactivity does indeed appear to be increased in asthma, as demonstrated by the cardiac response to various autonomic functions tests. However, other studies have reported a lack of association between bronchial and cardiac vagal tone, and this is in accord with the concept of system-independent ANS control. This review provides a discussion of cardiovascular-autonomic changes associated with either the pathophysiology of asthma per se or with asthma pharmacotherapy treatment. Previous investigations are summarised suggesting an apparent association between altered autonomic-cardiovascular control and bronchial asthma. The full extent of autonomic dysfunction, and its clinical implications, has yet to be fully determined and should be the subject of future investigation.

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

    Directory of Open Access Journals (Sweden)

    Yiyong Gou

    2017-04-01

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

  11. Dynamic recurrent neural networks for stable adaptive control of wing rock motion

    Science.gov (United States)

    Kooi, Steven Boon-Lam

    Wing rock is a self-sustaining limit cycle oscillation (LCO) which occurs as the result of nonlinear coupling between the dynamic response of the aircraft and the unsteady aerodynamic forces. In this thesis, dynamic recurrent RBF (Radial Basis Function) network control methodology is proposed to control the wing rock motion. The concept based on the properties of the Presiach hysteresis model is used in the design of dynamic neural networks. The structure and memory mechanism in the Preisach model is analogous to the parallel connectivity and memory formation in the RBF neural networks. The proposed dynamic recurrent neural network has a feature for adding or pruning the neurons in the hidden layer according to the growth criteria based on the properties of ensemble average memory formation of the Preisach model. The recurrent feature of the RBF network deals with the dynamic nonlinearities and endowed temporal memories of the hysteresis model. The control of wing rock is a tracking problem, the trajectory starts from non-zero initial conditions and it tends to zero as time goes to infinity. In the proposed neural control structure, the recurrent dynamic RBF network performs identification process in order to approximate the unknown non-linearities of the physical system based on the input-output data obtained from the wing rock phenomenon. The design of the RBF networks together with the network controllers are carried out in discrete time domain. The recurrent RBF networks employ two separate adaptation schemes where the RBF's centre and width are adjusted by the Extended Kalman Filter in order to give a minimum networks size, while the outer networks layer weights are updated using the algorithm derived from Lyapunov stability analysis for the stable closed loop control. The issue of the robustness of the recurrent RBF networks is also addressed. The effectiveness of the proposed dynamic recurrent neural control methodology is demonstrated through simulations to

  12. Artificial neural networks: Principle and application to model based control of drying systems -- A review

    Energy Technology Data Exchange (ETDEWEB)

    Thyagarajan, T.; Ponnavaikko, M. [Crescent Engineering Coll., Madras (India); Shanmugam, J. [Madras Inst. of Tech. (India); Panda, R.C.; Rao, P.G. [Central Leather Research Inst., Madras (India)

    1998-07-01

    This paper reviews the developments in the model based control of drying systems using Artificial Neural Networks (ANNs). Survey of current research works reveals the growing interest in the application of ANN in modeling and control of non-linear, dynamic and time-variant systems. Over 115 articles published in this area are reviewed. All landmark papers are systematically classified in chronological order, in three distinct categories; namely, conventional feedback controllers, model based controllers using conventional methods and model based controllers using ANN for drying process. The principles of ANN are presented in detail. The problems and issues of the drying system and the features of various ANN models are dealt with up-to-date. ANN based controllers lead to smoother controller outputs, which would increase actuator life. The paper concludes with suggestions for improving the existing modeling techniques as applied to predicting the performance characteristics of dryers. The hybridization techniques, namely, neural with fuzzy logic and genetic algorithms, presented, provide, directions for pursuing further research for the implementation of appropriate control strategies. The authors opine that the information presented here would be highly beneficial for pursuing research in modeling and control of drying process using ANN. 118 refs.

  13. The exon junction complex component Magoh controls brain size by regulating neural stem cell division

    Science.gov (United States)

    Silver, Debra L.; Watkins-Chow, Dawn E.; Schreck, Karisa C.; Pierfelice, Tarran J.; Larson, Denise M.; Burnetti, Anthony J.; Liaw, Hung-Jiun; Myung, Kyungjae; Walsh, Christopher A.; Gaiano, Nicholas; Pavan, William J.

    2010-01-01

    Summary Brain structure and size requires precise division of neural stem cells (NSCs), which self-renew and generate intermediate neural progenitors (INPs) and neurons. The factors that regulate NSCs remain poorly understood, as do mechanistic explanations of how aberrant NSC division causes reduced brain size as seen in microcephaly. Here we demonstrate that Magoh, a component of the exon junction complex (EJC) that binds RNA, controls mouse cerebral cortical size by regulating NSC division. Magoh haploinsufficiency causes microcephaly due to INP depletion and neuronal apoptosis. Defective mitosis underlies these phenotypes as depletion of EJC components disrupts mitotic spindle orientation and integrity, chromosome number, and genomic stability. In utero rescue experiments revealed that a key function of Magoh is to control levels of the microcephaly-associated protein, LIS1, during neurogenesis. This study uncovers new requirements for the EJC in brain development, NSC maintenance, and mitosis, thus implicating this complex in the pathogenesis of microcephaly. PMID:20364144

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

    Directory of Open Access Journals (Sweden)

    Jidong Wang

    2017-01-01

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

  15. Inhibition and impulsivity: behavioral and neural basis of response control.

    Science.gov (United States)

    Bari, Andrea; Robbins, Trevor W

    2013-09-01

    In many circumstances alternative courses of action and thoughts have to be inhibited to allow the emergence of goal-directed behavior. However, this has not been the accepted view in the past and only recently has inhibition earned its own place in the neurosciences as a fundamental cognitive function. In this review we first introduce the concept of inhibition from early psychological speculations based on philosophical theories of the human mind. The broad construct of inhibition is then reduced to its most readily observable component which necessarily is its behavioral manifestation. The study of 'response inhibition' has the advantage of dealing with a relatively simple and straightforward process, the overriding of a planned or already initiated action. Deficient inhibitory processes profoundly affect everyday life, causing impulsive conduct which is generally detrimental for the individual. Impulsivity has been consistently linked to several types of addiction, attention deficit/hyperactivity disorder, mania and other psychiatric conditions. Our discussion of the behavioral assessment of impulsivity will focus on objective laboratory tasks of response inhibition that have been implemented in parallel for humans and other species with relatively few qualitative differences. The translational potential of these measures has greatly improved our knowledge of the neurobiological basis of behavioral inhibition and impulsivity. We will then review the current models of behavioral inhibition along with their expression via underlying brain regions, including those involved in the activation of the brain's emergency 'brake' operation, those engaged in more controlled and sustained inhibitory processes and other ancillary executive functions. Copyright © 2013 Elsevier Ltd. All rights reserved.

  16. Intelligent fuzzy-neural pattern generation and control of a quadrupedal bionic inspection robot

    Science.gov (United States)

    Sayfeddine, D.; Bulgakov, A. G.

    2017-02-01

    This paper represents a case study on ‘single leg single step’ pattern generation and control of quadrupedal bionic robot movement using intelligent fuzzy-neural approaches. The aim is to set up a flip-flop mechanical configuration allowing the robot to move one step forward. The same algorithm can be integrated to develop a full trajectory pattern as an interconnected task of global path planning for autonomous quadrupedal robots.

  17. Control of obesity and glucose intolerance via building neural stem cells in the hypothalamus

    OpenAIRE

    Li, Juxue; Tang, Yizhe; Purkayastha, Sudarshana; Yan, Jingqi; Cai, DongSheng

    2014-01-01

    Neural stem cells (NSCs) were recently revealed to exist in the hypothalamus of adult mice. Here, following our observation showing that a partial loss of hypothalamic NSCs caused weight gain and glucose intolerance, we studied if NSCs-based cell therapy could be developed to control these disorders. While hypothalamus-implanted NSCs failed to survive in mice with obesity, NF-κB inhibition induced survival and neurogenesis of these cells, leading to effects in counteracting obesity and glucos...

  18. 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.

  19. Complex Dynamical Network Control for Trajectory Tracking Using Delayed Recurrent Neural Networks

    Directory of Open Access Journals (Sweden)

    Jose P. Perez

    2014-01-01

    Full Text Available In this paper, the problem of trajectory tracking is studied. Based on the V-stability and Lyapunov theory, a control law that achieves the global asymptotic stability of the tracking error between a delayed recurrent neural network and a complex dynamical network is obtained. To illustrate the analytic results, we present a tracking simulation of a dynamical network with each node being just one Lorenz’s dynamical system and three identical Chen’s dynamical systems.

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

    Science.gov (United States)

    Mclauchlan, Robert A.

    1989-01-01

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

  1. Cardiovascular System Changes and Related Risk Factors in Acromegaly Patients: A Case-Control Study

    Science.gov (United States)

    Guo, Xiaopeng; Gao, Lu; Zhang, Shuo; Li, Yilin; Wu, Yue; Fang, Ligang; Deng, Kan; Yao, Yong; Lian, Wei; Wang, Renzhi; Xing, Bing

    2015-01-01

    Background. Cardiovascular complications are known to be the main determinants of reduced life expectancy and decreased quality of life in acromegaly patients. Our study aimed to provide insight into the cardiovascular changes that occur in acromegaly patients and to investigate the correlative risk factors. Methods. A total of 108 patients definitively diagnosed with acromegaly and 108 controls matched for age and gender were recruited into study and control groups, respectively. Standard echocardiography was performed on all of the participants, and data were collected and analyzed. Results. All acromegaly patients presented with structural cardiac changes, including a larger heart cavity, thicker myocardial walls, and increased great vessel diameters compared with the control group. Additionally, the acromegaly patients presented with reduced diastolic function. Aging and increased body mass index (BMI) were correlated with myocardial hypertrophy and diastolic dysfunction; a longer disease duration was correlated with larger great vessel diameters. Conclusions. Ageing and increased BMI are independent risk factors for acromegalic cardiomyopathy, and a long disease duration results in the expansion of great vessels. Increased efforts should be made to diagnose acromegaly at an early stage and to advise acromegaly patients to maintain a healthy weight. PMID:26600803

  2. Review of Cardiovascular Disease Prevention and Control Programs: International Experience and Challenges in China

    Directory of Open Access Journals (Sweden)

    Seng Chuen Tan

    2016-01-01

    Full Text Available Major cardiovascular risk factors in China, such as hyperlipidemia, hypertension, dietary factors, exposure to tobacco, diabetes, obesity and physical inactivity, have contributed to deteriorating trends in cardiovascular disease (CVD deaths. In past years, a number of CVD prevention programs have been initiated in European and American countries and successfully brought down CVD related death rate by involving various parties such as physicians, patients, government agencies and payers. However, there is rare published literature that systemically reviewed such experience, which would be highly valuable for China and other countries with high CVD burden. In this article, we review the published literature on CVD prevention and control programs and report on interviews of local and foreign experts to provide recommendations for China-specific CVD prevention and control programs. In order to provide practical suggestions, we describe the type of programs as patient, physician, pharmacist, nurse, or payer-focused. Based on this evidence and identified challenges in China, programs focusing on disease management, treatment adherence, physician/health care provider education, financial incentives, and integrated healthcare are recommended for the prevention and control of CVD in China.

  3. Cardiovascular System Changes and Related Risk Factors in Acromegaly Patients: A Case-Control Study.

    Science.gov (United States)

    Guo, Xiaopeng; Gao, Lu; Zhang, Shuo; Li, Yilin; Wu, Yue; Fang, Ligang; Deng, Kan; Yao, Yong; Lian, Wei; Wang, Renzhi; Xing, Bing

    2015-01-01

    Background. Cardiovascular complications are known to be the main determinants of reduced life expectancy and decreased quality of life in acromegaly patients. Our study aimed to provide insight into the cardiovascular changes that occur in acromegaly patients and to investigate the correlative risk factors. Methods. A total of 108 patients definitively diagnosed with acromegaly and 108 controls matched for age and gender were recruited into study and control groups, respectively. Standard echocardiography was performed on all of the participants, and data were collected and analyzed. Results. All acromegaly patients presented with structural cardiac changes, including a larger heart cavity, thicker myocardial walls, and increased great vessel diameters compared with the control group. Additionally, the acromegaly patients presented with reduced diastolic function. Aging and increased body mass index (BMI) were correlated with myocardial hypertrophy and diastolic dysfunction; a longer disease duration was correlated with larger great vessel diameters. Conclusions. Ageing and increased BMI are independent risk factors for acromegalic cardiomyopathy, and a long disease duration results in the expansion of great vessels. Increased efforts should be made to diagnose acromegaly at an early stage and to advise acromegaly patients to maintain a healthy weight.

  4. Cognitive control in adolescence: neural underpinnings and relation to self-report behaviors.

    Directory of Open Access Journals (Sweden)

    Jessica R Andrews-Hanna

    Full Text Available BACKGROUND: Adolescence is commonly characterized by impulsivity, poor decision-making, and lack of foresight. However, the developmental neural underpinnings of these characteristics are not well established. METHODOLOGY/PRINCIPAL FINDINGS: To test the hypothesis that these adolescent behaviors are linked to under-developed proactive control mechanisms, the present study employed a hybrid block/event-related functional Magnetic Resonance Imaging (fMRI Stroop paradigm combined with self-report questionnaires in a large sample of adolescents and adults, ranging in age from 14 to 25. Compared to adults, adolescents under-activated a set of brain regions implicated in proactive top-down control across task blocks comprised of difficult and easy trials. Moreover, the magnitude of lateral prefrontal activity in adolescents predicted self-report measures of impulse control, foresight, and resistance to peer pressure. Consistent with reactive compensatory mechanisms to reduced proactive control, older adolescents exhibited elevated transient activity in regions implicated in response-related interference resolution. CONCLUSIONS/SIGNIFICANCE: Collectively, these results suggest that maturation of cognitive control may be partly mediated by earlier development of neural systems supporting reactive control and delayed development of systems supporting proactive control. Importantly, the development of these mechanisms is associated with cognitive control in real-life behaviors.

  5. Design of Course-Keeping Controller for a Ship Based on Backstepping and Neural Networks

    Directory of Open Access Journals (Sweden)

    Qiang Zhang

    2017-06-01

    Full Text Available Due to the existence of uncertainties and the unknown time variant environmental disturbances for ship course nonlinear control system, the ship course adaptive neural network robust course-keeping controller is designed by combining the backstepping technique. The neural networks (NNs are employed for the compensating of the nonlinear term of the nonlinear ship course-keeping control system. The designed adaptive laws are designed to estimate the weights of NNs and the bounds of unknown environmental disturbances. The first order commander are introduced to solve the problem of repeating differential operations in the traditional backstepping design method, which let the designed controller easier to implement in navigation practice and structure simplicity. Theoretically, it indicates that the proposed controller can track the setting course in arbitrary expected accuracy, while keeping all control signals in the ship course control closed-loop system are uniformly ultimately bounded. Finally, the training ship of Dalian Maritime University is taken for example; simulation results illustrated the effectiveness and the robustness of the proposed controller.

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

    Directory of Open Access Journals (Sweden)

    Feng Zhao

    2015-08-01

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

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

    Science.gov (United States)

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

    2016-09-01

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

  8. Neural tube defects and maternal serum zinc and copper concentrations in mid-pregnancy: a case-control study.

    Science.gov (United States)

    McMichael, A J; Dreosti, I E; Ryan, P; Robertson, E F

    1994-10-17

    To assess the relationship between mid-pregnancy maternal serum zinc and copper concentrations and neural tube defects. A prospective case-control study during 1978-1988 within a statewide hospital-based neural tube defect screening program measuring maternal serum alpha-fetoprotein levels at mid-pregnancy. Cases were 69 women with fetuses with confirmed neural tube defects. Controls were 592 women with fetuses without neural tube defects who were individually matched to cases for hospital, calendar date of screening, age and parity; there was a variable control-to-case ratio. For both unmatched and adjusted matched analyses, mean maternal serum zinc concentration was higher in cases than controls (P = 0.02 and P = 0.03, respectively). There were no case-control differences for serum copper concentrations. Conditional logistic regression analysis showed a (statistically non-significant) 50% increase in risk of neural tube defects in women whose serum zinc concentration was more than two standard deviations above the population mean. Within the normal range of maternal serum zinc and copper concentrations there is no variation in risk of neural tube defects. However, women with very high serum zinc levels may have an increased risk of neural tube defects. This could reflect deficient maternal-to-fetal transfer of zinc in some of those individuals. Any such phenomenon would be manifest in observational, but not experimental, studies.

  9. Control of uncertain systems by feedback linearization with neural networks augmentation. Part II. Controller validation by numerical simulation

    Directory of Open Access Journals (Sweden)

    Adrian TOADER

    2010-09-01

    Full Text Available The paper was conceived in two parts. Part I, previously published in this journal, highlighted the main steps of adaptive output feedback control for non-affine uncertain systems, having a known relative degree. The main paradigm of this approach was the feedback linearization (dynamic inversion with neural network augmentation. Meanwhile, based on new contributions of the authors, a new paradigm, that of robust servomechanism problem solution, has been added to the controller architecture. The current Part II of the paper presents the validation of the controller hereby obtained by using the longitudinal channel of a hovering VTOL-type aircraft as mathematical model.

  10. Neural network-based optimal adaptive output feedback control of a helicopter UAV.

    Science.gov (United States)

    Nodland, David; Zargarzadeh, Hassan; Jagannathan, Sarangapani

    2013-07-01

    Helicopter unmanned aerial vehicles (UAVs) are widely used for both military and civilian operations. Because the helicopter UAVs are underactuated nonlinear mechanical systems, high-performance controller design for them presents a challenge. This paper introduces an optimal controller design via an output feedback for trajectory tracking of a helicopter UAV, using a neural network (NN). The output-feedback control system utilizes the backstepping methodology, employing kinematic and dynamic controllers and an NN observer. The online approximator-based dynamic controller learns the infinite-horizon Hamilton-Jacobi-Bellman equation in continuous time and calculates the corresponding optimal control input by minimizing a cost function, forward-in-time, without using the value and policy iterations. Optimal tracking is accomplished by using a single NN utilized for the cost function approximation. The overall closed-loop system stability is demonstrated using Lyapunov analysis. Finally, simulation results are provided to demonstrate the effectiveness of the proposed control design for trajectory tracking.

  11. Neural Network Control of CSTR for Reversible Reaction Using Reverence Model Approach

    Directory of Open Access Journals (Sweden)

    Duncan ALOKO

    2007-01-01

    Full Text Available In this work, non-linear control of CSTR for reversible reaction is carried out using Neural Network as design tool. The Model Reverence approach in used to design ANN controller. The idea is to have a control system that will be able to achieve improvement in the level of conversion and to be able to track set point change and reject load disturbance. We use PID control scheme as benchmark to study the performance of the controller. The comparison shows that ANN controller out perform PID in the extreme range of non-linearity.This paper represents a preliminary effort to design a simplified neutral network control scheme for a class of non-linear process. Future works will involve further investigation of the effectiveness of thin approach for the real industrial chemical process

  12. Parietal neural prosthetic control of a computer cursor in a graphical-user-interface task

    Science.gov (United States)

    Revechkis, Boris; Aflalo, Tyson NS; Kellis, Spencer; Pouratian, Nader; Andersen, Richard A.

    2014-12-01

    Objective. To date, the majority of Brain-Machine Interfaces have been used to perform simple tasks with sequences of individual targets in otherwise blank environments. In this study we developed a more practical and clinically relevant task that approximated modern computers and graphical user interfaces (GUIs). This task could be problematic given the known sensitivity of areas typically used for BMIs to visual stimuli, eye movements, decision-making, and attentional control. Consequently, we sought to assess the effect of a complex, GUI-like task on the quality of neural decoding. Approach. A male rhesus macaque monkey was implanted with two 96-channel electrode arrays in area 5d of the superior parietal lobule. The animal was trained to perform a GUI-like ‘Face in a Crowd’ task on a computer screen that required selecting one cued, icon-like, face image from a group of alternatives (the ‘Crowd’) using a neurally controlled cursor. We assessed whether the crowd affected decodes of intended cursor movements by comparing it to a ‘Crowd Off’ condition in which only the matching target appeared without alternatives. We also examined if training a neural decoder with the Crowd On rather than Off had any effect on subsequent decode quality. Main results. Despite the additional demands of working with the Crowd On, the animal was able to robustly perform the task under Brain Control. The presence of the crowd did not itself affect decode quality. Training the decoder with the Crowd On relative to Off had no negative influence on subsequent decoding performance. Additionally, the subject was able to gaze around freely without influencing cursor position. Significance. Our results demonstrate that area 5d recordings can be used for decoding in a complex, GUI-like task with free gaze. Thus, this area is a promising source of signals for neural prosthetics that utilize computing devices with GUI interfaces, e.g. personal computers, mobile devices, and tablet

  13. Development and Training of a Neural Controller for Hind Leg Walking in a Dog Robot

    Science.gov (United States)

    Hunt, Alexander; Szczecinski, Nicholas; Quinn, Roger

    2017-01-01

    Animals dynamically adapt to varying terrain and small perturbations with remarkable ease. These adaptations arise from complex interactions between the environment and biomechanical and neural components of the animal's body and nervous system. Research into mammalian locomotion has resulted in several neural and neuro-mechanical models, some of which have been tested in simulation, but few “synthetic nervous systems” have been implemented in physical hardware models of animal systems. One reason is that the implementation into a physical system is not straightforward. For example, it is difficult to make robotic actuators and sensors that model those in the animal. Therefore, even if the sensorimotor circuits were known in great detail, those parameters would not be applicable and new parameter values must be found for the network in the robotic model of the animal. This manuscript demonstrates an automatic method for setting parameter values in a synthetic nervous system composed of non-spiking leaky integrator neuron models. This method works by first using a model of the system to determine required motor neuron activations to produce stable walking. Parameters in the neural system are then tuned systematically such that it produces similar activations to the desired pattern determined using expected sensory feedback. We demonstrate that the developed method successfully produces adaptive locomotion in the rear legs of a dog-like robot actuated by artificial muscles. Furthermore, the results support the validity of current models of mammalian locomotion. This research will serve as a basis for testing more complex locomotion controllers and for testing specific sensory pathways and biomechanical designs. Additionally, the developed method can be used to automatically adapt the neural controller for different mechanical designs such that it could be used to control different robotic systems. PMID:28420977

  14. Bilingualism increases neural response consistency and attentional control: evidence for sensory and cognitive coupling.

    Science.gov (United States)

    Krizman, Jennifer; Skoe, Erika; Marian, Viorica; Kraus, Nina

    2014-01-01

    Auditory processing is presumed to be influenced by cognitive processes - including attentional control - in a top-down manner. In bilinguals, activation of both languages during daily communication hones inhibitory skills, which subsequently bolster attentional control. We hypothesize that the heightened attentional demands of bilingual communication strengthens connections between cognitive (i.e., attentional control) and auditory processing, leading to greater across-trial consistency in the auditory evoked response (i.e., neural consistency) in bilinguals. To assess this, we collected passively-elicited auditory evoked responses to the syllable [da] in adolescent Spanish-English bilinguals and English monolinguals and separately obtained measures of attentional control and language ability. Bilinguals demonstrated enhanced attentional control and more consistent brainstem and cortical responses. In bilinguals, but not monolinguals, brainstem consistency tracked with language proficiency and attentional control. We interpret these enhancements in neural consistency as the outcome of strengthened attentional control that emerged from experience communicating in two languages. Copyright © 2013 Elsevier Inc. All rights reserved.

  15. Adaptive Neural-Sliding Mode Control of Active Suspension System for Camera Stabilization

    Directory of Open Access Journals (Sweden)

    Feng Zhao

    2015-01-01

    Full Text Available The camera always suffers from image instability on the moving vehicle due to the unintentional vibrations caused by road roughness. This paper presents a novel adaptive neural network based on sliding mode control strategy to stabilize the image captured area of the camera. The purpose is to suppress vertical displacement of sprung mass with the application of active suspension system. Since the active suspension system has nonlinear and time varying characteristics, adaptive neural network (ANN is proposed to make the controller robustness against systematic uncertainties, which release the model-based requirement of the sliding model control, and the weighting matrix is adjusted online according to Lyapunov function. The control system consists of two loops. The outer loop is a position controller designed with sliding mode strategy, while the PID controller in the inner loop is to track the desired force. The closed loop stability and asymptotic convergence performance can be guaranteed on the basis of the Lyapunov stability theory. Finally, the simulation results show that the employed controller effectively suppresses the vibration of the camera and enhances the stabilization of the entire camera, where different excitations are considered to validate the system performance.

  16. Vibration control of uncertain multiple launch rocket system using radial basis function neural network

    Science.gov (United States)

    Li, Bo; Rui, Xiaoting

    2018-01-01

    Poor dispersion characteristics of rockets due to the vibration of Multiple Launch Rocket System (MLRS) have always restricted the MLRS development for several decades. Vibration control is a key technique to improve the dispersion characteristics of rockets. For a mechanical system such as MLRS, the major difficulty in designing an appropriate control strategy that can achieve the desired vibration control performance is to guarantee the robustness and stability of the control system under the occurrence of uncertainties and nonlinearities. To approach this problem, a computed torque controller integrated with a radial basis function neural network is proposed to achieve the high-precision vibration control for MLRS. In this paper, the vibration response of a computed torque controlled MLRS is described. The azimuth and elevation mechanisms of the MLRS are driven by permanent magnet synchronous motors and supposed to be rigid. First, the dynamic model of motor-mechanism coupling system is established using Lagrange method and field-oriented control theory. Then, in order to deal with the nonlinearities, a computed torque controller is designed to control the vibration of the MLRS when it is firing a salvo of rockets. Furthermore, to compensate for the lumped uncertainty due to parametric variations and un-modeled dynamics in the design of the computed torque controller, a radial basis function neural network estimator is developed to adapt the uncertainty based on Lyapunov stability theory. Finally, the simulated results demonstrate the effectiveness of the proposed control system and show that the proposed controller is robust with regard to the uncertainty.

  17. System identification of closed-loop cardiovascular control mechanisms: diabetic autonomic neuropathy

    Science.gov (United States)

    Mukkamala, R.; Mathias, J. M.; Mullen, T. J.; Cohen, R. J.; Freeman, R.

    1999-01-01

    We applied cardiovascular system identification (CSI) to characterize closed-loop cardiovascular regulation in patients with diabetic autonomic neuropathy (DAN). The CSI method quantitatively analyzes beat-to-beat fluctuations in noninvasively measured heart rate, arterial blood pressure (ABP), and instantaneous lung volume (ILV) to characterize four physiological coupling mechanisms, two of which are autonomically mediated (the heart rate baroreflex and the coupling of respiration, measured in terms of ILV, to heart rate) and two of which are mechanically mediated (the coupling of ventricular contraction to the generation of the ABP wavelet and the coupling of respiration to ABP). We studied 37 control and 60 diabetic subjects who were classified as having minimal, moderate, or severe DAN on the basis of standard autonomic tests. The autonomically mediated couplings progressively decreased with increasing severity of DAN, whereas the mechanically mediated couplings were essentially unchanged. CSI identified differences between the minimal DAN and control groups, which were indistinguishable based on the standard autonomic tests. CSI may provide a powerful tool for assessing DAN.

  18. Effect of Intensive Blood Pressure Control on Cardiovascular Remodeling in Hypertensive Patients with Nephrosclerosis

    Directory of Open Access Journals (Sweden)

    Otelio Randall

    2013-01-01

    Full Text Available Pulse pressure (PP, a marker of arterial system properties, has been linked to cardiovascular (CV complications. We examined (a association between unit changes of PP and (i composite CV outcomes and (ii development of left-ventricular hypertrophy (LVH and (b effect of mean arterial pressure (MAP control on rate of change in PP. We studied 1094 nondiabetics with nephrosclerosis in the African American Study of Kidney Disease and Hypertension. Subjects were randomly assigned to usual MAP goal (102–107 mmHg or a lower MAP goal (≤92 mmHg and randomized to beta-blocker, angiotensin converting enzyme inhibitor, or calcium channel blocker. After covariate adjustment, a higher PP was associated with increased risk of CV outcome (RR = 1.28, CI = 1.11–1.47, P<0.01 and new LVH (RR = 1.26, CI = 1.04–1.54, P=0.02. PP increased at a greater rate in the usual than in lower MAP groups (slope ± SE: 1.08 ± 0.15 versus 0.42 ± 0.15 mmHg/year, P=0.002, but not by the antihypertensive treatment assignment. Observations indicate that control to a lower MAP slows the progression of PP, a correlate of cardiovascular remodeling and complications, and may be beneficial to CV health.

  19. Cardiovascular and Postural Control Interactions during Hypergravity: Effects on Cerebral Autoregulation in Males and Females

    Science.gov (United States)

    Goswami, Nandu; Blaber, Andrew; Bareille, Marie-Pierre; Beck, Arnaud; Avan, Paul; Bruner, Michelle; Hinghofer-Szalkay, Helmut

    2012-07-01

    Orthostatic intolerance remains a problem upon return to Earth from the microgravity environment of spaceflight. A variety of conditions including hypovolemia, cerebral vasoconstriction, cerebral or peripheral vascular disease, or cardiac arrhythmias may result in syncope if the person remains upright. Current research indicates that there is a greater dependence on visual and somatosensory information at the beginning of space flight with a decreased otolith gain during prolonged space flight (Herault et al., 2002). The goal of the research is to further our understanding of the fundamental adaptive homeostatic mechanisms involved in gravity related changes in cardiovascular and postural function. Cardiovascular, cerebrovascular, and postural sensory motor control systems in male and female participants before, during, and after exposure to graded levels of hyper-G were investigated. Hypotheses: 1) Activation of skeletal muscle pump will be directly related to the degree of orthostatic stress. 2) Simultaneous measurement of heart rate, blood pressure and postural sway will predict cardio-postural stability. Blood pressure and heart rate (means and variability), postural sway, center of pressure (COP), baroreflex function, calf blood flow, middle cerebral artery blood flow, non-invasive intracranial pressure measurements, and two-breath CO2 were measured. Results from the study will be used to provide an integrated insight into mechanisms of cardio-postural control and cerebral autoregulation, which are important aspects of human health in flights to Moon, Mars and distant planets.

  20. The Effect of Music on Anxiety and Cardiovascular Indices in Patients Undergoing Coronary Artery Bypass Graft: A Randomized Controlled Trial.

    Science.gov (United States)

    Heidari, Saeide; Babaii, Atye; Abbasinia, Mohammad; Shamali, Mahdi; Abbasi, Mohammad; Rezaei, Mahboobe

    2015-12-01

    The instability of cardiovascular indices and anxiety disorders are common among patients undergoing coronary artery bypass graft (CABG) and could interfere with their recovery. Therefore, improving the cardiovascular indices and anxiety is essential. This study aimed to investigate the effect of music therapy on anxiety and cardiovascular indices in patients undergoing CABG. In this randomized controlled trial, 60 patients hospitalized in the cardiovascular surgical intensive care unit of Shahid Beheshti Hospital in Qom city, Iran, in 2013 were selected using a consecutive sampling method and randomly allocated into the experimental and control groups. In the experimental group, patients received 30 minutes of light music, whereas in the control group, patients had 30 minutes of rest in bed. The cardiovascular indices and anxiety were measured immediately before, immediately after and half an hour after the study. Data were analyzed using the chi-square test and repeated measures analysis of variance. Compared to the immediately before intervention, the mean anxiety scores immediately after and 30 minutes after the intervention were significantly lower in the experimental group (P 0.05). Music therapy is effective in decreasing anxiety among patients undergoing CABG. However, the intervention was not effective on cardiovascular indices. Music can effectively be used as a non-pharmacological method to manage anxiety after CABG.

  1. Association of cardiovascular emerging risk factors with acute coronary syndrome and stroke: A case-control study.

    Science.gov (United States)

    Martínez Linares, José Manuel; Guisado Barrilao, Rafael; Ocaña Peinado, Francisco Manuel; Salgado Parreño, Francisco Javier

    2016-12-01

    In this study, we estimated the risk of acute coronary syndrome and stroke associated with several emerging cardiovascular risk factors. This was a case-control study, where an age - and sex-matched acute coronary syndrome group and stroke group were compared with controls. Demographic and clinical data were collected through patient interviews, and blood samples were taken for analysis. In the bivariate analysis, all cardiovascular risk factors analyzed showed as predictors of acute coronary syndrome and stroke, except total cholesterol and smoking. In the multivariate logistic regression model for acute coronary syndrome, hypertension and body mass index, N-terminal section brain natriuretic peptide and pregnancy-associated plasma protein-A were independent predictors. For stroke, the predictors were hypertension, diabetes mellitus, body mass index, and N-terminal section brain natriuretic peptide. Controlling for age, sex, and classical cardiovascular risk factors, N-terminal section brain natriuretic peptide and pregnancy-associated plasma protein-A were independent emerging cardiovascular risk factors for acute coronary syndrome, but pregnancy-associated plasma protein-A was not for stroke. High levels of cardiovascular risk factors in individuals with no episodes of cardiovascular disease requires the implementation of prevention programs, given that at least half of them are modifiable. © 2016 John Wiley & Sons Australia, Ltd.

  2. Calculation of PID controller parameters by using a fuzzy neural network.

    Science.gov (United States)

    Lee, Ching-Hung; Teng, Ching-Cheng

    2003-07-01

    In this paper, we use the fuzzy neural network (FNN) to develop a formula for designing the proportional-integral-derivative (PID) controller. This PID controller satisfies the criteria of minimum integrated absolute error (IAE) and maximum of sensitivity (Ms). The FNN system is used to identify the relationship between plant model and controller parameters based on IAE and Ms. To derive the tuning rule, the dominant pole assignment method is applied to simplify our optimization processes. Therefore, the FNN system is used to automatically tune the PID controller for different system parameters so that neither theoretical methods nor numerical methods need be used. Moreover, the FNN-based formula can modify the controller to meet our specification when the system model changes. A simulation result for applying to the motor position control problem is given to demonstrate the effectiveness of our approach.

  3. Single- versus Multiobjective Optimization for Evolution of Neural Controllers in Ms. Pac-Man

    Directory of Open Access Journals (Sweden)

    Tse Guan Tan

    2013-01-01

    Full Text Available The objective of this study is to focus on the automatic generation of game artificial intelligence (AI controllers for Ms. Pac-Man agent by using artificial neural network (ANN and multiobjective artificial evolution. The Pareto Archived Evolution Strategy (PAES is used to generate a Pareto optimal set of ANNs that optimize the conflicting objectives of maximizing Ms. Pac-Man scores (screen-capture mode and minimizing neural network complexity. This proposed algorithm is called Pareto Archived Evolution Strategy Neural Network or PAESNet. Three different architectures of PAESNet were investigated, namely, PAESNet with fixed number of hidden neurons (PAESNet_F, PAESNet with varied number of hidden neurons (PAESNet_V, and the PAESNet with multiobjective techniques (PAESNet_M. A comparison between the single- versus multiobjective optimization is conducted in both training and testing processes. In general, therefore, it seems that PAESNet_F yielded better results in training phase. But the PAESNet_M successfully reduces the runtime operation and complexity of ANN by minimizing the number of hidden neurons needed in hidden layer and also it provides better generalization capability for controlling the game agent in a nondeterministic and dynamic environment.

  4. Hoxb1b controls oriented cell division, cell shape and microtubule dynamics in neural tube morphogenesis

    Science.gov (United States)

    Žigman, Mihaela; Laumann-Lipp, Nico; Titus, Tom; Postlethwait, John; Moens, Cecilia B.

    2014-01-01

    Hox genes are classically ascribed to function in patterning the anterior-posterior axis of bilaterian animals; however, their role in directing molecular mechanisms underlying morphogenesis at the cellular level remains largely unstudied. We unveil a non-classical role for the zebrafish hoxb1b gene, which shares ancestral functions with mammalian Hoxa1, in controlling progenitor cell shape and oriented cell division during zebrafish anterior hindbrain neural tube morphogenesis. This is likely distinct from its role in cell fate acquisition and segment boundary formation. We show that, without affecting major components of apico-basal or planar cell polarity, Hoxb1b regulates mitotic spindle rotation during the oriented neural keel symmetric mitoses that are required for normal neural tube lumen formation in the zebrafish. This function correlates with a non-cell-autonomous requirement for Hoxb1b in regulating microtubule plus-end dynamics in progenitor cells in interphase. We propose that Hox genes can influence global tissue morphogenesis by control of microtubule dynamics in individual cells in vivo. PMID:24449840

  5. Rotor Resistance Online Identification of Vector Controlled Induction Motor Based on Neural Network

    Directory of Open Access Journals (Sweden)

    Bo Fan

    2014-01-01

    Full Text Available Rotor resistance identification has been well recognized as one of the most critical factors affecting the theoretical study and applications of AC motor’s control for high performance variable frequency speed adjustment. This paper proposes a novel model for rotor resistance parameters identification based on Elman neural networks. Elman recurrent neural network is capable of performing nonlinear function approximation and possesses the ability of time-variable characteristic adaptation. Those influencing factors of specified parameter are analyzed, respectively, and various work states are covered to ensure the completeness of the training samples. Through signal preprocessing on samples and training dataset, different input parameters identifications with one network are compared and analyzed. The trained Elman neural network, applied in the identification model, is able to efficiently predict the rotor resistance in high accuracy. The simulation and experimental results show that the proposed method owns extensive adaptability and performs very well in its application to vector controlled induction motor. This identification method is able to enhance the performance of induction motor’s variable-frequency speed regulation.

  6. Effect of Mediterranean Diet in Diabetes Control and Cardiovascular Risk Modification: A Systematic Review

    Directory of Open Access Journals (Sweden)

    Dana eSleiman

    2015-04-01

    Full Text Available Background: Over the past few years, there has been a worldwide significant increase in the incidence of type II diabetes (T2DM with both increase in morbidity and mortality. Controlling diabetes through life style modifications, including diet and exercise has always been the cornerstone in diabetes management. As a matter of fact, a number of studies addressed the potential protective role of Mediterranean diet in diabetic patients. Increasing evidence suggests that the Mediterranean diet could be of benefit in diseases associated with chronic inflammation, including metabolic syndrome, diabetes, obesity as well as atherosclerosis, cancer, pulmonary diseases, and cognition disorders. Methods: A systematic review was conducted on the effect of Mediterranean diet in diabetes control and cardiovascular risk modification as well as the possible mechanism through which this diet might exhibit its beneficial role. We did a comprehensive search of multiple electronic databases such as Medline, Google Scholars, PubMed, and the Cochrane central register data until May 2014. We included cross-sectional, prospective and controlled clinical trials that looked at the associations between Mediterranean diet and indices of diabetes control such HbA1c, fasting glucose, and HOMA, in addition to cardiovascular and peripheral vascular outcomes.Outcome/Conclusion: Most of the studies showed favorable effects of Mediterranean diet on glycemic control and CVD, although a certain degree of controversy remains regarding some issues, such as obesity. Important methodological differences and limitations in the studies make it difficult to compare results, thus further longer term studies are needed to evaluate the long-term efficacy of the Mediterranean diet along with the possibility of explaining its mechanism.

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

    Science.gov (United States)

    Hurst, Jacob; Bull, Larry

    2006-01-01

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

  8. Vascular Endothelial Growth Factor Receptor 3 Controls Neural Stem Cell Activation in Mice and Humans

    Directory of Open Access Journals (Sweden)

    Jinah Han

    2015-02-01

    Full Text Available Neural stem cells (NSCs continuously produce new neurons within the adult mammalian hippocampus. NSCs are typically quiescent but activated to self-renew or differentiate into neural progenitor cells. The molecular mechanisms of NSC activation remain poorly understood. Here, we show that adult hippocampal NSCs express vascular endothelial growth factor receptor (VEGFR 3 and its ligand VEGF-C, which activates quiescent NSCs to enter the cell cycle and generate progenitor cells. Hippocampal NSC activation and neurogenesis are impaired by conditional deletion of Vegfr3 in NSCs. Functionally, this is associated with compromised NSC activation in response to VEGF-C and physical activity. In NSCs derived from human embryonic stem cells (hESCs, VEGF-C/VEGFR3 mediates intracellular activation of AKT and ERK pathways that control cell fate and proliferation. These findings identify VEGF-C/VEGFR3 signaling as a specific regulator of NSC activation and neurogenesis in mammals.

  9. Statistical control chart and neural network classification for improving human fall detection

    KAUST Repository

    Harrou, Fouzi

    2017-01-05

    This paper proposes a statistical approach to detect and classify human falls based on both visual data from camera and accelerometric data captured by accelerometer. Specifically, we first use a Shewhart control chart to detect the presence of potential falls by using accelerometric data. Unfortunately, this chart cannot distinguish real falls from fall-like actions, such as lying down. To bypass this difficulty, a neural network classifier is then applied only on the detected cases through visual data. To assess the performance of the proposed method, experiments are conducted on the publicly available fall detection databases: the University of Rzeszow\\'s fall detection (URFD) dataset. Results demonstrate that the detection phase play a key role in reducing the number of sequences used as input into the neural network classifier for classification, significantly reducing computational burden and achieving better accuracy.

  10. Cardiovascular disease risks in adult Native and Mexican Americans with a history of alcohol use disorders: association with cardiovascular autonomic control.

    Science.gov (United States)

    Criado, José R; Gilder, David A; Kalafut, Mary A; Ehlers, Cindy L

    2016-04-01

    Hypertension and obesity are serious health problems that have been associated with an increased risk of cardiovascular disease (CVD). We recently showed a relationship between hypertension, obesity and cardiovagal control in a sample of Native and Mexican Americans at high risk of alcohol use disorders (AUD). While studies have shown that Native and Mexican Americans exhibit high rates of AUD, the consequences of AUD on CVD risk factors and their relationship with cardiovascular autonomic control is not well understood in these ethnic groups. This study investigated whether an association could be demonstrated between cardiovascular autonomic control and several CVD risk factors in Native and Mexican American men and women (n = 228) who are literate in English and are residing legally in San Diego County. Participants with lifetime history of AUD showed higher rates of systolic and diastolic hypertension and obesity than participants without lifetime AUD. Lifetime AUD was significantly associated with reduced HR response to deep breathing (HRDB) measure of cardiovagal control, higher current drinking quantity, and obesity. Reduced HRDB was also associated with increased systolic pre-hypertension or hypertension (pre-/hypertension) and with higher diastolic blood pressure in a linear regression model that included several diagnostic and demographic variables. HRDB and time- and frequency-domain measures of cardiovagal control were significantly reduced in participants with diastolic pre-/hypertension. These data suggest that lower cardiovagal control may play a role in the prevalence of systolic and diastolic pre-/hypertension in a community sample with a history of alcohol and substance use disorders.

  11. Neural Prescribed Performance Control for Uncertain Marine Surface Vessels without Accurate Initial Errors

    Directory of Open Access Journals (Sweden)

    Wenjie Si

    2017-01-01

    Full Text Available This paper deals with the problems concerned with the trajectory tracking control with prescribed performance for marine surface vessels without velocity measurements in uncertain dynamical environments, in the presence of parametric uncertainties, unknown disturbances, and unknown dead-zone. First, only the ship position and heading measurements are available and a high-gain observer is used to estimate the unmeasurable velocities. Second, by utilizing the prescribed performance control, the prescribed tracking control performance can be ensured, while the requirement for the initial error is removed via the preprocessing. At last, based on neural network approximation in combination with backstepping and Lyapunov synthesis, a robust adaptive neural control scheme is developed to handle the uncertainties and input dead-zone characteristics. Under the designed adaptive controller for marine surface vessels, all the signals in the closed-loop system are semiglobally uniformly ultimately bounded (SGUUB, and the prescribed transient and steady tracking control performance is guaranteed. Simulation studies are performed to demonstrate the effectiveness of the proposed method.

  12. Integrated chassis control for vehicle rollover prevention with neural network time-to-rollover warning metrics

    Directory of Open Access Journals (Sweden)

    Bing Zhu

    2016-02-01

    Full Text Available The rollover of road vehicles is one of the most serious problems related to transportation safety. In this article, a novel rollover prevention control system composed of rollover warning and integrated chassis control algorithm is proposed. First, a conventional time-to-rollover warning algorithm was presented based on the 3-degree of freedom vehicle model. In order to improve the precision of vehicle rollover prediction, a back-propagation neural network was adopted to regulate time to rollover online by considering multi-state parameters of the vehicle. Second, a rollover prevention algorithm based on integrated chassis control was investigated, where the active front steering and the active yaw moment control were coordinated by model predictive control methodology. Finally, the algorithms were evaluated under several typical maneuvers utilizing MATLAB/Simulink and Carsim co-simulation. The results show that the proposed neural network time-to-rollover metrics can be a good measure of the danger of rollover, and the roll stability of the simulated vehicle is improved significantly with reduced side slip angle and yaw rate by the proposed integrated chassis control rollover prevention system.

  13. Cell biology in neuroscience: Architects in neural circuit design: glia control neuron numbers and connectivity.

    Science.gov (United States)

    Corty, Megan M; Freeman, Marc R

    2013-11-11

    Glia serve many important functions in the mature nervous system. In addition, these diverse cells have emerged as essential participants in nearly all aspects of neural development. Improved techniques to study neurons in the absence of glia, and to visualize and manipulate glia in vivo, have greatly expanded our knowledge of glial biology and neuron-glia interactions during development. Exciting studies in the last decade have begun to identify the cellular and molecular mechanisms by which glia exert control over neuronal circuit formation. Recent findings illustrate the importance of glial cells in shaping the nervous system by controlling the number and connectivity of neurons.

  14. Sliding Mode Control for NSVs with Input Constraint Using Neural Network and Disturbance Observer

    Directory of Open Access Journals (Sweden)

    Yan-long Zhou

    2013-01-01

    Full Text Available The sliding mode control (SMC scheme is proposed for near space vehicles (NSVs with strong nonlinearity, high coupling, parameter uncertainty, and unknown time-varying disturbance based on radial basis function neural networks (RBFNNs and the nonlinear disturbance observer (NDO. Considering saturation characteristic of rudders, RBFNNs are constructed as a compensator to overcome the saturation nonlinearity. The stability of the closed-loop system is proved, and the tracking error as well as the disturbance observer error can converge to the origin through the Lyapunov analysis. Simulation results are presented to demonstrate the effectiveness of the proposed flight control scheme.

  15. Discrete-time adaptive backstepping nonlinear control via high-order neural networks.

    Science.gov (United States)

    Alanis, Alma Y; Sanchez, Edgar N; Loukianov, Alexander G

    2007-07-01

    This paper deals with adaptive tracking for discrete-time multiple-input-multiple-output (MIMO) nonlinear systems in presence of bounded disturbances. In this paper, a high-order neural network (HONN) structure is used to approximate a control law designed by the backstepping technique, applied to a block strict feedback form (BSFF). This paper also includes the respective stability analysis, on the basis of the Lyapunov approach, for the whole controlled system, including the extended Kalman filter (EKF)-based NN learning algorithm. Applicability of the scheme is illustrated via simulation for a discrete-time nonlinear model of an electric induction motor.

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

    Science.gov (United States)

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

    2011-04-01

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

  17. Polymorphisms of adrenergic cardiovascular control genes are associated with adolescent chronic fatigue syndrome.

    Science.gov (United States)

    Sommerfeldt, Line; Portilla, Helene; Jacobsen, Line; Gjerstad, Johannes; Wyller, Vegard Bruun

    2011-02-01

    To explore the frequency of polymorphisms in adrenergic cardiovascular control genes in adolescent with chronic fatigue syndrome (CFS) and the relation of such polymorphisms to cardiovascular variables. DNA from 53 patients with CFS, 12-18 years old, was analysed for five single nucleotide polymorphisms (SNPs) in the genes catechol-O-methyltransferase (COMT), the β₂ -adrenergic receptor (two SNPs), the β₁ -adrenergic receptor and the α₂(a) -adrenergic receptor. Frequencies were compared to a reference population constructed from the National Center for Biotechnology Information (NCBI) database, and associations between frequencies and autonomic cardiovascular responses during a 20° head-up tilt-test were explored. For the COMT SNP Rs4680, patients with CFS had a higher frequency of the AA genotype and a lower frequency of the G containing genotypes (AG and GG), when compared to the reference sample (p = 0.046). Also, the AA genotype was associated with a smaller increase in LF/HF ratio (low-frequency:high-frequency heart rate variability ratio, an index of cardiac sympathovagal balance) during head-up tilt when compared to the AG/GG genotypes. For the β₂ -adrenergic receptor SNP Rs1042714, patients with CFS had a lower frequency of the GG genotype and a higher frequency of the genotypes containing C (CG and CC) (p = 0.044). CFS might be related to polymorphisms of COMT and the β₂ -adrenergic receptor. More details of the molecular mechanisms remain to be investigated. © 2010 The Author(s)/Acta Paediatrica © 2010 Foundation Acta Paediatrica.

  18. Exercise improves cardiovascular control in a model of dislipidemia and menopause.

    Science.gov (United States)

    Heeren, Marcelo Velloso; De Sousa, Leandro Eziquiel; Mostarda, Cristiano; Moreira, Edson; Machert, Henrique; Rigatto, Katya Vianna; Wichi, Rogério Brandão; Irigoyen, M C; De Angelis, Kátia

    2009-02-20

    The present study investigated the effects of exercise training on arterial pressure, baroreflex sensitivity, cardiovascular autonomic control and metabolic parameters on female LDL-receptor knockout ovariectomized mice. Mice were divided into two groups: sedentary and trained. Trained group was submitted to an exercise training protocol. Blood cholesterol was measured. Arterial pressure (AP) signals were directly recorded in conscious mice. Baroreflex sensitivity was evaluated by tachycardic and bradycardic responses to AP changes. Cardiovascular autonomic modulation was measured in frequency (FFT) and time domains. Maximal exercise capacity was increased in trained as compared to sedentary group. Blood cholesterol was diminished in trained mice (191+/-8mg/dL) when compared to sedentary mice (250+/-9mg/dL, p<0.05). Mean AP and HR were reduced in trained group (101+/-3mmHg and 535+/-14bpm, p<0.05) when compared with sedentary group (125+/-3mmHg and 600+/-12bpm). Exercise training induced improvement in bradycardic reflex response in trained animals (-4.24+/-0.62bpm/mmHg) in relation to sedentary animals (-1.49+/-0.15bpm/mmHg, p<0.01); tachycardic reflex responses were similar between studied groups. Exercise training increased the variance (34+/-8 vs. 6.6+/-1.5ms(2) in sedentary, p<0.005) and the high-frequency band (HF) of the pulse interval (IP) (53+/-7% vs. 26+/-6% in sedentary, p<0.01). It is tempting to speculate that results of this experimental study might represent a rationale for this non-pharmacological intervention in the management of cardiovascular risk factors in dyslipidemic post-menopause women.

  19. Impact of Urate Level on Cardiovascular Risk in Allopurinol Treated Patients. A Nested Case-Control Study

    DEFF Research Database (Denmark)

    Søltoft Larsen, Kasper; Pottegård, Anton; Lindegaard, Hanne M

    2016-01-01

    : To investigate the effect of achieving target plasma urate with allopurinol on cardiovascular outcomes in a case-control study nested within long-term users of allopurinol. METHODS: We identified long-term users of allopurinol in Funen County, Denmark. Among these, we identified all cases of cardiovascular...... events and sampled 4 controls to each case from the same population. The cases and controls were compared with respect to whether they reached a urate target below 0.36 mmol/l on allopurinol. The derived odds ratios were controlled for potential confounders available from data on prescriptions......, allopurinol dose or the achieved urate level. Overall, the doses of allopurinol used in this study were low (mean ≈ 140 mg/day). CONCLUSION: We were unable to demonstrate a link between achieved urate level in patients treated with allopurinol and risk of cardiovascular events. Possible explanations include...

  20. Cardiovascular Risk Factors in Subclinical Hypothyroidism: A Case Control Study in Nepalese Population

    Directory of Open Access Journals (Sweden)

    Rajendra KC

    2015-01-01

    Full Text Available Objectives. To assess cardiovascular risk factors in Nepalese population with subclinical hypothyroidism as compared to age and sex matched controls. Materials and Methods. A case control study was conducted among 200 subjects (100 subclinical hypothyroid and 100 euthyroid at B.P. Koirala Institute of Health Sciences, Dharan, Nepal. Demographic and anthropometric variables including systolic and diastolic blood pressure (BP were taken. Blood samples were assayed for serum free triiodothyronine (fT3, free thyroxine (fT4, thyroid stimulating hormone (TSH, total cholesterol (TC, high density lipoprotein cholesterol (HDL-C, low density lipoprotein cholesterol (LDL-C, and high sensitivity C reactive protein (hs-CRP. Results. Subclinical hypothyroid patients had significantly higher diastolic BP, total cholesterol, LDL cholesterol, and hs-CRP than controls. The odds ratio of having hypercholesterolemia (>200 mg/dL, low HDL cholesterol (100 mg/dL, high hs-CRP (>1 mg/L, and high diastolic BP (>80 mmHg and being overweight (BMI ≥ 23 Kg/m2 in subclinical hypothyroidism was 2.29 (95% CI; 1.2–4.38, p=0.011, 1.73 (95% CI; 0.82–3.62, p=0.141, 3.04 (95% CI; 1.66–5.56, p<0.001, 2.02 (95% CI; 1.12–3.64, p=0.018, 3.35 (95% CI; 1.72–6.55, p<0.001, and 0.9 (95% CI; 0.48–1.67, p=0.753, respectively, as compared to controls. Conclusion. Subclinical hypothyroid patients are associated with higher risk for cardiovascular disease than euthyroid subjects.

  1. Variable Torque Control of Offshore Wind Turbine on Spar Floating Platform Using Advanced RBF Neural Network

    Directory of Open Access Journals (Sweden)

    Lei Wang

    2014-01-01

    Full Text Available Offshore floating wind turbine (OFWT has been a challenging research spot because of the high-quality wind power and complex load environment. This paper focuses on the research of variable torque control of offshore wind turbine on Spar floating platform. The control objective in below-rated wind speed region is to optimize the output power by tracking the optimal tip-speed ratio and ideal power curve. Aiming at the external disturbances and nonlinear uncertain dynamic systems of OFWT because of the proximity to load centers and strong wave coupling, this paper proposes an advanced radial basis function (RBF neural network approach for torque control of OFWT system at speeds lower than rated wind speed. The robust RBF neural network weight adaptive rules are acquired based on the Lyapunov stability analysis. The proposed control approach is tested and compared with the NREL baseline controller using the “NREL offshore 5 MW wind turbine” model mounted on a Spar floating platform run on FAST and Matlab/Simulink, operating in the below-rated wind speed condition. The simulation results show a better performance in tracking the optimal output power curve, therefore, completing the maximum wind energy utilization.

  2. BP neural network tuned PID controller for position tracking of a pneumatic artificial muscle.

    Science.gov (United States)

    Fan, Jizhuang; Zhong, Jun; Zhao, Jie; Zhu, Yanhe

    2015-01-01

    Although Pneumatic Artificial Muscle (PAM) has a promising future in rehabilitation robots, it's difficult to realize accurate position control due to its highly nonlinear properties. This paper deals with position control of PAM. To describe the hysteresis inside PAM, a polynomial based phenomenological function is developed. Based on the phenomenological model for PAM and analysis of pressure dynamics within PAM, an adaptive cascade controller is proposed. Both outer loop and inner loop employ BP Neural Network tuned PID algorithm. The outer loop is to handle high nonlinearities and unmodeled dynamics of PAM, while the inner loop is responsible for nonlinearities caused by pressure dynamics. Experimental results show high tracking accuracy as compared with a convention PID controller. The proposed controller is effective in improving performance of PAM and will be implemented in a rehabilitation robot.

  3. Neural Network Control for the Linear Motion of a Spherical Mobile Robot

    Directory of Open Access Journals (Sweden)

    Yao Cai

    2011-09-01

    Full Text Available This paper discussed the stabilization and position tracking control of the linear motion of an underactuated spherical robot. By considering the actuator dynamics, a complete dynamic model of the robot is deduced, which is a complex third order, two variables nonlinear differential system and those two variables have strong coupling due to the mechanical structure of the robot. Different from traditional treatments, no linearization is applied to this system but a single‐input multiple‐output PID (SIMO_PID controller is designed by adopting a six‐input single‐ output CMAC_GBF (Cerebellar Model Articulation Controller with General Basis Function neural network to compensate the actuator nonlinearity and the credit assignment (CA learning method to obtain faster convergence of CMAC_GBF. The proposed controller is generalizable to other single‐input multiple‐output system with good real‐time capability. Simulations in Matlab are used to validate the control effects.

  4. Neural-network-observer-based optimal control for unknown nonlinear systems using adaptive dynamic programming

    Science.gov (United States)

    Liu, Derong; Huang, Yuzhu; Wang, Ding; Wei, Qinglai

    2013-09-01

    In this paper, an observer-based optimal control scheme is developed for unknown nonlinear systems using adaptive dynamic programming (ADP) algorithm. First, a neural-network (NN) observer is designed to estimate system states. Then, based on the observed states, a neuro-controller is constructed via ADP method to obtain the optimal control. In this design, two NN structures are used: a three-layer NN is used to construct the observer which can be applied to systems with higher degrees of nonlinearity and without a priori knowledge of system dynamics, and a critic NN is employed to approximate the value function. The optimal control law is computed using the critic NN and the observer NN. Uniform ultimate boundedness of the closed-loop system is guaranteed. The actor, critic, and observer structures are all implemented in real-time, continuously and simultaneously. Finally, simulation results are presented to demonstrate the effectiveness of the proposed control scheme.

  5. UKF-based closed loop iterative learning control of epileptiform wave in a neural mass model.

    Science.gov (United States)

    Shan, Bonan; Wang, Jiang; Deng, Bin; Wei, Xile; Yu, Haitao; Li, Huiyan

    2015-02-01

    A novel closed loop control framework is proposed to inhibit epileptiform wave in a neural mass model by external electric field, where the unscented Kalman filter method is used to reconstruct dynamics and estimate unmeasurable parameters of the model. Specifically speaking, the iterative learning control algorithm is introduced into the framework to optimize the control signal. In the proposed method, the control effect can be significantly improved based on the observation of the past attempts. Accordingly, the proposed method can effectively suppress the epileptiform wave as well as showing robustness to noises and uncertainties. Lastly, the simulation is carried out to illustrate the feasibility of the proposed method. Besides, this work shows potential value to design model-based feedback controllers for epilepsy treatment.

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

    Energy Technology Data Exchange (ETDEWEB)

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

    1995-12-31

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

  7. Learning from adaptive neural network output feedback control of a unicycle-type mobile robot.

    Science.gov (United States)

    Zeng, Wei; Wang, Qinghui; Liu, Fenglin; Wang, Ying

    2016-03-01

    This paper studies learning from adaptive neural network (NN) output feedback control of nonholonomic unicycle-type mobile robots. The major difficulties are caused by the unknown robot system dynamics and the unmeasurable states. To overcome these difficulties, a new adaptive control scheme is proposed including designing a new adaptive NN output feedback controller and two high-gain observers. It is shown that the stability of the closed-loop robot system and the convergence of tracking errors are guaranteed. The unknown robot system dynamics can be approximated by radial basis function NNs. When repeating same or similar control tasks, the learned knowledge can be recalled and reused to achieve guaranteed stability and better control performance, thereby avoiding the tremendous repeated training process of NNs. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

  8. Sensorless Speed Control of Permanent Magnet Synchronous Motors by Neural Network Algorithm

    Directory of Open Access Journals (Sweden)

    Ming-Shyan Wang

    2014-01-01

    Full Text Available The sliding mode control has the merits with respect to the variation of the disturbance and robustness. In this paper, the sensorless sliding-mode observer with least mean squared error approach for permanent magnet synchronous motor (PMSM to detect the rotor position by counter electromotive force and then compute motor speed is designed and implemented. In addition, the neural network control is also used to compensate the PI gain tuning to increase the speed accuracy without regarding the errors of the current measurement and motor noise. In this paper, a digital signal processor TMS320F2812 utilizes its high-speed ADC module to get current feedback information and thus to estimate the rotor position and takes advantage of the built-in modules to achieve SVPWM current control so that the senseless speed control will be accomplished. The correctness and effectiveness of the proposed control system will be verified from the experimental results.

  9. Crew exploration vehicle (CEV) attitude control using a neural-immunology/memory network

    Science.gov (United States)

    Weng, Liguo; Xia, Min; Wang, Wei; Liu, Qingshan

    2015-01-01

    This paper addresses the problem of the crew exploration vehicle (CEV) attitude control. CEVs are NASA's next-generation human spaceflight vehicles, and they use reaction control system (RCS) jet engines for attitude adjustment, which calls for control algorithms for firing the small propulsion engines mounted on vehicles. In this work, the resultant CEV dynamics combines both actuation and attitude dynamics. Therefore, it is highly nonlinear and even coupled with significant uncertainties. To cope with this situation, a neural-immunology/memory network is proposed. It is inspired by the human memory and immune systems. The control network does not rely on precise system dynamics information. Furthermore, the overall control scheme has a simple structure and demands much less computation as compared with most existing methods, making it attractive for real-time implementation. The effectiveness of this approach is also verified via simulation.

  10. A Neural Network Controller for Variable-Speed Variable-Pitch Wind Energy Conversion Systems Using Generalized Minimum Entropy Criterion

    Directory of Open Access Journals (Sweden)

    Mifeng Ren

    2014-01-01

    Full Text Available This paper considers the neural network controller design problem for variable pitch wind energy conversion systems (WECS with non-Gaussian wind speed disturbances in the stochastic distribution control framework. The approach here is used to directly model the unknown control law based on a fixed neural network (the number of layers and nodes in a neural network is fixed without the need to construct a separate model for the WECS. In order to characterize the randomness of the WECS, a generalized minimum entropy criterion is established to train connection weights of the neural network. For the train purpose, both kernel density estimation method and sliding window technique are adopted to estimate the PDF of tracking error and entropies. Due to the unknown process dynamics, the gradient of the objective function in a gradient-descent-type algorithm is estimated using an incremental perturbation method. The proposed approach is illustrated on a simulated WECS with non-Gaussian wind speed.

  11. Control of metabolic and cardiovascular function by the leptin-brain melanocortin pathway.

    Science.gov (United States)

    do Carmo, Jussara M; da Silva, Alexandre A; Dubinion, John; Sessums, Price O; Ebaady, Sabira H; Wang, Zhen; Hall, John E

    2013-08-01

    Obesity is recognized as a major worldwide health problem. Excess weight gain is the most common cause of elevated blood pressure (BP) and markedly increases the risk of metabolic, cardiovascular and renal diseases. Although the mechanisms linking obesity with hypertension have not been fully elucidated, increased sympathetic nervous system (SNS) activity contributes to elevated BP in obese subjects. Recent evidence indicates that leptin and the central nervous system (CNS) melanocortin system, including melanocortin 4 receptors (MC4R), play a key role in linking obesity with increased SNS activity and hypertension. Leptin, a peptide-hormone produced by adipose tissue, crosses the blood-brain barrier and activates brain centers that control multiple metabolic functions as well as SNS activity and BP via the CNS melanocortin system. The crosstalk between peripheral signals (e.g., leptin) and activation of CNS pathways (e.g., MC4R) that regulate energy balance, SNS activity and BP represents an important target for treating obesity and its metabolic and cardiovascular consequences. © 2013 International Union of Biochemistry and Molecular Biology.

  12. A Unified Point Process Probabilistic Framework to Assess Heartbeat Dynamics and Autonomic Cardiovascular Control

    Directory of Open Access Journals (Sweden)

    Zhe eChen

    2012-02-01

    Full Text Available In recent years, time-varying inhomogeneous point process models have been introduced for assessment of instantaneous heartbeat dynamics as well as specific cardiovascular control mechanisms and hemodynamics. Assessment of the model's statistics is established through the Wiener-Volterra theory and a multivariate autoregressive (AR structure. A variety of instantaneous cardiovascular metrics, such as heart rate (HR, heart rate variability (HRV, respiratory sinus arrhythmia (RSA, and baroreceptor-cardiac reflex (baroreflex sensitivity (BRS, are derived within a parametric framework and instantaneously updated with adaptive and local maximum likelihood estimation algorithms. Inclusion of second order nonlinearities, with subsequent bispectral quantification in the frequency domain, further allows for definition of instantaneous metrics of nonlinearity. We here organize a comprehensive review of the devised methods as applied to experimental recordings from healthy subjects during propofol anesthesia. Collective results reveal interesting dynamic trends across the different pharmacological interventions operated within each anesthesia session, confirming the ability of the algorithm to track important changes in cardiorespiratory elicited interactions, and pointing at our mathematical approach as a promising monitoring tool for an accurate, noninvasive assessment in clinical practice.

  13. A case control study of cardiovascular health in chemical war disabled Iranian victims.

    Science.gov (United States)

    Rohani, Atoosheh; Akbari, Vahid; Moghadam, Fatemeh Tabesh

    2010-07-01

    Sulfur mustard (SM) is an alkylating chemical warfare agent that was widely used during Iran-Iraq war between 1983 and 1988. SM exposure leads to various late complications. The aim of this study was to determine the late cardiovascular effects of SM in war-disabled Iranian victims. This was a retrospective cohort case control study on 50 patients with symptoms of SM exposure and 50 cases who had been in Iran-Iraq war, without chemical injury. We performed exercise stress test and echocardiography for all of patients. The study group comprised 100 males of mean age 45.6 ± 6.2 years. In chemical war injury group, two patients (4%) had positive exercise stress test. On coronary angiography, they were found to have coronary artery disease. One patient had severe mitral regurgitation and normal coronary angiography; he was referred for mitral valve replacement. Left ventricular (LV) diastolic abnormality was detected in 23% of these subjects. In another group, 5% had LV diastolic abnormality (P = 0.02) and all of them had normal stress test. Cardiovascular abnormalities are another late complication in chemical war disabled Iranian victims. Diastolic dysfunction was the most common abnormality in both groups of patients.

  14. Control of Metabolic and Cardiovascular Function by the Leptin–Brain Melanocortin Pathway

    Science.gov (United States)

    do Carmo, Jussara M.; da Silva, Alexandre A.; Dubinion, John; Sessums, Price O.; Ebaady, Sabira H.; Wang, Zhen; Hall, John E.

    2014-01-01

    Obesity is recognized as a major worldwide health problem. Excess weight gain is the most common cause of elevated blood pressure (BP) and markedly increases the risk of metabolic, cardiovascular and renal diseases. Although the mechanisms linking obesity with hypertension have not been fully elucidated, increased sympathetic nervous system (SNS) activity contributes to elevated BP in obese subjects. Recent evidence indicates that leptin and the central nervous system (CNS) melanocortin system, including melanocortin 4 receptors (MC4R), play a key role in linking obesity with increased SNS activity and hypertension. Leptin, a peptide-hormone produced by adipose tissue, crosses the blood–brain barrier and activates brain centers that control multiple metabolic functions as well as SNS activity and BP via the CNS melanocortin system. The crosstalk between peripheral signals (e.g., leptin) and activation of CNS pathways (e.g., MC4R) that regulate energy balance, SNS activity and BP represents an important target for treating obesity and its metabolic and cardiovascular consequences. PMID:23847053

  15. Adaptive neural control of MIMO nonlinear systems with a block-triangular pure-feedback control structure.

    Science.gov (United States)

    Chen, Zhenfeng; Ge, Shuzhi Sam; Zhang, Yun; Li, Yanan

    2014-11-01

    This paper presents adaptive neural tracking control for a class of uncertain multiinput-multioutput (MIMO) nonlinear systems in block-triangular form. All subsystems within these MIMO nonlinear systems are of completely nonaffine pure-feedback form and allowed to have different orders. To deal with the nonaffine appearance of the control variables, the mean value theorem is employed to transform the systems into a block-triangular strict-feedback form with control coefficients being couplings among various inputs and outputs. A systematic procedure is proposed for the design of a new singularity-free adaptive neural tracking control strategy. Such a design procedure can remove the couplings among subsystems and hence avoids the possible circular control construction problem. As a consequence, all the signals in the closed-loop system are guaranteed to be semiglobally uniformly ultimately bounded. Moreover, the outputs of the systems are ensured to converge to a small neighborhood of the desired trajectories. Simulation studies verify the theoretical findings revealed in this paper.

  16. Neural-Network-Based Fuzzy Logic Navigation Control for Intelligent Vehicles

    Directory of Open Access Journals (Sweden)

    Ahcene Farah

    2002-06-01

    Full Text Available This paper proposes a Neural-Network-Based Fuzzy logic system for navigation control of intelligent vehicles. First, the use of Neural Networks and Fuzzy Logic to provide intelligent vehicles  with more autonomy and intelligence is discussed. Second, the system  for the obstacle avoidance behavior is developed. Fuzzy Logic improves Neural Networks (NN obstacle avoidance approach by handling imprecision and rule-based approximate reasoning. This system must make the vehicle able, after supervised learning, to achieve two tasks: 1- to make one’s way towards its target by a NN, and 2- to avoid static or dynamic obstacles by a Fuzzy NN capturing the behavior of a human expert. Afterwards, two association phases between each task and the appropriate actions are carried out by Trial and Error learning and their coordination allows to decide the appropriate action. Finally, the simulation results display the generalization and adaptation abilities of the system by testing it in new unexplored environments.

  17. Zebrafish Rfx4 controls dorsal and ventral midline formation in the neural tube.

    Science.gov (United States)

    Sedykh, Irina; Keller, Abigail N; Yoon, Baul; Roberson, Laura; Moskvin, Oleg V; Grinblat, Yevgenya

    2017-12-15

    Rfx winged-helix transcription factors, best known as key regulators of core ciliogenesis, also play ciliogenesis-independent roles during neural development. Mammalian Rfx4 controls neural tube morphogenesis via both mechanisms. We set out to identify conserved aspects of rfx4 gene function during vertebrate development and to establish a new genetic model in which to analyze these mechanisms further. To this end, we have generated frame-shift alleles in the zebrafish rfx4 locus using CRISPR/Cas9 mutagenesis. Using RNAseq-based transcriptome analysis, in situ hybridization and immunostaining we identified a requirement for zebrafish rfx4 in the forming midlines of the caudal neural tube. These functions are mediated, least in part, through transcriptional regulation of several zic genes in the dorsal hindbrain and of foxa2 in the ventral hindbrain and spinal cord (floor plate). The midline patterning functions of rfx4 are conserved, since rfx4 regulates transcription of foxa2 and zic2 in zebrafish and in mouse. In contrast, zebrafish rfx4 function is dispensable for forebrain morphogenesis, while mouse rfx4 is required for normal formation of forebrain ventricles in a ciliogenesis-dependent manner. Collectively, this report identifies conserved aspects of rfx4 function and establishes a robust new genetic model for in-depth dissection of these mechanisms. This article is protected by copyright. All rights reserved. © 2017 Wiley Periodicals, Inc.

  18. Impact on cardiovascular disease events of the implementation of Argentina's national tobacco control law.

    Science.gov (United States)

    Konfino, Jonatan; Ferrante, Daniel; Mejia, Raul; Coxson, Pamela; Moran, Andrew; Goldman, Lee; Pérez-Stable, Eliseo J

    2014-03-01

    Argentina's congress passed a tobacco control law that would enforce 100% smoke-free environments for the entire country, strong and pictorial health warnings on tobacco products and a comprehensive advertising ban. However, the Executive Branch continues to review the law and it has not been fully implemented. Our objective was to project the potential impact of full implementation of this tobacco control legislation on cardiovascular disease. The Coronary Heart Disease (CHD) Policy Model was used to project future cardiovascular events. Data sources for the model included vital statistics, morbidity and mortality data, and tobacco use estimates from the National Risk Factor Survey. Estimated effectiveness of interventions was based on a literature review. Results were expressed as life-years, myocardial infarctions and strokes saved in an 8-year-period between 2012 and 2020. In addition we projected the incremental effectiveness on the same outcomes of a tobacco price increase not included in the law. In the period 2012-2020, 7500 CHD deaths, 16 900 myocardial infarctions and 4300 strokes could be avoided with the full implementation and enforcement of this law. Annual per cent reduction would be 3% for CHD deaths, 3% for myocardial infarctions and 1% for stroke. If a tobacco price increase is implemented the projected avoided CHD deaths, myocardial infarctions and strokes would be 15 500, 34 600 and 11 900, respectively. Implementation of the tobacco control law would produce significant public health benefits in Argentina. Strong advocacy is needed at national and international levels to get this law implemented throughout Argentina.

  19. Changes in ambient temperature elicit divergent control of metabolic and cardiovascular actions by leptin.

    Science.gov (United States)

    do Carmo, Jussara M; da Silva, Alexandre A; Romero, Damian G; Hall, John E

    2017-06-01

    Interactions of hypothalamic signaling pathways that control body temperature (BT), blood pressure (BP), and energy balance are poorly understood. We investigated whether the chronic BP and metabolic actions of leptin are differentially modulated by changes in ambient temperature (TA ). Mean arterial pressure (MAP), heart rate (HR), BT, motor activity (MA), and oxygen consumption (Vo2) were measured 24 h/d at normal laboratory TA (23°C), at thermoneutral zone (TNZ, 30°C) for mice or during cold exposure (15°C) in male wild-type mice. After control measurements, leptin (4 μg/kg/min) or saline vehicle was infused for 7 d. At TNZ, leptin reduced food intake (-11.0 ± 0.5 g cumulative deficit) and body weight by 6% but caused no changes in MAP or HR. At 15°C, leptin infusion did not alter food intake but increased MAP and HR (8 ± 1 mmHg and 33 ± 7 bpm), while Vo2 increased by ∼10%. Leptin reduced plasma glucose and insulin levels at 15°C but not at 30°C. These results demonstrate that the chronic anorexic effects of leptin are enhanced at TNZ, while its effects on insulin and glucose levels are attenuated and its effects on BP and HR are abolished. Conversely, cold TA caused resistance to leptin's anorexic effects but amplified its effects to raise BP and reduce insulin and glucose levels. Thus, the brain circuits by which leptin regulates food intake and cardiovascular function are differentially influenced by changes in TA -Do Carmo, J. M., da Silva, A. A., Romero, D. G., Hall, J. E. Changes in ambient temperature elicit divergent control of metabolic and cardiovascular actions by leptin. © FASEB.

  20. Efficacy and safety of alirocumab in high cardiovascular risk patients with inadequately controlled hypercholesterolaemia on maximally tolerated doses of statins

    DEFF Research Database (Denmark)

    Cannon, Christopher P; Cariou, Bertrand; Blom, Dirk

    2015-01-01

    AIMS: To compare the efficacy [low-density lipoprotein cholesterol (LDL-C) lowering] and safety of alirocumab, a fully human monoclonal antibody to proprotein convertase subtilisin/kexin 9, compared with ezetimibe, as add-on therapy to maximally tolerated statin therapy in high cardiovascular risk...... patients with inadequately controlled hypercholesterolaemia. METHODS AND RESULTS: COMBO II is a double-blind, double-dummy, active-controlled, parallel-group, 104-week study of alirocumab vs. ezetimibe. Patients (n = 720) with high cardiovascular risk and elevated LDL-C despite maximal doses of statins.......04 mmol/L with alirocumab and 2.1 ± 0.05 mmol/L with ezetimibe, and were maintained to Week 52. Alirocumab was generally well tolerated, with no evidence of an excess of treatment-emergent adverse events. CONCLUSION: In patients at high cardiovascular risk with inadequately controlled LDL-C, alirocumab...

  1. Design a PID Controller for Suspension System by Back Propagation Neural Network

    Directory of Open Access Journals (Sweden)

    M. Heidari

    2013-01-01

    Full Text Available This paper presents a neural network for designing of a PID controller for suspension system. The suspension system, designed as a quarter model, is used to simplify the problem to one-dimensional spring-damper system. In this paper, back propagation neural network (BPN has been used for determining the gain parameters of a PID controller for suspension system of automotive. The BPN method is found to be the most accurate and quick. The best results were obtained by the BPN by Levenberg-Marquardt algorithm training with 10 neurons in the one hidden layer. Training was continued until the mean squared error is less than . Desired error value was achieved in the BPN, and the BPN was tested with both data used and not used for training. By training of this network, it is possible to estimate the gain parameters of PID controller at any condition. The inputs of network are automotive velocity, overshoot percentage, settling time, and steady state error of suspension system response. Also outputs of the net are the gain parameters of PID controller. Resultant low relative error value of the ANN model indicates the usability of the BPN in this area.

  2. No Evidence That Gratitude Enhances Neural Performance Monitoring or Conflict-Driven Control.

    Science.gov (United States)

    Saunders, Blair; He, Frank F H; Inzlicht, Michael

    2015-01-01

    It has recently been suggested that gratitude can benefit self-regulation by reducing impulsivity during economic decision making. We tested if comparable benefits of gratitude are observed for neural performance monitoring and conflict-driven self-control. In a pre-post design, 61 participants were randomly assigned to either a gratitude or happiness condition, and then performed a pre-induction flanker task. Subsequently, participants recalled an autobiographical event where they had felt grateful or happy, followed by a post-induction flanker task. Despite closely following existing protocols, participants in the gratitude condition did not report elevated gratefulness compared to the happy group. In regard to self-control, we found no association between gratitude--operationalized by experimental condition or as a continuous predictor--and any control metric, including flanker interference, post-error adjustments, or neural monitoring (the error-related negativity, ERN). Thus, while gratitude might increase economic patience, such benefits may not generalize to conflict-driven control processes.

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

    Energy Technology Data Exchange (ETDEWEB)

    Ondrej Linda; Milos Manic; Timothy R. McJunkin

    2011-08-01

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

  4. On the Control of Social Approach-Avoidance Behavior: Neural and Endocrine Mechanisms.

    Science.gov (United States)

    Kaldewaij, Reinoud; Koch, Saskia B J; Volman, Inge; Toni, Ivan; Roelofs, Karin

    The ability to control our automatic action tendencies is crucial for adequate social interactions. Emotional events trigger automatic approach and avoidance tendencies. Although these actions may be generally adaptive, the capacity to override these emotional reactions may be key to flexible behavior during social interaction. The present chapter provides a review of the neuroendocrine mechanisms underlying this ability and their relation to social psychopathologies. Aberrant social behavior, such as observed in social anxiety or psychopathy, is marked by abnormalities in approach-avoidance tendencies and the ability to control them. Key neural regions involved in the regulation of approach-avoidance behavior are the amygdala, widely implicated in automatic emotional processing, and the anterior prefrontal cortex, which exerts control over the amygdala. Hormones, especially testosterone and cortisol, have been shown to affect approach-avoidance behavior and the associated neural mechanisms. The present chapter also discusses ways to directly influence social approach and avoidance behavior and will end with a research agenda to further advance this important research field. Control over approach-avoidance tendencies may serve as an exemplar of emotional action regulation and might have a great value in understanding the underlying mechanisms of the development of affective disorders.

  5. Feasibility of EMG-based neural network controller for an upper extremity neuroprosthesis.

    Science.gov (United States)

    Hincapie, Juan Gabriel; Kirsch, Robert F

    2009-02-01

    The overarching goal of this project is to provide shoulder and elbow function to individuals with C5/C6 spinal cord injury (SCI) using functional electrical stimulation (FES), increasing the functional outcomes currently provided by a hand neuroprosthesis. The specific goal of this study was to design a controller based on an artificial neural network (ANN) that extracts information from the activity of muscles that remain under voluntary control sufficient to predict appropriate stimulation levels for several paralyzed muscles in the upper extremity. The ANN was trained with activation data obtained from simulations using a musculoskeletal model of the arm that was modified to reflect C5 SCI and FES capabilities. Several arm movements were recorded from able-bodied subjects and these kinematics served as the inputs to inverse dynamic simulations that predicted muscle activation patterns corresponding to the movements recorded. A system identification procedure was used to identify an optimal reduced set of voluntary input muscles from the larger set that are typically under voluntary control in C5 SCI. These voluntary activations were used as the inputs to the ANN and muscles that are typically paralyzed in C5 SCI were the outputs to be predicted. The neural network controller was able to predict the needed FES paralyzed muscle activations from "voluntary" activations with less than a 3.6% RMS prediction error.

  6. FPGA Implementation of Self-Organized Spiking Neural Network Controller for Mobile Robots

    Directory of Open Access Journals (Sweden)

    Fangzheng Xue

    2014-06-01

    Full Text Available Spiking neural network, a computational model which uses spikes to process the information, is good candidate for mobile robot controller. In this paper, we present a novel mechanism for controlling mobile robots based on self-organized spiking neural network (SOSNN and introduce a method for FPGA implementation of this SOSNN. The spiking neuron we used is Izhikevich model. A key feature of this controller is that it can simulate the process of unconditioned reflex (avoid obstacles using infrared sensor signals and conditioned reflex (make right choices in multiple T-maze by spike timing-dependent plasticity (STDP learning and dopamine-receptor modulation. Experimental results show that the proposed controller is effective and is easy to implement. The FPGA implementation method aims to build up a specific network using generic blocks designed in the MATLAB Simulink environment. The main characteristics of this original solution are: on-chip learning algorithm implementation, high reconfiguration capability, and operation under real time constraints. An extended analysis has been carried out on the hardware resources used to implement the whole SOSNN network, as well as each individual component block.

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

    Directory of Open Access Journals (Sweden)

    Yongcheng Li

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

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

    Science.gov (United States)

    Wang, Yuechao; Li, Hongyi; Zheng, Xiongfei

    2016-01-01

    We propose the architecture of a novel robot system merging biological and artificial intelligence based on a neural controller connected to an external agent. We initially built a framework that connected the dissociated neural network to a mobile robot system to implement a realistic vehicle. The mobile robot system characterized by a camera and two-wheeled robot was designed to execute the target-searching task. We modified a software architecture and developed a home-made stimulation generator to build a bi-directional connection between the biological and the artificial components via simple binomial coding/decoding schemes. In this paper, we utilized a specific hierarchical dissociated neural network for the first time as the neural controller. Based on our work, neural cultures were successfully employed to control an artificial agent resulting in high performance. Surprisingly, under the tetanus stimulus training, the robot performed better and better with the increasement of training cycle because of the short-term plasticity of neural network (a kind of reinforced learning). Comparing to the work previously reported, we adopted an effective experimental proposal (i.e. increasing the training cycle) to make sure of the occurrence of the short-term plasticity, and preliminarily demonstrated that the improvement of the robot’s performance could be caused independently by the plasticity development of dissociated neural network. This new framework may provide some possible solutions for the learning abilities of intelligent robots by the engineering application of the plasticity processing of neural networks, also for the development of theoretical inspiration for the next generation neuro-prostheses on the basis of the bi-directional exchange of information within the hierarchical neural networks. PMID:27806074

  9. Cardiovascular magnetic resonance in patients with pectus excavatum compared with normal controls

    Directory of Open Access Journals (Sweden)

    Abrazado Marlon

    2010-12-01

    Full Text Available Abstract Purpose To assess cardiothoracic structure and function in patients with pectus excavatum compared with control subjects using cardiovascular magnetic resonance imaging (CMR. Method Thirty patients with pectus excavatum deformity (23 men, 7 women, age range: 14-67 years underwent CMR using 1.5-Tesla scanner (Siemens and were compared to 25 healthy controls (18 men, 7 women, age range 18-50 years. The CMR protocol included cardiac cine images, pulmonary artery flow quantification, time resolved 3D contrast enhanced MR angiography (CEMRA and high spatial resolution CEMRA. Chest wall indices including maximum transverse diameter, pectus index (PI, and chest-flatness were measured in all subjects. Left and right ventricular ejection fractions (LVEF, RVEF, ventricular long and short dimensions (LD, SD, mid-ventricle myocardial shortening, pulmonary-systemic circulation time, and pulmonary artery flow were quantified. Results In patients with pectus excavatum, the pectus index was 9.3 ± 5.0 versus 2.8 ± 0.4 in controls (P Conclusion Depression of the sternum in pectus excavatum patients distorts RV geometry. Resting RVEF was reduced by 6% of the control value, suggesting that these geometrical changes may influence myocardial performance. Resting LV function, pulmonary circulation times and pulmonary vascular anatomy and perfusion indices were no different to controls.

  10. A Novel Fractional-Order PID Controller for Integrated Pressurized Water Reactor Based on Wavelet Kernel Neural Network Algorithm

    Directory of Open Access Journals (Sweden)

    Yu-xin Zhao

    2014-01-01

    Full Text Available This paper presents a novel wavelet kernel neural network (WKNN with wavelet kernel function. It is applicable in online learning with adaptive parameters and is applied on parameters tuning of fractional-order PID (FOPID controller, which could handle time delay problem of the complex control system. Combining the wavelet function and the kernel function, the wavelet kernel function is adopted and validated the availability for neural network. Compared to the conservative wavelet neural network, the most innovative character of the WKNN is its rapid convergence and high precision in parameters updating process. Furthermore, the integrated pressurized water reactor (IPWR system is established by RELAP5, and a novel control strategy combining WKNN and fuzzy logic rule is proposed for shortening controlling time and utilizing the experiential knowledge sufficiently. Finally, experiment results verify that the control strategy and controller proposed have the practicability and reliability in actual complicated system.

  11. RBF neural network based PI pitch controller for a class of 5-MW wind turbines using particle swarm optimization algorithm.

    Science.gov (United States)

    Poultangari, Iman; Shahnazi, Reza; Sheikhan, Mansour

    2012-09-01

    In order to control the pitch angle of blades in wind turbines, commonly the proportional and integral (PI) controller due to its simplicity and industrial usability is employed. The neural networks and evolutionary algorithms are tools that provide a suitable ground to determine the optimal PI gains. In this paper, a radial basis function (RBF) neural network based PI controller is proposed for collective pitch control (CPC) of a 5-MW wind turbine. In order to provide an optimal dataset to train the RBF neural network, particle swarm optimization (PSO) evolutionary algorithm is used. The proposed method does not need the complexities, nonlinearities and uncertainties of the system under control. The simulation results show that the proposed controller has satisfactory performance. Copyright © 2012 ISA. Published by Elsevier Ltd. All rights reserved.

  12. A Novel Neural Network Vector Control for Single-Phase Grid-Connected Converters with L, LC and LCL Filters

    Directory of Open Access Journals (Sweden)

    Xingang Fu

    2016-04-01

    Full Text Available This paper investigates a novel recurrent neural network (NN-based vector control approach for single-phase grid-connected converters (GCCs with L (inductor, LC (inductor-capacitor and LCL (inductor-capacitor-inductor filters and provides their comparison study with the conventional standard vector control method. A single neural network controller replaces two current-loop PI controllers, and the NN training approximates the optimal control for the single-phase GCC system. The Levenberg–Marquardt (LM algorithm was used to train the NN controller based on the complete system equations without any decoupling policies. The proposed NN approach can solve the decoupling problem associated with the conventional vector control methods for L, LC and LCL-filter-based single-phase GCCs. Both simulation study and hardware experiments demonstrate that the neural network vector controller shows much more improved performance than that of conventional vector controllers, including faster response speed and lower overshoot. Especially, NN vector control could achieve very good performance using low switch frequency. More importantly, the neural network vector controller is a damping free controller, which is generally required by a conventional vector controller for an LCL-filter-based single-phase grid-connected converter and, therefore, can overcome the inefficiency problem caused by damping policies.

  13. Predictive control based on neural networks: an application to a fluid catalytic cracking industrial unit

    Directory of Open Access Journals (Sweden)

    V.M.L. Santos

    2000-12-01

    Full Text Available Artificial Neural Networks (ANNs constitute a technology that has recently become the focus of great attention. The reason for this is due mainly to its capacity to treat complex and nonlinear problems. This work consists of the identification and control of a fluid cracking catalytic unit (FCCU using techniques based on multilayered ANNs. The FCC unit is a typical example of a complex and nonlinear process, possessing great interaction among the operation variables and many operational constraints to be attended. Model Predictive Control is indicated in these occasions. The FCC model adopted was validated with plant data by Moro (1992; and was used in this work to replace the real process in the generation of data for the identification of the ANNs and to test the predictive control strategy. The results of the identification and control of the process through ANNs indicate the viability of the technique.

  14. Adaptive Neural Network Control for the Trajectory Tracking of the Furuta Pendulum.

    Science.gov (United States)

    Moreno-Valenzuela, Javier; Aguilar-Avelar, Carlos; Puga-Guzman, Sergio A; Santibanez, Victor

    2016-12-01

    The purpose of this paper is to introduce a novel adaptive neural network-based control scheme for the Furuta pendulum, which is a two degree-of-freedom underactuated system. Adaptation laws for the input and output weights are also provided. The proposed controller is able to guarantee tracking of a reference signal for the arm while the pendulum remains in the upright position. The key aspect of the derivation of the controller is the definition of an output function that depends on the position and velocity errors. The internal and external dynamics are rigorously analyzed, thereby proving the uniform ultimate boundedness of the error trajectories. By using real-time experiments, the new scheme is compared with other control methodologies, therein demonstrating the improved performance of the proposed adaptive algorithm.

  15. An artificial neural network controller based on MPSO-BFGS hybrid optimization for spherical flying robot

    Science.gov (United States)

    Liu, Xiaolin; Li, Lanfei; Sun, Hanxu

    2017-12-01

    Spherical flying robot can perform various tasks in the complex and varied environment to reduce labor costs. However, it is difficult to guarantee the stability of the spherical flying robot in the case of strong coupling and time-varying disturbance. In this paper, an artificial neural network controller (ANNC) based on MPSO-BFGS hybrid optimization algorithm is proposed. The MPSO algorithm is used to optimize the initial weights of the controller to avoid the local optimal solution. The BFGS algorithm is introduced to improve the convergence ability of the network. We use Lyapunov method to analyze the stability of ANNC. The controller is simulated under the condition of nonlinear coupling disturbance. The experimental results show that the proposed controller can obtain the expected value in shoter time compared with the other considered methods.

  16. Adaptive Robust Control for Space Robot with Ucertainty base on Neural Network

    Directory of Open Access Journals (Sweden)

    Zhang Wenhui

    2013-11-01

    Full Text Available The trajectory tracking problems of a class of space robot manipulators with parameters and non-parameters uncertainty are considered. An adaptive robust control algorithm based on neural network is proposed by the paper. Neutral network is used to adaptive learn and compensate the unknown system for parameters uncertainties? the weight adaptive laws are designed by the paper? System stability base on Lyapunov theory is analysised to ensure the convergence of the algorithm. Non-parameters uncertainties are estimated and compensated by robust controller. It is proven that the designed controller can guarantee the asymptotic convergence of tracking error. The controller could guarantee good robust and the stability of closed-loop system. The simulation results show that the presented method is effective.

  17. Cardiovascular effects of aerobic exercise training in formerly preeclamptic women and healthy parous control subjects.

    Science.gov (United States)

    Scholten, Ralph R; Thijssen, Dick J H; Lotgering, Fred K; Hopman, Maria T E; Spaanderman, Marc E A

    2014-11-01

    Women who have had preeclampsia demonstrate higher prevalence of metabolic syndrome (MetS), impaired vascular function, and increased sympathetic activity and are at increased risk of cardiovascular disease. The aim of this study was to assess the effects of 12 weeks of exercise training (70-80% maximum volume of oxygen utilization) in women who had had preeclampsia on physical fitness, components of MetS, vasculature, and autonomic functions compared with healthy control subjects. Our prospective case-control study included 24 normotensive women who had had preeclampsia and 20 control subjects who were matched for age and postpartum interval (all 6-12 months after delivery). Before and after training, we measured all components of MetS (ie, BP, lipids, glucose/insulin, and albuminuria), carotid intima media thickness (IMT) and brachial and superficial femoral artery endothelial function that used flow-mediated dilation (FMD). Autonomic activity was quantified with power spectral analysis (low-frequency/high-frequency power [LF/HF] ratio). At baseline, women who had had preeclampsia demonstrated higher values of most components of MetS. Compared with the control subjects, women who had had preeclampsia had increased IMT (580 ± 92 μm vs 477 ± 65 μm, respectively), impaired endothelial function (FMD brachial artery, 5.3% ± 2.2% vs 10.8% ± 3.5%, respectively; FMD superficial femoral artery, 4.9% ± 2.1% vs 8.7% ± 3.2%, respectively) and increased LF/HF power ratio (2.2 ± 1.0 vs 1.3 ± 0.4, respectively; all P exercise training decreased values of most components of MetS and IMT, improved FMD, and concurrently reduced LF/HF. Despite these improvements, vascular and autonomic variables did not normalize by 12 weeks of training in women who had had preeclampsia. This study demonstrates that exercise training in women who had had preeclampsia and control subjects improves components of MetS, endothelial function, vascular wall thickness, and autonomic control

  18. Application of CMAC Neural Network Coupled with Active Disturbance Rejection Control Strategy on Three-motor Synchronization Control System

    Directory of Open Access Journals (Sweden)

    Hui Li

    2014-04-01

    Full Text Available Three-motor synchronous coordination system is a MI-MO nonlinear and complex control system. And it often works in poor working condition. Advanced control strategies are required to improve the control performance of the system and to achieve the decoupling between main motor speed and tension. Cerebellar Model Articulation Controller coupled with Active Disturbance Rejection Control (CMAC-ADRC control strategy is proposed. The speed of the main motor and tensions between two motors is decoupled by extended state observer (ESO in ADRC. ESO in ADRC is used to compensate internal and external disturbances of the system online. And the anti interference of the system is improved by ESO. And the same time the control model is optimized. Feedforward control is implemented by the adoption of CMAC neural network controller. And control precision of the system is improved in reason of CMAC. The overshoot of the system can be reduced without affecting the dynamic response of the system by the use of CMAC-ADRC. The simulation results show that: the CMAC- ADRC control strategy is better than the traditional PID control strategy. And CMAC-ADRC control strategy can achieve the decoupling between speed and tension. The control system using CMAC-ADRC have strong anti-interference ability and small regulate time and small overshoot. The magnitude of the system response incited by the interference using CMAC-ADRC is smaller than the system using conventional PID control 6.43 %. And the recovery time of the system with CMAC-ADRC is shorter than the system with traditional PID control 0.18 seconds. And the triangular wave tracking error of the system with CMAC-ADRC is smaller than the system with conventional PID control 0.24 rad/min. Thus the CMAC-ADRC control strategy is a good control strategy and is able to fit three-motor synchronous coordinated control.

  19. The Lateral Tracking Control for the Intelligent Vehicle Based on Adaptive PID Neural Network

    Science.gov (United States)

    Han, Gaining; Fu, Weiping; Wang, Wen; Wu, Zongsheng

    2017-01-01

    The intelligent vehicle is a complicated nonlinear system, and the design of a path tracking controller is one of the key technologies in intelligent vehicle research. This paper mainly designs a lateral control dynamic model of the intelligent vehicle, which is used for lateral tracking control. Firstly, the vehicle dynamics model (i.e., transfer function) is established according to the vehicle parameters. Secondly, according to the vehicle steering control system and the CARMA (Controlled Auto-Regression and Moving-Average) model, a second-order control system model is built. Using forgetting factor recursive least square estimation (FFRLS), the system parameters are identified. Finally, a neural network PID (Proportion Integral Derivative) controller is established for lateral path tracking control based on the vehicle model and the steering system model. Experimental simulation results show that the proposed model and algorithm have the high real-time and robustness in path tracing control. This provides a certain theoretical basis for intelligent vehicle autonomous navigation tracking control, and lays the foundation for the vertical and lateral coupling control. PMID:28556817

  20. The Lateral Tracking Control for the Intelligent Vehicle Based on Adaptive PID Neural Network.

    Science.gov (United States)

    Han, Gaining; Fu, Weiping; Wang, Wen; Wu, Zongsheng

    2017-05-30

    The intelligent vehicle is a complicated nonlinear system, and the design of a path tracking controller is one of the key technologies in intelligent vehicle research. This paper mainly designs a lateral control dynamic model of the intelligent vehicle, which is used for lateral tracking control. Firstly, the vehicle dynamics model (i.e., transfer function) is established according to the vehicle parameters. Secondly, according to the vehicle steering control system and the CARMA (Controlled Auto-Regression and Moving-Average) model, a second-order control system model is built. Using forgetting factor recursive least square estimation (FFRLS), the system parameters are identified. Finally, a neural network PID (Proportion Integral Derivative) controller is established for lateral path tracking control based on the vehicle model and the steering system model. Experimental simulation results show that the proposed model and algorithm have the high real-time and robustness in path tracing control. This provides a certain theoretical basis for intelligent vehicle autonomous navigation tracking control, and lays the foundation for the vertical and lateral coupling control.