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Sample records for controls cardiac neural

  1. Defining the neural fulcrum for chronic vagus nerve stimulation: implications for integrated cardiac control.

    Science.gov (United States)

    Ardell, Jeffrey L; Nier, Heath; Hammer, Matthew; Southerland, E Marie; Ardell, Christopher L; Beaumont, Eric; KenKnight, Bruce H; Armour, J Andrew

    2017-09-01

    The evoked cardiac response to bipolar cervical vagus nerve stimulation (VNS) reflects a dynamic interaction between afferent mediated decreases in central parasympathetic drive and suppressive effects evoked by direct stimulation of parasympathetic efferent axons to the heart. The neural fulcrum is defined as the operating point, based on frequency-amplitude-pulse width, where a null heart rate response is reproducibly evoked during the on-phase of VNS. Cardiac control, based on the principal of the neural fulcrum, can be elicited from either vagus. Beta-receptor blockade does not alter the tachycardia phase to low intensity VNS, but can increase the bradycardia to higher intensity VNS. While muscarinic cholinergic blockade prevented the VNS-induced bradycardia, clinically relevant doses of ACE inhibitors, beta-blockade and the funny channel blocker ivabradine did not alter the VNS chronotropic response. While there are qualitative differences in VNS heart control between awake and anaesthetized states, the physiological expression of the neural fulcrum is maintained. Vagus nerve stimulation (VNS) is an emerging therapy for treatment of chronic heart failure and remains a standard of therapy in patients with treatment-resistant epilepsy. The objective of this work was to characterize heart rate (HR) responses (HRRs) during the active phase of chronic VNS over a wide range of stimulation parameters in order to define optimal protocols for bidirectional bioelectronic control of the heart. In normal canines, bipolar electrodes were chronically implanted on the cervical vagosympathetic trunk bilaterally with anode cephalad to cathode (n = 8, 'cardiac' configuration) or with electrode positions reversed (n = 8, 'epilepsy' configuration). In awake state, HRRs were determined for each combination of pulse frequency (2-20 Hz), intensity (0-3.5 mA) and pulse widths (130-750 μs) over 14 months. At low intensities and higher frequency VNS, HR increased during the

  2. Central-peripheral neural network interactions evoked by vagus nerve stimulation: functional consequences on control of cardiac function.

    Science.gov (United States)

    Ardell, Jeffrey L; Rajendran, Pradeep S; Nier, Heath A; KenKnight, Bruce H; Armour, J Andrew

    2015-11-15

    Using vagus nerve stimulation (VNS), we sought to determine the contribution of vagal afferents to efferent control of cardiac function. In anesthetized dogs, the right and left cervical vagosympathetic trunks were stimulated in the intact state, following ipsilateral or contralateral vagus nerve transection (VNTx), and then following bilateral VNTx. Stimulations were performed at currents from 0.25 to 4.0 mA, frequencies from 2 to 30 Hz, and a 500-μs pulse width. Right or left VNS evoked significantly greater current- and frequency-dependent suppression of chronotropic, inotropic, and lusitropic function subsequent to sequential VNTx. Bradycardia threshold was defined as the current first required for a 5% decrease in heart rate. The threshold for the right vs. left vagus-induced bradycardia in the intact state (2.91 ± 0.18 and 3.47 ± 0.20 mA, respectively) decreased significantly with right VNTx (1.69 ± 0.17 mA for right and 3.04 ± 0.27 mA for left) and decreased further following bilateral VNTx (1.29 ± 0.16 mA for right and 1.74 ± 0.19 mA for left). Similar effects were observed following left VNTx. The thresholds for afferent-mediated effects on cardiac parameters were 0.62 ± 0.04 and 0.65 ± 0.06 mA with right and left VNS, respectively, and were reflected primarily as augmentation. Afferent-mediated tachycardias were maintained following β-blockade but were eliminated by VNTx. The increased effectiveness and decrease in bradycardia threshold with sequential VNTx suggest that 1) vagal afferents inhibit centrally mediated parasympathetic efferent outflow and 2) the ipsilateral and contralateral vagi exert a substantial buffering capacity. The intact threshold reflects the interaction between multiple levels of the cardiac neural hierarchy.

  3. Chronic vagal stimulation for the treatment of low ejection fraction heart failure : results of the NEural Cardiac TherApy foR Heart Failure (NECTAR-HF) randomized controlled trial

    NARCIS (Netherlands)

    Zannad, Faiez; De Ferrari, Gaetano M; Tuinenburg, Anton E; Wright, David; Brugada, Josep; Butter, Christian; Klein, Helmut; Stolen, Craig; Meyer, Scott; Stein, Kenneth M; Ramuzat, Agnes; Schubert, Bernd; Daum, Doug; Neuzil, Petr; Botman, Cornelis; Castel, Maria Angeles; D'Onofrio, Antonio; Solomon, Scott D; Wold, Nicholas; Ruble, Stephen B

    2015-01-01

    AIM: The neural cardiac therapy for heart failure (NECTAR-HF) was a randomized sham-controlled trial designed to evaluate whether a single dose of vagal nerve stimulation (VNS) would attenuate cardiac remodelling, improve cardiac function and increase exercise capacity in symptomatic heart failure

  4. Cardiac neural crest contributes to cardiomyogenesis in zebrafish.

    Science.gov (United States)

    Sato, Mariko; Yost, H Joseph

    2003-05-01

    In birds and mammals, cardiac neural crest is essential for heart development and contributes to conotruncal cushion formation and outflow tract septation. The zebrafish prototypical heart lacks outflow tract septation, raising the question of whether cardiac neural crest exists in zebrafish. Here, results from three distinct lineage-labeling approaches identify zebrafish cardiac neural crest cells and indicate that these cells have the ability to generate MF20-positive muscle cells in the myocardium of the major chambers during development. Fate-mapping demonstrates that cardiac neural crest cells originate both from neural tube regions analogous to those found in birds, as well as from a novel region rostral to the otic vesicle. In contrast to other vertebrates, cardiac neural crest invades the myocardium in all segments of the heart, including outflow tract, atrium, atrioventricular junction, and ventricle in zebrafish. Three distinct groups of premigratory neural crest along the rostrocaudal axis have different propensities to contribute to different segments in the heart and are correspondingly marked by unique combinations of gene expression patterns. Zebrafish will serve as a model for understanding interactions between cardiac neural crest and cardiovascular development.

  5. 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

  6. 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....

  7. 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....

  8. Neural Networks in Control Applications

    DEFF Research Database (Denmark)

    Sørensen, O.

    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....... - Control concepts including parameter estimation - Control concepts including inverse modelling - Control concepts including optimal control For each of the three groups, different control concepts and specific training methods are detailed described.Further, all control concepts are tested on the same......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...

  9. Chaos control of cardiac arrhythmias.

    Science.gov (United States)

    Garfinkel, A; Weiss, J N; Ditto, W L; Spano, M L

    1995-01-01

    Chaos theory has shown that many disordered and erratic phenomena are in fact deterministic, and can be understood causally and controlled. The prospect that cardiac arrhythmias might be instances of deterministic chaos is therefore intriguing. We used a recently developed method of chaos control to stabilize a ouabain-induced arrhythmia in rabbit ventricular tissue in vitro. Extension of these results to clinically significant arrhythmias such as fibrillation will require overcoming the additional obstacles of spatiotemporal complexity.

  10. 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...

  11. 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...

  12. Connecting teratogen-induced congenital heart defects to neural crest cells and their effect on cardiac function.

    Science.gov (United States)

    Karunamuni, Ganga H; Ma, Pei; Gu, Shi; Rollins, Andrew M; Jenkins, Michael W; Watanabe, Michiko

    2014-09-01

    Neural crest cells play many key roles in embryonic development, as demonstrated by the abnormalities that result from their specific absence or dysfunction. Unfortunately, these key cells are particularly sensitive to abnormalities in various intrinsic and extrinsic factors, such as genetic deletions or ethanol-exposure that lead to morbidity and mortality for organisms. This review discusses the role identified for a segment of neural crest in regulating the morphogenesis of the heart and associated great vessels. The paradox is that their derivatives constitute a small proportion of cells to the cardiovascular system. Findings supporting that these cells impact early cardiac function raises the interesting possibility that they indirectly control cardiovascular development at least partially through regulating function. Making connections between insults to the neural crest, cardiac function, and morphogenesis is more approachable with technological advances. Expanding our understanding of early functional consequences could be useful in improving diagnosis and testing therapies.

  13. 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.

  14. Optogenetic stimulation of multiwell MEA plates for neural and cardiac applications

    Science.gov (United States)

    Clements, Isaac P.; Millard, Daniel C.; Nicolini, Anthony M.; Preyer, Amanda J.; Grier, Robert; Heckerling, Andrew; Blum, Richard A.; Tyler, Phillip; McSweeney, K. M.; Lu, Yi-Fan; Hall, Diana; Ross, James D.

    2016-03-01

    Microelectrode array (MEA) technology enables advanced drug screening and "disease-in-a-dish" modeling by measuring the electrical activity of cultured networks of neural or cardiac cells. Recent developments in human stem cell technologies, advancements in genetic models, and regulatory initiatives for drug screening have increased the demand for MEA-based assays. In response, Axion Biosystems previously developed a multiwell MEA platform, providing up to 96 MEA culture wells arrayed into a standard microplate format. Multiwell MEA-based assays would be further enhanced by optogenetic stimulation, which enables selective excitation and inhibition of targeted cell types. This capability for selective control over cell culture states would allow finer pacing and probing of cell networks for more reliable and complete characterization of complex network dynamics. Here we describe a system for independent optogenetic stimulation of each well of a 48-well MEA plate. The system enables finely graded control of light delivery during simultaneous recording of network activity in each well. Using human induced pluripotent stem cell (hiPSC) derived cardiomyocytes and rodent primary neuronal cultures, we demonstrate high channel-count light-based excitation and suppression in several proof-of-concept experimental models. Our findings demonstrate advantages of combining multiwell optical stimulation and MEA recording for applications including cardiac safety screening, neural toxicity assessment, and advanced characterization of complex neuronal diseases.

  15. Genetic dissection of cardiac growth control pathways

    Science.gov (United States)

    MacLellan, W. R.; Schneider, M. D.

    2000-01-01

    Cardiac muscle cells exhibit two related but distinct modes of growth that are highly regulated during development and disease. Cardiac myocytes rapidly proliferate during fetal life but exit the cell cycle irreversibly soon after birth, following which the predominant form of growth shifts from hyperplastic to hypertrophic. Much research has focused on identifying the candidate mitogens, hypertrophic agonists, and signaling pathways that mediate these processes in isolated cells. What drives the proliferative growth of embryonic myocardium in vivo and the mechanisms by which adult cardiac myocytes hypertrophy in vivo are less clear. Efforts to answer these questions have benefited from rapid progress made in techniques to manipulate the murine genome. Complementary technologies for gain- and loss-of-function now permit a mutational analysis of these growth control pathways in vivo in the intact heart. These studies have confirmed the importance of suspected pathways, have implicated unexpected pathways as well, and have led to new paradigms for the control of cardiac growth.

  16. Mitochondrial quality control in cardiac diseases.

    Directory of Open Access Journals (Sweden)

    Juliane Campos

    2016-10-01

    Full Text Available Disruption of mitochondrial homeostasis is a hallmark of cardiac diseases. Therefore, maintenance of mitochondrial integrity through different surveillance mechanisms is critical for cardiomyocyte survival. In this review, we discuss the most recent findings on the central role of mitochondrial quality control processes including regulation of mitochondrial redox balance, aldehyde metabolism, proteostasis, dynamics and clearance in cardiac diseases, highlighting their potential as therapeutic targets.

  17. 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...

  18. Neural Network Controlled Visual Saccades

    Science.gov (United States)

    Johnson, Jeffrey D.; Grogan, Timothy A.

    1989-03-01

    The paper to be presented will discuss research on a computer vision system controlled by a neural network capable of learning through classical (Pavlovian) conditioning. Through the use of unconditional stimuli (reward and punishment) the system will develop scan patterns of eye saccades necessary to differentiate and recognize members of an input set. By foveating only those portions of the input image that the system has found to be necessary for recognition the drawback of computational explosion as the size of the input image grows is avoided. The model incorporates many features found in animal vision systems, and is governed by understandable and modifiable behavior patterns similar to those reported by Pavlov in his classic study. These behavioral patterns are a result of a neuronal model, used in the network, explicitly designed to reproduce this behavior.

  19. 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...

  20. 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...

  1. 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....

  2. 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....

  3. Neural control of lengthening contractions.

    Science.gov (United States)

    Duchateau, Jacques; Enoka, Roger M

    2016-01-01

    A number of studies over the last few decades have established that the control strategy employed by the nervous system during lengthening (eccentric) differs from those used during shortening (concentric) and isometric contractions. The purpose of this review is to summarize current knowledge on the neural control of lengthening contractions. After a brief discussion of methodological issues that can confound the comparison between lengthening and shortening actions, the review provides evidence that untrained individuals are usually unable to fully activate their muscles during a maximal lengthening contraction and that motor unit activity during submaximal lengthening actions differs from that during shortening actions. Contrary to common knowledge, however, more recent studies have found that the recruitment order of motor units is similar during submaximal shortening and lengthening contractions, but that discharge rate is systematically lower during lengthening actions. Subsequently, the review examines the mechanisms responsible for the specific control of maximal and submaximal lengthening contractions as reported by recent studies on the modulation of cortical and spinal excitability. As similar modulation has been observed regardless of contraction intensity, it appears that spinal and corticospinal excitability are reduced during lengthening compared with shortening and isometric contractions. Nonetheless, the modulation observed during lengthening contractions is mainly attributable to inhibition at the spinal level.

  4. Nonlinear System Control Using Neural Networks

    Directory of Open Access Journals (Sweden)

    Jaroslava Žilková

    2006-10-01

    Full Text Available The paper is focused especially on presenting possibilities of applying off-linetrained artificial neural networks at creating the system inverse models that are used atdesigning control algorithm for non-linear dynamic system. The ability of cascadefeedforward neural networks to model arbitrary non-linear functions and their inverses isexploited. This paper presents a quasi-inverse neural model, which works as a speedcontroller of an induction motor. The neural speed controller consists of two cascadefeedforward neural networks subsystems. The first subsystem provides desired statorcurrent components for control algorithm and the second subsystem providescorresponding voltage components for PWM converter. The availability of the proposedcontroller is verified through the MATLAB simulation. The effectiveness of the controller isdemonstrated for different operating conditions of the drive system.

  5. Position Control of Motion Compensation Cardiac Catheters

    Science.gov (United States)

    Kesner, Samuel B.; Howe, Robert D.

    2011-01-01

    Robotic catheters have the potential to revolutionize cardiac surgery by enabling minimally invasive structural repairs within the beating heart. This paper presents an actuated catheter system that compensates for the fast motion of cardiac tissue using 3D ultrasound image guidance. We describe the design and operation of the mechanical drive system and catheter module and analyze the catheter performance limitations of friction and backlash in detail. To mitigate these limitations, we propose and evaluate mechanical and control system compensation methods, including inverse and model-based backlash compensation, to improve the system performance. Finally, in vivo results are presented that demonstrate that the catheter can track the cardiac tissue motion with less than 1 mm RMS error. The ultimate goal of this research is to create a fast and dexterous robotic catheter system that can perform surgery on the delicate structures inside of the beating heart. PMID:21874124

  6. 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.

  7. Neural dynamics for mobile robot adaptive control

    OpenAIRE

    Oubbati, Mohamed

    2006-01-01

    In this thesis, we investigate how dynamics in recurrent neural networks can be used to solve some specific mobile robot problems. We have designed a motion control approach based on a novel recurrent neural network. The advantage of this approach is that, no knowledge about the dynamic model is required, and no synaptic weight changing is needed in presence of time varying parameters. Furthermore, this approach allows a single fixed-weight network to act as a dynamic controller for several d...

  8. Longterm effects of cardiac mediastinal nerve cryoablation on neural inducibility of atrial fibrillation in canines.

    Science.gov (United States)

    Leiria, Tiago Luiz Luz; Glavinovic, Tamara; Armour, J Andrew; Cardinal, René; de Lima, Gustavo Glotz; Kus, Teresa

    2011-04-26

    In canines, excessive activation of select mediastinal nerve inputs to the intrinsic cardiac nervous system induces atrial fibrillation (AF). Since ablation of neural elements is proposed as an adjunct to circumferential pulmonary vein ablation for AF, we investigated the short and long-term effects of mediastinal nerve ablation on AF inducibility. Under general anesthesia, in 11 dogs several mediastinal nerve sites were identified on the superior vena cava that, when stimulated electrically during the atrial refractory period, reproducibly initiated AF. Cryoablation of one nerve site was then performed and inducibility retested early (1-2 months post Cryo; n=7) or late (4 months post Cryo; n=4). Four additional dogs that underwent a sham procedure were retested 1 to 2 months post-surgery. Stimulation induced AF at 91% of nerve sites tested in control versus 21% nerve sites early and 54% late post-ablation (both P<0.05). Fewer stimuli were required to induce AF in controls versus the Early Cryo group; this capacity returned to normal values in the Late Cryo group. AF episodes were longer in control versus the Early or Late Cryo groups. Heart rate responses to vagal or stellate ganglion stimulation, as well as to local nicotine infusion into the right coronary artery, were similar in all groups. In conclusion, focal damage to intrinsic cardiac neuronal inputs causes short-term stunning of neuronal inducibility of AF without major loss of overall adrenergic or cholinergic efferent neuronal control. That recovery of AF inducibility occurs rapidly post-surgery indicates the plasticity of intrathoracic neuronal elements to focal injury. Copyright © 2011 Elsevier B.V. All rights reserved.

  9. Implementation study of an analog spiking neural network for assisting cardiac delay prediction in a cardiac resynchronization therapy device.

    Science.gov (United States)

    Sun, Qing; Schwartz, François; Michel, Jacques; Herve, Yannick; Dalmolin, Renzo

    2011-06-01

    In this paper, we aim at developing an analog spiking neural network (SNN) for reinforcing the performance of conventional cardiac resynchronization therapy (CRT) devices (also called biventricular pacemakers). Targeting an alternative analog solution in 0.13- μm CMOS technology, this paper proposes an approach to improve cardiac delay predictions in every cardiac period in order to assist the CRT device to provide real-time optimal heartbeats. The primary analog SNN architecture is proposed and its implementation is studied to fulfill the requirement of very low energy consumption. By using the Hebbian learning and reinforcement learning algorithms, the intended adaptive CRT device works with different functional modes. The simulations of both learning algorithms have been carried out, and they were shown to demonstrate the global functionalities. To improve the realism of the system, we introduce various heart behavior models (with constant/variable heart rates) that allow pathologic simulations with/without noise on the signals of the input sensors. The simulations of the global system (pacemaker models coupled with heart models) have been investigated and used to validate the analog spiking neural network implementation.

  10. Threshold control of chaotic neural network.

    Science.gov (United States)

    He, Guoguang; Shrimali, Manish Dev; Aihara, Kazuyuki

    2008-01-01

    The chaotic neural network constructed with chaotic neurons exhibits rich dynamic behaviour with a nonperiodic associative memory. In the chaotic neural network, however, it is difficult to distinguish the stored patterns in the output patterns because of the chaotic state of the network. In order to apply the nonperiodic associative memory into information search, pattern recognition etc. it is necessary to control chaos in the chaotic neural network. We have studied the chaotic neural network with threshold activated coupling, which provides a controlled network with associative memory dynamics. The network converges to one of its stored patterns or/and reverse patterns which has the smallest Hamming distance from the initial state of the network. The range of the threshold applied to control the neurons in the network depends on the noise level in the initial pattern and decreases with the increase of noise. The chaos control in the chaotic neural network by threshold activated coupling at varying time interval provides controlled output patterns with different temporal periods which depend upon the control parameters.

  11. Integrated Neural Flight and Propulsion Control System

    Science.gov (United States)

    Kaneshige, John; Gundy-Burlet, Karen; Norvig, Peter (Technical Monitor)

    2001-01-01

    This paper describes an integrated neural flight and propulsion control system. which uses a neural network based approach for applying alternate sources of control power in the presence of damage or failures. Under normal operating conditions, the system utilizes conventional flight control surfaces. Neural networks are used to provide consistent handling qualities across flight conditions and for different aircraft configurations. Under damage or failure conditions, the system may utilize unconventional flight control surface allocations, along with integrated propulsion control, when additional control power is necessary for achieving desired flight control performance. In this case, neural networks are used to adapt to changes in aircraft dynamics and control allocation schemes. Of significant importance here is the fact that this system can operate without emergency or backup flight control mode operations. An additional advantage is that this system can utilize, but does not require, fault detection and isolation information or explicit parameter identification. Piloted simulation studies were performed on a commercial transport aircraft simulator. Subjects included both NASA test pilots and commercial airline crews. Results demonstrate the potential for improving handing qualities and significantly increasing survivability rates under various simulated failure conditions.

  12. Control of Unknown Chaotic Systems Based on Neural Predictive Control

    Institute of Scientific and Technical Information of China (English)

    LIDong-Mei; WANGZheng-Ou

    2003-01-01

    We introduce the predictive control into the control of chaotic system and propose a neural network control algorithm based on predictive control. The proposed control system stabilizes the chaotic motion in an unknown chaotic system onto the desired target trajectory. The proposed algorithm is simple and its convergence speed is much higher than existing similar algorithms. The control system can control hyperchaos. We analyze the stability of the control system and prove the convergence property of the neural controller. The theoretic derivation and simulations demonstrate the effectiveness of the algorithm.

  13. Robust adaptive neural network control with supervisory controller

    Institute of Scientific and Technical Information of China (English)

    张天平; 梅建东

    2004-01-01

    The problem of direct adaptive neural network control for a class of uncertain nonlinear systems with unknown constant control gain is studied in this paper. Based on the supervisory control strategy and the approximation capability of multilayer neural networks (MNNs), a novel design scheme of direct adaptive neural network controller is proposed.The adaptive law of the adjustable parameter vector and the matrix of weights in the neural networks and the gain of sliding mode control term to adaptively compensate for the residual and the approximation error of MNNs is determined by using a Lyapunov method. The approach does not require the optimal approximation error to be square-integrable or the supremum of the optimal approximation error to be known. By theoretical analysis, the closed-loop control system is proven to be globally stable in the sense that all signals involved are bounded, with tracking error converging to zero.Simulation results demonstrate the effectiveness of the approach.

  14. Control of Unknown Chaotic Systems Based on Neural Predictive Control

    Institute of Scientific and Technical Information of China (English)

    LI Dong-Mei; WANG Zheng-Ou

    2003-01-01

    We introduce the predictive control into the control of chaotic system and propose a neural networkcontrol algorithm based on predictive control. The proposed control system stabilizes the chaotic motion in an unknownchaotic system onto the desired target trajectory. The proposed algorithm is simple and its convergence speed is muchhigher than existing similar algorithms. The control system can control hyperchaos. We analyze the stability of thecontrol system and prove the convergence property of the neural controller. The theoretic derivation and simulationsdemonstrate the effectiveness of the algorithm.

  15. 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

  16. 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.

  17. Color control of printers by neural networks

    Science.gov (United States)

    Tominaga, Shoji

    1998-07-01

    A method is proposed for solving the mapping problem from the 3D color space to the 4D CMYK space of printer ink signals by means of a neural network. The CIE-L*a*b* color system is used as the device-independent color space. The color reproduction problem is considered as the problem of controlling an unknown static system with four inputs and three outputs. A controller determines the CMYK signals necessary to produce the desired L*a*b* values with a given printer. Our solution method for this control problem is based on a two-phase procedure which eliminates the need for UCR and GCR. The first phase determines a neural network as a model of the given printer, and the second phase determines the combined neural network system by combining the printer model and the controller in such a way that it represents an identity mapping in the L*a*b* color space. Then the network of the controller part realizes the mapping from the L*a*b* space to the CMYK space. Practical algorithms are presented in the form of multilayer feedforward networks. The feasibility of the proposed method is shown in experiments using a dye sublimation printer and an ink jet printer.

  18. Understanding the brain by controlling neural activity

    OpenAIRE

    Krug, Kristine; Salzman, C. Daniel; Waddell, Scott

    2015-01-01

    Causal methods to interrogate brain function have been employed since the advent of modern neuroscience in the nineteenth century. Initially, randomly placed electrodes and stimulation of parts of the living brain were used to localize specific functions to these areas. Recent technical developments have rejuvenated this approach by providing more precise tools to dissect the neural circuits underlying behaviour, perception and cognition. Carefully controlled behavioural experiments have been...

  19. A Direct Feedback Control Based on Fuzzy Recurrent Neural Network

    Institute of Scientific and Technical Information of China (English)

    李明; 马小平

    2002-01-01

    A direct feedback control system based on fuzzy-recurrent neural network is proposed, and a method of training weights of fuzzy-recurrent neural network was designed by applying modified contract mapping genetic algorithm. Computer simul ation results indicate that fuzzy-recurrent neural network controller has perfect dynamic and static performances .

  20. Prediction based chaos control via a new neural network

    Energy Technology Data Exchange (ETDEWEB)

    Shen Liqun [School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001 (China)], E-mail: liqunshen@gmail.com; Wang Mao [Space Control and Inertia Technology Research Center, Harbin Institute of Technology, Harbin 150001 (China); Liu Wanyu [School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001 (China); Sun Guanghui [Space Control and Inertia Technology Research Center, Harbin Institute of Technology, Harbin 150001 (China)

    2008-11-17

    In this Letter, a new chaos control scheme based on chaos prediction is proposed. To perform chaos prediction, a new neural network architecture for complex nonlinear approximation is proposed. And the difficulty in building and training the neural network is also reduced. Simulation results of Logistic map and Lorenz system show the effectiveness of the proposed chaos control scheme and the proposed neural network.

  1. Neural control of hand muscles during prehension.

    Science.gov (United States)

    Johnston, Jamie A; Winges, Sara A; Santello, Marco

    2009-01-01

    In the past two decades a large number of studies have successfully characterized important features of the kinetics and kinematics of object grasping and manipulation, providing significant insight into how the Central Nervous System (CNS) controls the hand, one of the most complex motor systems, in a variety of behaviors. In this chapter we briefly review studies of hand kinematics and kinetics and highlight their major findings and open questions. The major focus of this chapter is on the neural control of the hand, an objective that has been pursued by studies on electromyography (EMG) of hand muscles. Here we review what has been learned through different yet complementary methodological approaches. In particular, the study of single motor unit activity has revealed how the distribution of common neural input within and across hand muscles might reflect a muscle-pair specific organization. Studies of motor unit population have revealed important synergistic patterns of muscle activity while also revealing muscle-pair specific patterns of neural coupling. We conclude the chapter with the results of recent simulation studies aiming at combining advantages of single and multi-unit recordings to maximize the amount of information that can be extracted from EMG signal analysis.

  2. Nonlinear system identification and control based on modular neural networks.

    Science.gov (United States)

    Puscasu, Gheorghe; Codres, Bogdan

    2011-08-01

    A new approach for nonlinear system identification and control based on modular neural networks (MNN) is proposed in this paper. The computational complexity of neural identification can be greatly reduced if the whole system is decomposed into several subsystems. This is obtained using a partitioning algorithm. Each local nonlinear model is associated with a nonlinear controller. These are also implemented by neural networks. The switching between the neural controllers is done by a dynamical switcher, also implemented by neural networks, that tracks the different operating points. The proposed multiple modelling and control strategy has been successfully tested on simulated laboratory scale liquid-level system.

  3. Optogenetic Light Crafting Tools for the Control of Cardiac Arrhythmias.

    Science.gov (United States)

    Richter, Claudia; Christoph, Jan; Lehnart, Stephan E; Luther, Stefan

    2016-01-01

    The control of spatiotemporal dynamics in biological systems is a fundamental problem in nonlinear sciences and has important applications in engineering and medicine. Optogenetic tools combined with advanced optical technologies provide unique opportunities to develop and validate novel approaches to control spatiotemporal complexity in neuronal and cardiac systems. Understanding of the mechanisms and instabilities underlying the onset, perpetuation, and control of cardiac arrhythmias will enable the development and translation of novel therapeutic approaches. Here we describe in detail the preparation and optical mapping of transgenic channelrhodopsin-2 (ChR2) mouse hearts, cardiac cell cultures, and the optical setup for photostimulation using digital light processing.

  4. 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.

  5. 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.

  6. Macro-micro imaging of cardiac-neural circuits in co-cultures from normal and diseased hearts.

    Science.gov (United States)

    Bub, Gil; Burton, Rebecca-Ann B

    2015-07-15

    The autonomic nervous system plays an important role in the modulation of normal cardiac rhythm, but is also implicated in modulating the heart's susceptibility to re-entrant ventricular and atrial arrhythmias. The mechanisms by which the autonomic nervous system is pro-arrhythmic or anti-arrhythmic is multifaceted and varies for different types of arrhythmia and their cardiac substrates. Despite decades of research in this area, fundamental questions related to how neuron density and spatial organization modulate cardiac wave dynamics remain unanswered. These questions may be ill-posed in intact tissues where the activity of individual cells is often experimentally inaccessible. Development of simplified biological models that would allow us to better understand the influence of neural activation on cardiac activity can be beneficial. This Symposium Review summarizes the development of in vitro cardiomyocyte cell culture models of re-entrant activity, as well as challenges associated with extending these models to include the effects of neural activation.

  7. 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.

  8. 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.

  9. Neural-networks-based Modelling and a Fuzzy Neural Networks Controller of MCFC

    Institute of Scientific and Technical Information of China (English)

    2002-01-01

    Molten Carbonate Fuel Cells (MCFC) are produced with a highly efficient and clean power generation technology which will soon be widely utilized. The temperature characters of MCFC stack are briefly analyzed. A radial basis function (RBF) neural networks identification technology is applied to set up the temperature nonlinear model of MCFC stack, and the identification structure, algorithm and modeling training process are given in detail. A fuzzy controller of MCFC stack is designed. In order to improve its online control ability, a neural network trained by the I/O data of a fuzzy controller is designed. The neural networks can memorize and expand the inference rules of the fuzzy controller and substitute for the fuzzy controller to control MCFC stack online. A detailed design of the controller is given. The validity of MCFC stack modelling based on neural networks and the superior performance of the fuzzy neural networks controller are proved by Simulations.

  10. Dishevelled 2 is essential for cardiac outflow tract development, somite segmentation and neural tube closure.

    Science.gov (United States)

    Hamblet, Natasha S; Lijam, Nardos; Ruiz-Lozano, Pilar; Wang, Jianbo; Yang, Yasheng; Luo, Zhenge; Mei, Lin; Chien, Kenneth R; Sussman, Daniel J; Wynshaw-Boris, Anthony

    2002-12-01

    The murine dishevelled 2 (Dvl2) gene is an ortholog of the Drosophila segment polarity gene Dishevelled, a member of the highly conserved Wingless/Wnt developmental pathway. Dvl2-deficient mice were produced to determine the role of Dvl2 in mammalian development. Mice containing null mutations in Dvl2 present with 50% lethality in both inbred 129S6 and in a hybrid 129S6-NIH Black Swiss background because of severe cardiovascular outflow tract defects, including double outlet right ventricle, transposition of the great arteries and persistent truncus arteriosis. The majority of the surviving Dvl2(-/-) mice were female, suggesting that penetrance was influenced by sex. Expression of Pitx2 and plexin A2 was attenuated in Dvl2 null mutants, suggesting a defect in cardiac neural crest development during outflow tract formation. In addition, approximately 90% of Dvl2(-/-) mice have vertebral and rib malformations that affect the proximal as well as the distal parts of the ribs. These skeletal abnormalities were more pronounced in mice deficient for both Dvl1 and Dvl2. Somite differentiation markers used to analyze Dvl2(-/-) and Dvl1(-/-);Dvl2(-/-) mutant embryos revealed mildly aberrant expression of Uncx4.1, delta 1 and myogenin, suggesting defects in somite segmentation. Finally, 2-3% of Dvl2(-/-) embryos displayed thoracic spina bifida, while virtually all Dvl1/2 double mutant embryos displayed craniorachishisis, a completely open neural tube from the midbrain to the tail. Thus, Dvl2 is essential for normal cardiac morphogenesis, somite segmentation and neural tube closure, and there is functional redundancy between Dvl1 and Dvl2 in some phenotypes.

  11. Neural Networks Control of a Magnetic Levitation System

    Science.gov (United States)

    2001-04-17

    investigation of the use of artificial neural networks (ANN) in conjunction of proportional-integral-derivative ( PID ) controllers in control of non...neural networks in controlling closed-loop active magnetic bearing and comparison with the use of PID controllers . The obtained results should create a

  12. 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....

  13. Neural Correlates of Consciousness at Near-Electrocerebral Silence in an Asphyxial Cardiac Arrest Model.

    Science.gov (United States)

    Lee, Donald E; Lee, Lauren G; Siu, Danny; Bazrafkan, Afsheen K; Farahabadi, Maryam H; Dinh, Tin J; Orellana, Josue; Xiong, Wei; Lopour, Beth A; Akbari, Yama

    2017-04-01

    Recent electrophysiological studies have suggested surges in electrical correlates of consciousness (i.e., elevated gamma power and connectivity) after cardiac arrest (CA). This study examines electrocorticogram (ECoG) activity and coherence of the dying brain during asphyxial CA. Male Wistar rats (n = 16) were induced with isoflurane anesthesia, which was washed out before asphyxial CA. Mean phase coherence and ECoG power were compared during different stages of the asphyxial period to assess potential neural correlates of consciousness. After asphyxia, the ECoG progressed through four distinct stages (asphyxial stages 1-4 [AS1-4]), including a transient period of near-electrocerebral silence lasting several seconds (AS3). Electrocerebral silence (AS4) occurred within 1 min of the start of asphyxia, and pulseless electrical activity followed the start of AS4 by 1-2 min. AS3 was linked to a significant increase in frontal coherence between the left and right motor cortices (p silence contains distinctive neural activity. Specifically, the burst in frontal coherence and posterior shift of ECoG power that we find during this period immediately preceding CA may be a neural correlate of conscious processing.

  14. 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.

  15. Left ventricle segmentation in cardiac MRI images using fully convolutional neural networks

    Science.gov (United States)

    Vázquez Romaguera, Liset; Costa, Marly Guimarães Fernandes; Romero, Francisco Perdigón; Costa Filho, Cicero Ferreira Fernandes

    2017-03-01

    According to the World Health Organization, cardiovascular diseases are the leading cause of death worldwide, accounting for 17.3 million deaths per year, a number that is expected to grow to more than 23.6 million by 2030. Most cardiac pathologies involve the left ventricle; therefore, estimation of several functional parameters from a previous segmentation of this structure can be helpful in diagnosis. Manual delineation is a time consuming and tedious task that is also prone to high intra and inter-observer variability. Thus, there exists a need for automated cardiac segmentation method to help facilitate the diagnosis of cardiovascular diseases. In this work we propose a deep fully convolutional neural network architecture to address this issue and assess its performance. The model was trained end to end in a supervised learning stage from whole cardiac MRI images input and ground truth to make a per pixel classification. For its design, development and experimentation was used Caffe deep learning framework over an NVidia Quadro K4200 Graphics Processing Unit. The net architecture is: Conv64-ReLU (2x) - MaxPooling - Conv128-ReLU (2x) - MaxPooling - Conv256-ReLU (2x) - MaxPooling - Conv512-ReLu-Dropout (2x) - Conv2-ReLU - Deconv - Crop - Softmax. Training and testing processes were carried out using 5-fold cross validation with short axis cardiac magnetic resonance images from Sunnybrook Database. We obtained a Dice score of 0.92 and 0.90, Hausdorff distance of 4.48 and 5.43, Jaccard index of 0.97 and 0.97, sensitivity of 0.92 and 0.90 and specificity of 0.99 and 0.99, overall mean values with SGD and RMSProp, respectively.

  16. Spatiotemporal control to eliminate cardiac alternans using isostable reduction

    Science.gov (United States)

    Wilson, Dan; Moehlis, Jeff

    2017-03-01

    Cardiac alternans, an arrhythmia characterized by a beat-to-beat alternation of cardiac action potential durations, is widely believed to facilitate the transition from normal cardiac function to ventricular fibrillation and sudden cardiac death. Alternans arises due to an instability of a healthy period-1 rhythm, and most dynamical control strategies either require extensive knowledge of the cardiac system, making experimental validation difficult, or are model independent and sacrifice important information about the specific system under study. Isostable reduction provides an alternative approach, in which the response of a system to external perturbations can be used to reduce the complexity of a cardiac system, making it easier to work with from an analytical perspective while retaining many of its important features. Here, we use isostable reduction strategies to reduce the complexity of partial differential equation models of cardiac systems in order to develop energy optimal strategies for the elimination of alternans. Resulting control strategies require significantly less energy to terminate alternans than comparable strategies and do not require continuous state feedback.

  17. 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...... detail and discuss the implementation difficulties. The neural generalized predictive controller is tested on a pneumatic servo sys-tem....

  18. 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.

  19. An Artificial Neural Network Control System for Spacecraft Attitude Stabilization

    Science.gov (United States)

    1990-06-01

    NAVAL POSTGRADUATE SCHOOL Monterey, California ’-DTIC 0 ELECT f NMARO 5 191 N S, U, THESIS B . AN ARTIFICIAL NEURAL NETWORK CONTROL SYSTEM FOR...NO. NO. NO ACCESSION NO 11. TITLE (Include Security Classification) AN ARTIFICIAL NEURAL NETWORK CONTROL SYSTEM FOR SPACECRAFT ATTITUDE STABILIZATION...obsolete a U.S. G v pi.. iim n P.. oiice! toog-eo.5s43 i Approved for public release; distribution is unlimited. AN ARTIFICIAL NEURAL NETWORK CONTROL

  20. 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

  1. Evolution of Neural Controllers for Robot Navigation in Human Environments

    Directory of Open Access Journals (Sweden)

    Genci Capi

    2010-01-01

    Full Text Available Problem statement: In this study, we presented a novel vision-based learning approach for autonomous robot navigation. Approach: In our method, we converted the captured image in a binary one, which after the partition is used as the input of the neural controller. Results: The neural control system, which maps the visual information to motor commands, is evolved online using real robots. Conclusion/Recommendations: We showed that evolved neural networks performed well in indoor human environments. Furthermore, we compared the performance of neural controllers with an algorithmic vision based control method.

  2. CLASSIFICATION OF CARDIAC ARRHYTHMIAS WITH ARTIFICIAL NEURAL NETWORKS ACCORDING TO GENDER DIFFERENCES

    Directory of Open Access Journals (Sweden)

    KASIM SERBEST

    2015-09-01

    Full Text Available Cardiac arrhythmias are common heart diseases. Electrocardiography (ECG is an important measure for diagnosing arrhythmias. Researchers use the ECG signals in order to train artificial neural networks (ANN. In previous studies the ECG signals of males and females were analysed together. We know that there are some differences between male and female ECG signals. This paper suggests that classifying the arrhythmias according to gender differences gives more accurate results. In this study we classify the subjects as normal and right bundle branch block (RBBB using cascade forward back algorithm in MATLAB. The accuracy of network simulations are as follow: 81.25% only male, 80% only female, 40% male and female together.

  3. A Fuzzy-Neural Network Control of Nonlinear Dynamic Systems

    Institute of Scientific and Technical Information of China (English)

    2000-01-01

    In this paper,an adaptive dynamic control scheme based on a fuzzy neural network is presented,that presents utilizes both feed-forward and feedback controller elements.The former of the two elements comprises a neural network with both identification and control role,and the latter is a fuzzy neural algorithm,which is introduced to provide additional control enhancement.The feedforward controller provides only coarse control,whereas the feedback oontroller can generate on-line conditional proposition rule automatically to improve the overall control action.These properties make the design very versatile and applicable to a range of industrial applications.

  4. Neural networks for function approximation in nonlinear control

    Science.gov (United States)

    Linse, Dennis J.; Stengel, Robert F.

    1990-01-01

    Two neural network architectures are compared with a classical spline interpolation technique for the approximation of functions useful in a nonlinear control system. A standard back-propagation feedforward neural network and a cerebellar model articulation controller (CMAC) neural network are presented, and their results are compared with a B-spline interpolation procedure that is updated using recursive least-squares parameter identification. Each method is able to accurately represent a one-dimensional test function. Tradeoffs between size requirements, speed of operation, and speed of learning indicate that neural networks may be practical for identification and adaptation in a nonlinear control environment.

  5. Neural Network Inverse Adaptive Controller Based on Davidon Least Square

    Institute of Scientific and Technical Information of China (English)

    2000-01-01

    General neural network inverse adaptive controller haa two flaws: the first is the slow convergence speed; the second is the invalidation to the non-minimum phase system.These defects limit the scope in which the neural network inverse adaptive controller is used.We employ Davidon least squares in training the multi-layer feedforward neural network used in approximating the inverse model of plant to expedite the convergence,and then through constructing the pseudo-plant,a neural network inverse adaptive controller is put forward which is still effective to the nonlinear non-minimum phase system.The simulation results show the validity of this scheme.

  6. Neural Modeling and Control of Diesel Engine with Pollution Constraints

    CERN Document Server

    Ouladsine, Mustapha; Dovifaaz, Xavier; 10.1007/s10846-005-3806-y

    2009-01-01

    The paper describes a neural approach for modelling and control of a turbocharged Diesel engine. A neural model, whose structure is mainly based on some physical equations describing the engine behaviour, is built for the rotation speed and the exhaust gas opacity. The model is composed of three interconnected neural submodels, each of them constituting a nonlinear multi-input single-output error model. The structural identi?cation and the parameter estimation from data gathered on a real engine are described. The neural direct model is then used to determine a neural controller of the engine, in a specialized training scheme minimising a multivariable criterion. Simulations show the effect of the pollution constraint weighting on a trajectory tracking of the engine speed. Neural networks, which are ?exible and parsimonious nonlinear black-box models, with universal approximation capabilities, can accurately describe or control complex nonlinear systems, with little a priori theoretical knowledge. The present...

  7. 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...

  8. Cardiac Autonomic Control in Individuals With Down Syndrome

    Science.gov (United States)

    Goulopoulou, Styliani; Baynard, Tracy; Collier, Scott; Giannopoulou, Ifigenia; Figueroa, Arturo; Beets, Michael; Pitetti, Kenneth; Fernhall, Bo

    2006-01-01

    Our goal in this study was to compare cardiac autonomic control at rest between 50 individuals with Down syndrome and 24 control participants without disabilities. Resting autonomic function was assessed using analysis of heart rate variability. Participants with Down syndrome had reduced total heart rate variability, which indicates possible…

  9. Neural Networks for Signal Processing and Control

    Science.gov (United States)

    Hesselroth, Ted Daniel

    Neural networks are developed for controlling a robot-arm and camera system and for processing images. The networks are based upon computational schemes that may be found in the brain. In the first network, a neural map algorithm is employed to control a five-joint pneumatic robot arm and gripper through feedback from two video cameras. The pneumatically driven robot arm employed shares essential mechanical characteristics with skeletal muscle systems. To control the position of the arm, 200 neurons formed a network representing the three-dimensional workspace embedded in a four-dimensional system of coordinates from the two cameras, and learned a set of pressures corresponding to the end effector positions, as well as a set of Jacobian matrices for interpolating between these positions. Because of the properties of the rubber-tube actuators of the arm, the position as a function of supplied pressure is nonlinear, nonseparable, and exhibits hysteresis. Nevertheless, through the neural network learning algorithm the position could be controlled to an accuracy of about one pixel (~3 mm) after two hundred learning steps. Applications of repeated corrections in each step via the Jacobian matrices leads to a very robust control algorithm since the Jacobians learned by the network have to satisfy the weak requirement that they yield a reduction of the distance between gripper and target. The second network is proposed as a model for the mammalian vision system in which backward connections from the primary visual cortex (V1) to the lateral geniculate nucleus play a key role. The application of hebbian learning to the forward and backward connections causes the formation of receptive fields which are sensitive to edges, bars, and spatial frequencies of preferred orientations. The receptive fields are learned in such a way as to maximize the rate of transfer of information from the LGN to V1. Orientational preferences are organized into a feature map in the primary visual

  10. Application of neural networks to unsteady aerodynamic control

    Science.gov (United States)

    Faller, William E.; Schreck, Scott J.; Luttges, Marvin W.

    1994-01-01

    The problem under consideration in this viewgraph presentation is to understand, predict, and control the fluid mechanics of dynamic maneuvers, unsteady boundary layers, and vortex dominated flows. One solution is the application of neural networks demonstrating closed-loop control. Neural networks offer unique opportunities: simplify modeling of three dimensional, vortex dominated, unsteady separated flow fields; are effective means for controlling unsteady aerodynamics; and address integration of sensors, controllers, and time lags into adaptive control systems.

  11. 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.

  12. 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.

  13. Neural Networks for Dynamic Flight Control

    Science.gov (United States)

    1993-12-01

    uses the Adaline (22) model for development of the neural networks. Neural Graphics and other AFIT applications use a slightly different model. The...primary difference in the Nguyen application is that the Adaline uses the nonlinear function .f(a) = tanh(a) where standard backprop uses the sigmoid

  14. Neural Control of the Immune System

    Science.gov (United States)

    Sundman, Eva; Olofsson, Peder S.

    2014-01-01

    Neural reflexes support homeostasis by modulating the function of organ systems. Recent advances in neuroscience and immunology have revealed that neural reflexes also regulate the immune system. Activation of the vagus nerve modulates leukocyte cytokine production and alleviates experimental shock and autoimmune disease, and recent data have…

  15. Neural Control of the Immune System

    Science.gov (United States)

    Sundman, Eva; Olofsson, Peder S.

    2014-01-01

    Neural reflexes support homeostasis by modulating the function of organ systems. Recent advances in neuroscience and immunology have revealed that neural reflexes also regulate the immune system. Activation of the vagus nerve modulates leukocyte cytokine production and alleviates experimental shock and autoimmune disease, and recent data have…

  16. 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

  17. Control Loop Sensor Calibration Using Neural Networks for Robotic Control

    Directory of Open Access Journals (Sweden)

    Kathleen A. Kramer

    2011-01-01

    Full Text Available Whether sensor model’s inaccuracies are a result of poor initial modeling or from sensor damage or drift, the effects can be just as detrimental. Sensor modeling errors result in poor state estimation. This, in turn, can cause a control system relying upon the sensor’s measurements to become unstable, such as in robotics where the control system is applied to allow autonomous navigation. A technique referred to as a neural extended Kalman filter (NEKF is developed to provide both state estimation in a control loop and to learn the difference between the true sensor dynamics and the sensor model. The technique requires multiple sensors on the control system so that the properly operating and modeled sensors can be used as truth. The NEKF trains a neural network on-line using the same residuals as the state estimation. The resulting sensor model can then be reincorporated fully into the system to provide the added estimation capability and redundancy.

  18. Thermoelastic steam turbine rotor control based on neural network

    Science.gov (United States)

    Rzadkowski, Romuald; Dominiczak, Krzysztof; Radulski, Wojciech; Szczepanik, R.

    2015-12-01

    Considered here are Nonlinear Auto-Regressive neural networks with eXogenous inputs (NARX) as a mathematical model of a steam turbine rotor for controlling steam turbine stress on-line. In order to obtain neural networks that locate critical stress and temperature points in the steam turbine during transient states, an FE rotor model was built. This model was used to train the neural networks on the basis of steam turbine transient operating data. The training included nonlinearity related to steam turbine expansion, heat exchange and rotor material properties during transients. Simultaneous neural networks are algorithms which can be implemented on PLC controllers. This allows for the application neural networks to control steam turbine stress in industrial power plants.

  19. 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...... has not been addressed specifically for these controllers. On the other hand a number of results concerning the stability of receding horizon controllers on a nonlinear system exist. In this paper we present a proof of stability for a predictive controller controlling a neural network model...

  20. Controlled Exposures to Air Pollutants and Risk of Cardiac Arrhythmia

    Science.gov (United States)

    Watts, Simon J.; Hunter, Amanda J.; Shah, Anoop S.V.; Bosson, Jenny A.; Unosson, Jon; Barath, Stefan; Lundbäck, Magnus; Cassee, Flemming R.; Donaldson, Ken; Sandström, Thomas; Blomberg, Anders; Newby, David E.; Mills, Nicholas L.

    2014-01-01

    Background: Epidemiological studies have reported associations between air pollution exposure and increases in cardiovascular morbidity and mortality. Exposure to air pollutants can influence cardiac autonomic tone and reduce heart rate variability, and may increase the risk of cardiac arrhythmias, particularly in susceptible patient groups. Objectives: We investigated the incidence of cardiac arrhythmias during and after controlled exposure to air pollutants in healthy volunteers and patients with coronary heart disease. Methods: We analyzed data from 13 double-blind randomized crossover studies including 282 participants (140 healthy volunteers and 142 patients with stable coronary heart disease) from whom continuous electrocardiograms were available. The incidence of cardiac arrhythmias was recorded for each exposure and study population. Results: There were no increases in any cardiac arrhythmia during or after exposure to dilute diesel exhaust, wood smoke, ozone, concentrated ambient particles, engineered carbon nanoparticles, or high ambient levels of air pollution in either healthy volunteers or patients with coronary heart disease. Conclusions: Acute controlled exposure to air pollutants did not increase the short-term risk of arrhythmia in participants. Research employing these techniques remains crucial in identifying the important pathophysiological pathways involved in the adverse effects of air pollution, and is vital to inform environmental and public health policy decisions. Citation: Langrish JP, Watts SJ, Hunter AJ, Shah AS, Bosson JA, Unosson J, Barath S, Lundbäck M, Cassee FR, Donaldson K, Sandström T, Blomberg A, Newby DE, Mills NL. 2014. Controlled exposures to air pollutants and risk of cardiac arrhythmia. Environ Health Perspect 122:747–753; http://dx.doi.org/10.1289/ehp.1307337 PMID:24667535

  1. Autonomic neural control of heart rate during dynamic exercise: revisited.

    Science.gov (United States)

    White, Daniel W; Raven, Peter B

    2014-06-15

    The accepted model of autonomic control of heart rate (HR) during dynamic exercise indicates that the initial increase is entirely attributable to the withdrawal of parasympathetic nervous system (PSNS) activity and that subsequent increases in HR are entirely attributable to increases in cardiac sympathetic activity. In the present review, we sought to re-evaluate the model of autonomic neural control of HR in humans during progressive increases in dynamic exercise workload. We analysed data from both new and previously published studies involving baroreflex stimulation and pharmacological blockade of the autonomic nervous system. Results indicate that the PSNS remains functionally active throughout exercise and that increases in HR from rest to maximal exercise result from an increasing workload-related transition from a 4 : 1 vagal-sympathetic balance to a 4 : 1 sympatho-vagal balance. Furthermore, the beat-to-beat autonomic reflex control of HR was found to be dependent on the ability of the PSNS to modulate the HR as it was progressively restrained by increasing workload-related sympathetic nerve activity. (i) increases in exercise workload-related HR are not caused by a total withdrawal of the PSNS followed by an increase in sympathetic tone; (ii) reciprocal antagonism is key to the transition from vagal to sympathetic dominance, and (iii) resetting of the arterial baroreflex causes immediate exercise-onset reflexive increases in HR, which are parasympathetically mediated, followed by slower increases in sympathetic tone as workloads are increased. © 2014 The Authors. The Journal of Physiology © 2014 The Physiological Society.

  2. Decentralized Neural Backstepping Control Applied to a Robot Manipulator

    OpenAIRE

    Ramon Garcia-Hernandez; Ruz-Hernandez, Jose A.; Jose L. Rullan-Lara

    2013-01-01

    This paper presents a discrete‐time decentralized control scheme for trajectory tracking of a two degrees of freedom (DOF) robot manipulator. A high order neural network (HONN) is used to approximate a decentralized control law designed by the backstepping technique as applied to a block strict feedback form (BSFF). The weights for each neural network are adapted online by an extended Kalman filter training algorithm. The motion for each joint is controlled independently using only local angu...

  3. Decoupling Control Method Based on Neural Network for Missiles

    Institute of Scientific and Technical Information of China (English)

    ZHAN Li; LUO Xi-shuang; ZHANG Tian-qiao

    2005-01-01

    In order to make the static state feedback nonlinear decoupling control law for a kind of missile to be easy for implementation in practice, an improvement is discussed. The improvement method is to introduce a BP neural network to approximate the decoupling control laws which are designed for different aerodynamic characteristic points, so a new decoupling control law based on BP neural network is produced after the network training. The simulation results on an example illustrate the approach obtained feasible and effective.

  4. 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.

  5. Implementations of learning control systems using neural networks

    Science.gov (United States)

    Sartori, Michael A.; Antsaklis, Panos J.

    1992-01-01

    The systematic storage in neural networks of prior information to be used in the design of various control subsystems is investigated. Assuming that the prior information is available in a certain form (namely, input/output data points and specifications between the data points), a particular neural network and a corresponding parameter design method are introduced. The proposed neural network addresses the issue of effectively using prior information in the areas of dynamical system (plant and controller) modeling, fault detection and identification, information extraction, and control law scheduling.

  6. Study on Adaptive Control with Neural Network Compensation

    Institute of Scientific and Technical Information of China (English)

    单剑锋; 黄忠华; 崔占忠

    2004-01-01

    A scheme of adaptive control based on a recurrent neural network with a neural network compensation is presented for a class of nonlinear systems with a nonlinear prefix. The recurrent neural network is used to identify the unknown nonlinear part and compensate the difference between the real output and the identified model output. The identified model of the controlled object consists of a linear model and the neural network. The generalized minimum variance control method is used to identify pareters, which can deal with the problem of adaptive control of systems with unknown nonlinear part, which can not be controlled by traditional methods. Simulation results show that this algorithm has higher precision, faster convergent speed.

  7. Optical control of excitation waves in cardiac tissue

    Science.gov (United States)

    Burton, Rebecca A. B.; Klimas, Aleksandra; Ambrosi, Christina M.; Tomek, Jakub; Corbett, Alex; Entcheva, Emilia; Bub, Gil

    2015-12-01

    In nature, macroscopic excitation waves are found in a diverse range of settings including chemical reactions, metal rust, yeast, amoeba and the heart and brain. In the case of living biological tissue, the spatiotemporal patterns formed by these excitation waves are different in healthy and diseased states. Current electrical and pharmacological methods for wave modulation lack the spatiotemporal precision needed to control these patterns. Optical methods have the potential to overcome these limitations, but to date have only been demonstrated in simple systems, such as the Belousov-Zhabotinsky chemical reaction. Here, we combine dye-free optical imaging with optogenetic actuation to achieve dynamic control of cardiac excitation waves. Illumination with patterned light is demonstrated to optically control the direction, speed and spiral chirality of such waves in cardiac tissue. This all-optical approach offers a new experimental platform for the study and control of pattern formation in complex biological excitable systems.

  8. Coordinated infraslow neural and cardiac oscillations mark fragility and offline periods in mammalian sleep.

    Science.gov (United States)

    Lecci, Sandro; Fernandez, Laura M J; Weber, Frederik D; Cardis, Romain; Chatton, Jean-Yves; Born, Jan; Lüthi, Anita

    2017-02-01

    Rodents sleep in bouts lasting minutes; humans sleep for hours. What are the universal needs served by sleep given such variability? In sleeping mice and humans, through monitoring neural and cardiac activity (combined with assessment of arousability and overnight memory consolidation, respectively), we find a previously unrecognized hallmark of sleep that balances two fundamental yet opposing needs: to maintain sensory reactivity to the environment while promoting recovery and memory consolidation. Coordinated 0.02-Hz oscillations of the sleep spindle band, hippocampal ripple activity, and heart rate sequentially divide non-rapid eye movement (non-REM) sleep into offline phases and phases of high susceptibility to external stimulation. A noise stimulus chosen such that sleeping mice woke up or slept through at comparable rates revealed that offline periods correspond to raising, whereas fragility periods correspond to declining portions of the 0.02-Hz oscillation in spindle activity. Oscillations were present throughout non-REM sleep in mice, yet confined to light non-REM sleep (stage 2) in humans. In both species, the 0.02-Hz oscillation predominated over posterior cortex. The strength of the 0.02-Hz oscillation predicted superior memory recall after sleep in a declarative memory task in humans. These oscillations point to a conserved function of mammalian non-REM sleep that cycles between environmental alertness and internal memory processing in 20- to 25-s intervals. Perturbed 0.02-Hz oscillations may cause memory impairment and ill-timed arousals in sleep disorders.

  9. Adaptive artificial neural network for autonomous robot control

    Science.gov (United States)

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

    1992-01-01

    The topics are presented in viewgraph form and include: neural network controller for robot arm positioning with visual feedback; initial training of the arm; automatic recovery from cumulative fault scenarios; and error reduction by iterative fine movements.

  10. 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.

  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 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....

  12. Adaptive Neural Network Based Control of Noncanonical Nonlinear Systems.

    Science.gov (United States)

    Zhang, Yanjun; Tao, Gang; Chen, Mou

    2016-09-01

    This paper presents a new study on the adaptive neural network-based control of a class of noncanonical nonlinear systems with large parametric uncertainties. Unlike commonly studied canonical form nonlinear systems whose neural network approximation system models have explicit relative degree structures, which can directly be used to derive parameterized controllers for adaptation, noncanonical form nonlinear systems usually do not have explicit relative degrees, and thus their approximation system models are also in noncanonical forms. It is well-known that the adaptive control of noncanonical form nonlinear systems involves the parameterization of system dynamics. As demonstrated in this paper, it is also the case for noncanonical neural network approximation system models. Effective control of such systems is an open research problem, especially in the presence of uncertain parameters. This paper shows that it is necessary to reparameterize such neural network system models for adaptive control design, and that such reparameterization can be realized using a relative degree formulation, a concept yet to be studied for general neural network system models. This paper then derives the parameterized controllers that guarantee closed-loop stability and asymptotic output tracking for noncanonical form neural network system models. An illustrative example is presented with the simulation results to demonstrate the control design procedure, and to verify the effectiveness of such a new design method.

  13. 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.

  14. 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.

  15. 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

    —This paper describes reactive neural control used to generate phototaxis and obstacle avoidance behavior of walking machines. It utilizes discrete-time neurodynamics and consists of two main neural modules: neural preprocessing and modular neural control. The neural preprocessing network acts...

  16. Automatic coronary artery calcium scoring in cardiac CT angiography using paired convolutional neural networks.

    Science.gov (United States)

    Wolterink, Jelmer M; Leiner, Tim; de Vos, Bob D; van Hamersvelt, Robbert W; Viergever, Max A; Išgum, Ivana

    2016-12-01

    The amount of coronary artery calcification (CAC) is a strong and independent predictor of cardiovascular events. CAC is clinically quantified in cardiac calcium scoring CT (CSCT), but it has been shown that cardiac CT angiography (CCTA) may also be used for this purpose. We present a method for automatic CAC quantification in CCTA. This method uses supervised learning to directly identify and quantify CAC without a need for coronary artery extraction commonly used in existing methods. The study included cardiac CT exams of 250 patients for whom both a CCTA and a CSCT scan were available. To restrict the volume-of-interest for analysis, a bounding box around the heart is automatically determined. The bounding box detection algorithm employs a combination of three ConvNets, where each detects the heart in a different orthogonal plane (axial, sagittal, coronal). These ConvNets were trained using 50 cardiac CT exams. In the remaining 200 exams, a reference standard for CAC was defined in CSCT and CCTA. Out of these, 100 CCTA scans were used for training, and the remaining 100 for evaluation of a voxel classification method for CAC identification. The method uses ConvPairs, pairs of convolutional neural networks (ConvNets). The first ConvNet in a pair identifies voxels likely to be CAC, thereby discarding the majority of non-CAC-like voxels such as lung and fatty tissue. The identified CAC-like voxels are further classified by the second ConvNet in the pair, which distinguishes between CAC and CAC-like negatives. Given the different task of each ConvNet, they share their architecture, but not their weights. Input patches are either 2.5D or 3D. The ConvNets are purely convolutional, i.e. no pooling layers are present and fully connected layers are implemented as convolutions, thereby allowing efficient voxel classification. The performance of individual 2.5D and 3D ConvPairs with input sizes of 15 and 25 voxels, as well as the performance of ensembles of these Conv

  17. Knowledge, attitudes and metabolic control of diabetic and cardiac patients

    Directory of Open Access Journals (Sweden)

    Bruna Emy Ono

    2016-01-01

    Full Text Available Objective: to verify the relationship between knowledge, attitudes and metabolic control in diabetic and cardiac patients. Methods: descriptive, exploratory and cross-sectional study exploring the knowledge, attitudes and diabetes metabolic control in 46 participants with heart disease. Results: participants were predominantly male with incomplete secondary education who demonstrated poor knowledge and unfavorable attitudes towards the disease. There was no difference between participants with and without knowledge on variables of metabolic and clinical control of diabetes, neither with respect to attitudes towards the disease. Conclusion: knowledge about diabetes was unsatisfactory in patients with heart disease and unrelated to favorable actions and better disease control.

  18. Neural network-based nonlinear model predictive control vs. linear quadratic gaussian control

    Science.gov (United States)

    Cho, C.; Vance, R.; Mardi, N.; Qian, Z.; Prisbrey, K.

    1997-01-01

    One problem with the application of neural networks to the multivariable control of mineral and extractive processes is determining whether and how to use them. The objective of this investigation was to compare neural network control to more conventional strategies and to determine if there are any advantages in using neural network control in terms of set-point tracking, rise time, settling time, disturbance rejection and other criteria. The procedure involved developing neural network controllers using both historical plant data and simulation models. Various control patterns were tried, including both inverse and direct neural network plant models. These were compared to state space controllers that are, by nature, linear. For grinding and leaching circuits, a nonlinear neural network-based model predictive control strategy was superior to a state space-based linear quadratic gaussian controller. The investigation pointed out the importance of incorporating state space into neural networks by making them recurrent, i.e., feeding certain output state variables into input nodes in the neural network. It was concluded that neural network controllers can have better disturbance rejection, set-point tracking, rise time, settling time and lower set-point overshoot, and it was also concluded that neural network controllers can be more reliable and easy to implement in complex, multivariable plants.

  19. Controlling the contractile strength of engineered cardiac muscle by hierarchal tissue architecture.

    Science.gov (United States)

    Feinberg, Adam W; Alford, Patrick W; Jin, Hongwei; Ripplinger, Crystal M; Werdich, Andreas A; Sheehy, Sean P; Grosberg, Anna; Parker, Kevin Kit

    2012-08-01

    The heart is a muscular organ with a wrapping, laminar structure embedded with neural and vascular networks, collagen fibrils, fibroblasts, and cardiac myocytes that facilitate contraction. We hypothesized that these non-muscle components may have functional benefit, serving as important structural alignment cues in inter- and intra-cellular organization of cardiac myocytes. Previous studies have demonstrated that alignment of engineered myocardium enhances calcium handling, but how this impacts actual force generation remains unclear. Quantitative assays are needed to determine the effect of alignment on contractile function and muscle physiology. To test this, micropatterned surfaces were used to build 2-dimensional myocardium from neonatal rat ventricular myocytes with distinct architectures: confluent isotropic (serving as the unaligned control), confluent anisotropic, and 20 μm spaced, parallel arrays of multicellular myocardial fibers. We combined image analysis of sarcomere orientation with muscular thin film contractile force assays in order to calculate the peak sarcomere-generated stress as a function of tissue architecture. Here we report that increasing peak systolic stress in engineered cardiac tissues corresponds with increasing sarcomere alignment. This change is larger than would be anticipated from enhanced calcium handling and increased uniaxial alignment alone. These results suggest that boundary conditions (heterogeneities) encoded in the extracellular space can regulate muscle tissue function, and that structural organization and cytoskeletal alignment are critically important for maximizing peak force generation.

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

    DEFF Research Database (Denmark)

    Sørensen, O.

    1997-01-01

    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...... study of the networks themselves. With this end in view the following restrictions have been made: 1) Amongst numerous neural network structures, only the Multi Layer Perceptron (a feed-forward network) is applied. 2) Amongst numerous training algorithms, only the Recursive Prediction Error Method using...... 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...

  1. 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.

  2. A rule-based neural controller for inverted pendulum system.

    Science.gov (United States)

    Hao, J; Vandewalle, J; Tan, S

    1993-03-01

    This paper tries to demonstrate how a heuristic neural control approach can be used to solve a complex nonlinear control problem. The control task is to swing up a pendulum mounted on a cart from its stable position (vertically down) to the zero state (up right) and keep it there by applying a sequence of two opposing constant forces of equal magnitude to the mass center of the cart. In addition, the displacement of the cart itself is confined to within a preset limit during the swinging up action and it will eventually be brought to the origin of the track. This is truly a nontrivial nonlinear regulation problem and is considerably difficult compared to the pendulum balancing problem (and its variations) widely adopted as a benchmarking test system for neural controllers. Through the solution of this specific control problem, we try to illustrate a heuristic neural control approach with task decomposition, control rule extraction and neural net rule implementation as its basic elements. Specializing to the pendulum problem, the global control task is decomposed into subtasks namely pendulum positioning and cart positioning. Accordingly, three separate neural subcontrollers are designed to cater to the subtasks and their coordination, i.e., pendulum subcontroller (PSC), cart subcontroller (CSC) and the switching subcontroller (SSC). Each of the subcontrollers is designed based on the rules and guidelines obtained from the experiences of a human operator. The simulation result is included to show the actual performance of the controller.

  3. 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.

  4. Adaptive Control for Robotic Manipulators Base on RBF Neural Network

    Directory of Open Access Journals (Sweden)

    MA Jing

    2013-09-01

    Full Text Available An adaptive neural network controller is brought forward by the paper to solve trajectory tracking problems of robotic manipulators with uncertainties. The first scheme consists of a PD feedback and a dynamic compensator which is composed by neural network controller and variable structure controller. Neutral network controller is designed to adaptive learn and compensate the unknown uncertainties, variable structure controller is designed to eliminate approach errors of neutral network. The adaptive weight learning algorithm of neural network is designed to ensure online real-time adjustment, offline learning phase is not need; Global asymptotic stability (GAS of system base on Lyapunov theory is analysised to ensure the convergence of the algorithm. The simulation result s show that the kind of the control scheme is effective and has good robustness.

  5. Adaptive Control of Flexible Redundant Manipulators Using Neural Networks

    Institute of Scientific and Technical Information of China (English)

    SONG Yimin; LI Jianxin; WANG Shiyu; LIU Jianping

    2006-01-01

    An investigation on the neural networks based active vibration control of flexible redundant manipulators was conducted.The smart links of the manipulator were synthesized with the flexible links to which were attached piezoceramic actuators and strain gauge sensors.A nonlinear adaptive control strategy named neural networks based indirect adaptive control (NNIAC) was employed to improve the dynamic performance of the manipulator.The mathematical model of the 4-layered dynamic recurrent neural networks (DRNN) was introduced.The neuro-identifier and the neurocontroller featuring the DRNN topology were designed off line so as to enhance the initial robustness of the NNIAC.By adjusting the neuro-identifier and the neuro-controller alternatively,the manipulator was controlled on line for achieving the desired dynamic performance.Finally,a planar 3R redundant manipulator with one smart link was utilized as an illustrative example.The simulation results proved the validity of the control strategy.

  6. Payload Invariant Control via Neural Networks: Development and Experimental Evaluation

    Science.gov (United States)

    1989-12-01

    Pendulum ........ ....................... A-3 A.2. An Inverted Pendulum Control System ...... .............. A-4 ix Figure Page A.3. An ADALINE ...Tolat and Widrow use a computer simulation for the inverted pendulum problem. One Adaptive Linear Neuron ( ADALINE , see Figure A.3) is used for the neural... ADALINE [79] MURPHY’s basic components are four interconnected CMAC’ type neural networks, an autofocus camera, and a Rhino XR-3 manipulator [54

  7. Feed Forward Neural Network and Optimal Control Problem with Control and State Constraints

    Science.gov (United States)

    Kmet', Tibor; Kmet'ová, Mária

    2009-09-01

    A feed forward neural network based optimal control synthesis is presented for solving optimal control problems with control and state constraints. The paper extends adaptive critic neural network architecture proposed by [5] to the optimal control problems with control and state constraints. The optimal control problem is transcribed into a nonlinear programming problem which is implemented with adaptive critic neural network. The proposed simulation method is illustrated by the optimal control problem of nitrogen transformation cycle model. Results show that adaptive critic based systematic approach holds promise for obtaining the optimal control with control and state constraints.

  8. Robust Adaptive Neural Control of Morphing Aircraft with Prescribed Performance

    Directory of Open Access Journals (Sweden)

    Zhonghua Wu

    2017-01-01

    Full Text Available This study proposes a low-computational composite adaptive neural control scheme for the longitudinal dynamics of a swept-back wing aircraft subject to parameter uncertainties. To efficiently release the constraint often existing in conventional neural designs, whose closed-loop stability analysis always necessitates that neural networks (NNs be confined in the active regions, a smooth switching function is presented to conquer this issue. By integrating minimal learning parameter (MLP technique, prescribed performance control, and a kind of smooth switching strategy into back-stepping design, a new composite switching adaptive neural prescribed performance control scheme is proposed and a new type of adaptive laws is constructed for the altitude subsystem. Compared with previous neural control scheme for flight vehicle, the remarkable feature is that the proposed controller not only achieves the prescribed performance including transient and steady property but also addresses the constraint on NN. Two comparative simulations are presented to verify the effectiveness of the proposed controller.

  9. Neural Network-Based Active Control for Offshore Platforms

    Institute of Scientific and Technical Information of China (English)

    周亚军; 赵德有

    2003-01-01

    A new active control scheme, based on neural network, for the suppression of oscillation in multiple-degree-of-freedom (MDOF) offshore platforms, is studied in this paper. With the main advantages of neural network, i.e. the inherent robustness, fault tolerance, and generalized capability of its parallel massive interconnection structure, the active structural control of offshore platforms under random waves is accomplished by use of the BP neural network model. The neural network is trained offline with the data generated from numerical analysis, and it simulates the process of Classical Linear Quadratic Regular Control for the platform under random waves. After the learning phase, the trained network has learned about the nonlinear dynamic behavior of the active control system, and is capable of predicting the active control forces of the next time steps. The results obtained show that the active control is feasible and effective, and it finally overcomes time delay owing to the robustness, fault tolerance, and generalized capability of artificial neural network.

  10. Synthetical Control of AGC/LPC System Based on Neural Networks Internal Model Control

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    One synthetical control method of AGC/LPC system based on intelligence control theory-neural networks internal model control method is presented. Genetic algorithm (GA) is applied to optimize the parameters of the neural networks. Simulation results prove that this method is effective.

  11. 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.

  12. Practical Application of Neural Networks in State Space Control

    DEFF Research Database (Denmark)

    Bendtsen, Jan Dimon

    In the present thesis we address some problems in discrete-time state space control of nonlinear dynamical systems and attempt to solve them using generic nonlinear models based on artificial neural networks. The main aim of the work is to examine how well such control algorithms perform when...... applied to a realistic process. The thesis therefore strives to provide a thorough treatment of two classes of neural network-based controllers, and to make a rigorous comparison between them and a classical linear controller. Thus, the thesis starts out with a short review of some relevant system...... 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...

  13. A lightweight feedback-controlled microdrive for chronic neural recordings

    Science.gov (United States)

    Jovalekic, A.; Cavé-Lopez, S.; Canopoli, A.; Ondracek, J. M.; Nager, A.; Vyssotski, A. L.; Hahnloser, R. H. R.

    2017-04-01

    Objective. Chronic neural recordings have provided many insights into the relationship between neural activity and behavior. We set out to develop a miniaturized motorized microdrive that allows precise electrode positioning despite possibly unreliable motors. Approach. We designed a feedback-based motor control mechanism. It contains an integrated position readout from an array of magnets and a Hall sensor. Main results. Our extremely lightweight (feedback-based microdrive control requires little extra size and weight, suggesting that such control can be incorporated into more complex multi-electrode designs.

  14. 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.

  15. Model predictive combustion control based on neural nets

    Energy Technology Data Exchange (ETDEWEB)

    Schmidt, D. [Powitec Intelligent Technologies GmbH, Essen (Germany); Kampschreuer, T. [AVR Afvalverwerking B.V., Duiven/Arnheim (Netherlands)

    2008-07-01

    The first closed-loop Neural Net combustion controller in the Netherlands has been installed at the HVC plant in Alkmaar. During the summer 2006 the first of the 'old' three lines was equipped with an individually controllable primary air distribution. As 'fire controller' the combustion optimiser from Powitec, the PiT Navigator, was selected, a system using digital image processing and neural nets. This paper shows the results from operating the plant with and without the NMPC optimiser and from the performance tests. (orig.)

  16. Biologically inspired neural network controller for an infrared tracking system

    Science.gov (United States)

    Frigo, Janette R.; Tilden, Mark W.

    1999-01-01

    Many biological system exhibit capable, adaptive behavior with a minimal nervous system such as those found in lower invertebrates. Scientists and engineers are studying biological system because these models may have real-world applications. the analog neural controller, herein, is loosely modeled after minimal biological nervous systems. The system consists of the controller and pair of sensor mounted on an actuator. It is implemented with an electrical oscillator network, two IR sensor and a dc motor, used as an actuator for the system. The system tracks an IR target source. The pointing accuracy of this neural network controller is estimated through experimental measurements and a numerical model of the system.

  17. Neural network based dynamic controllers for industrial robots.

    Science.gov (United States)

    Oh, S Y; Shin, W C; Kim, H G

    1995-09-01

    The industrial robot's dynamic performance is frequently measured by positioning accuracy at high speeds and a good dynamic controller is essential that can accurately compute robot dynamics at a servo rate high enough to ensure system stability. A real-time dynamic controller for an industrial robot is developed here using neural networks. First, an efficient time-selectable hidden layer architecture has been developed based on system dynamics localized in time, which lends itself to real-time learning and control along with enhanced mapping accuracy. Second, the neural network architecture has also been specially tuned to accommodate servo dynamics. This not only facilitates the system design through reduced sensing requirements for the controller but also enhances the control performance over the control architecture neglecting servo dynamics. Experimental results demonstrate the controller's excellent learning and control performances compared with a conventional controller and thus has good potential for practical use in industrial robots.

  18. Neural Network Approach to Railway Stand Lateral SKEW Control

    Directory of Open Access Journals (Sweden)

    Peter Mark Benes

    2014-02-01

    Full Text Available The paper presents a study of an adaptive approach to lateral skew co ntrol for an experimental railway stand. The preliminary experiments with the real ex perimental railway stand and simulations with its 3-D mechanical model, indicates difficulties of model-based control of the device. Thus, use of neural networks for identification and c ontrol of lateral skew shall be investigated. This paper focuses on real-data based modelling of the railway stand by various neural network models, i.e; linear neural unit and quadratic neur al unit architectures. Furthermore, training methods of these neural architecture s as such, real-time-recurrent- learning and a variation of back-propagation-through-time are exam ined, accompanied by a discussion of the produced experimental results

  19. Neural Network Predictive Control Based Power System Stabilizer

    Directory of Open Access Journals (Sweden)

    Ali Mohamed Yousef

    2012-04-01

    Full Text Available The present study investigates the power system stabilizer based on neural predictive control for improving power system dynamic performance over a wide range of operating conditions. In this study a design and application of the Neural Network Model Predictive Controller (NN-MPC on a simple power system composed of a synchronous generator connected to an infinite bus through a transmission line is proposed. The synchronous machine is represented in detail, taking into account the effect of the machine saliency and the damper winding. Neural network model predictive control combines reliable prediction of neural network model with excellent performance of model predictive control using nonlinear Levenberg-Marquardt optimization. This control system is used the rotor speed deviation as a feedback signal. Furthermore, the using performance system of the proposed controller is compared with the system performance using conventional one (PID controller through simulation studies. Digital simulation has been carried out in order to validate the effectiveness proposed NN-MPC power system stabilizer for achieving excellent performance. The results demonstrate that the effectiveness and superiority of the proposed controller in terms of fast response and small settling time.

  20. Active Control of Sound based on Diagonal Recurrent Neural Network

    NARCIS (Netherlands)

    Jayawardhana, Bayu; Xie, Lihua; Yuan, Shuqing

    2002-01-01

    Recurrent neural network has been known for its dynamic mapping and better suited for nonlinear dynamical system. Nonlinear controller may be needed in cases where the actuators exhibit the nonlinear characteristics, or in cases when the structure to be controlled exhibits nonlinear behavior. The fe

  1. Active Control of Sound based on Diagonal Recurrent Neural Network

    NARCIS (Netherlands)

    Jayawardhana, Bayu; Xie, Lihua; Yuan, Shuqing

    2002-01-01

    Recurrent neural network has been known for its dynamic mapping and better suited for nonlinear dynamical system. Nonlinear controller may be needed in cases where the actuators exhibit the nonlinear characteristics, or in cases when the structure to be controlled exhibits nonlinear behavior. The fe

  2. neural network based load frequency control for restructuring power ...

    African Journals Online (AJOL)

    2012-03-01

    Mar 1, 2012 ... the system in the back propagation chain used in controller training. For this ... power systems. Different types of controllers ... role to allow power exchanges and to supply bet- ter conditions for the ... To solve all these problems in the above men- ..... term load forecasting using generalized neural network ...

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

    Institute of Scientific and Technical Information of China (English)

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

    2005-01-01

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

  4. The other side of cardiac Ca2+ signaling: transcriptional control

    Directory of Open Access Journals (Sweden)

    Alejandro eDomínguez-Rodríquez

    2012-11-01

    Full Text Available Ca2+ is probably the most versatile signal transduction element used by all cell types. In the heart, it is essential to activate cellular contraction in each heartbeat. Nevertheless Ca2+ is not only a key element in excitation-contraction coupling (EC coupling, but it is also a pivotal second messenger in cardiac signal transduction, being able to control processes such as excitability, metabolism, and transcriptional regulation. Regarding the latter, Ca2+ activates Ca2+-dependent transcription factors by a process called excitation-transcription coupling (ET coupling. ET coupling is an integrated process by which the common signaling pathways that regulate EC coupling activate transcription factors. Although ET coupling has been extensively studied in neurons and other cell types, less is known in cardiac muscle. Some hints have been found in studies on the development of cardiac hypertrophy, where two Ca2+-dependent enzymes are key actors: Ca2+/Calmodulin kinase II (CaMKII and phosphatase calcineurin, both of which are activated by the complex Ca2+/ /Calmodulin. The question now is how ET coupling occurs in cardiomyocytes, where intracellular Ca2+ is continuously oscillating. In this focused review, we will draw attention to location of Ca2+ signaling: intranuclear ([Ca2+]n or cytoplasmic ([Ca2+]c, and the specific ionic channels involved in the activation of cardiac ET coupling. Specifically, we will highlight the role of the 1,4,5 inositol triphosphate receptors (IP3Rs in the elevation of [Ca2+]n levels, which are important to locally activate CaMKII, and the role of transient receptor potential channels canonical (TRPCs in [Ca2+]c, needed to activate calcineurin.

  5. Active Noise Feedback Control Using a Neural Network

    OpenAIRE

    Zhang Qizhi; Jia Yongle

    2001-01-01

    The active noise control (ANC) is discussed. Many digital ANC systems often based on the filter-x algorithm for finite impulse response (FIR) filter use adaptive filtering techniques. But if the primary noise path is nonlinear, the control system based on adaptive filter technology will be invalid. In this paper, an adaptive active nonlinear noise feedback control approach using a neural network is derived. The feedback control system drives a secondary signal to destructively interfere with ...

  6. CONTROL SCHEMES FOR CMAC NEURAL NETWORK-BASED VISUAL SERVOING

    Institute of Scientific and Technical Information of China (English)

    Wang Huaming; Xi Wenming; Zhu Jianying

    2003-01-01

    In IBVS (image based visual servoing), the error signal in image space should be transformed into the control signal in the input space quickly. To avoid the iterative adjustment and complicated inverse solution of image Jacobian, CMAC (cerebellar model articulation controller) neural network is inserted into visual servo control loop to implement the nonlinear mapping. Two control schemes are used. Simulation results on two schemes are provided, which show a better tracking precision and stability can be achieved using scheme 2.

  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. Neural network payload estimation for adaptive robot control.

    Science.gov (United States)

    Leahy, M R; Johnson, M A; Rogers, S K

    1991-01-01

    A concept is proposed for utilizing artificial neural networks to enhance the high-speed tracking accuracy of robotic manipulators. Tracking accuracy is a function of the controller's ability to compensate for disturbances produced by dynamical interactions between the links. A model-based control algorithm uses a nominal model of those dynamical interactions to reduce the disturbances. The problem is how to provide accurate dynamics information to the controller in the presence of payload uncertainty and modeling error. Neural network payload estimation uses a series of artificial neural networks to recognize the payload variation associated with a degradation in tracking performance. The network outputs are combined with a knowledge of nominal dynamics to produce a computationally efficient direct form of adaptive control. The concept is validated through experimentation and analysis on the first three links of a PUMA-560 manipulator. A multilayer perceptron architecture with two hidden layers is used. Integration of the principles of neural network pattern recognition and model-based control produces a tracking algorithm with enhanced robustness to incomplete dynamic information. Tracking efficacy and applicability to robust control algorithms are discussed.

  9. Automatic coronary calcium scoring in cardiac CT angiography using convolutional neural networks

    NARCIS (Netherlands)

    Wolterink, Jelmer M.; Leiner, Tim; Viergever, Max A.; Isgum, I

    2015-01-01

    The amount of coronary artery calcification (CAC) is a strong and independent predictor of cardiovascular events. Non-contrast enhanced cardiac CT is considered a reference for quantification of CAC. Recently, it has been shown that CAC may be quantified in cardiac CT angiography (CCTA). We present

  10. Ideomotor feedback control in a recurrent neural network.

    Science.gov (United States)

    Galtier, Mathieu

    2015-06-01

    The architecture of a neural network controlling an unknown environment is presented. It is based on a randomly connected recurrent neural network from which both perception and action are simultaneously read and fed back. There are two concurrent learning rules implementing a sort of ideomotor control: (i) perception is learned along the principle that the network should predict reliably its incoming stimuli; (ii) action is learned along the principle that the prediction of the network should match a target time series. The coherent behavior of the neural network in its environment is a consequence of the interaction between the two principles. Numerical simulations show a promising performance of the approach, which can be turned into a local and better "biologically plausible" algorithm.

  11. A retinoic acid responsive Hoxa3 transgene expressed in embryonic pharyngeal endoderm, cardiac neural crest and a subdomain of the second heart field.

    Directory of Open Access Journals (Sweden)

    Nata Y S-G Diman

    Full Text Available A transgenic mouse line harbouring a β-galacdosidase reporter gene controlled by the proximal 2 kb promoter of Hoxa3 was previously generated to investigate the regulatory cues governing Hoxa3 expression in the mouse. Examination of transgenic embryos from embryonic day (E 8.0 to E15.5 revealed regionally restricted reporter activity in the developing heart. Indeed, transgene expression specifically delineated cells from three distinct lineages: a subpopulation of the second heart field contributing to outflow tract myocardium, the cardiac neural crest cells and the pharyngeal endoderm. Manipulation of the Retinoic Acid (RA signaling pathway showed that RA is required for correct expression of the transgene. Therefore, this transgenic line may serve as a cardiosensor line of particular interest for further analysis of outflow tract development.

  12. A Retinoic Acid Responsive Hoxa3 Transgene Expressed in Embryonic Pharyngeal Endoderm, Cardiac Neural Crest and a Subdomain of the Second Heart Field

    Science.gov (United States)

    Diman, Nata Y. S.-G.; Remacle, Sophie; Bertrand, Nicolas; Picard, Jacques J.; Zaffran, Stéphane; Rezsohazy, René

    2011-01-01

    A transgenic mouse line harbouring a β-galacdosidase reporter gene controlled by the proximal 2 kb promoter of Hoxa3 was previously generated to investigate the regulatory cues governing Hoxa3 expression in the mouse. Examination of transgenic embryos from embryonic day (E) 8.0 to E15.5 revealed regionally restricted reporter activity in the developing heart. Indeed, transgene expression specifically delineated cells from three distinct lineages: a subpopulation of the second heart field contributing to outflow tract myocardium, the cardiac neural crest cells and the pharyngeal endoderm. Manipulation of the Retinoic Acid (RA) signaling pathway showed that RA is required for correct expression of the transgene. Therefore, this transgenic line may serve as a cardiosensor line of particular interest for further analysis of outflow tract development. PMID:22110697

  13. Active Noise Feedback Control Using a Neural Network

    Directory of Open Access Journals (Sweden)

    Zhang Qizhi

    2001-01-01

    Full Text Available The active noise control (ANC is discussed. Many digital ANC systems often based on the filter-x algorithm for finite impulse response (FIR filter use adaptive filtering techniques. But if the primary noise path is nonlinear, the control system based on adaptive filter technology will be invalid. In this paper, an adaptive active nonlinear noise feedback control approach using a neural network is derived. The feedback control system drives a secondary signal to destructively interfere with the original noise to cut down the noise power. An on-line learning algorithm based on the error gradient descent method was proposed, and the local stability of closed loop system is proved using the discrete Lyapunov function. A nonlinear simulation example shows that the adaptive active noise feedback control method based on a neural network is very effective to the nonlinear noise control.

  14. Fuzzy-Neural Control of Hot-Rolling Mill

    Directory of Open Access Journals (Sweden)

    Khearia Mohamad

    2010-12-01

    Full Text Available This paper deals with the application of Fuzzy-Neural Networks (FNNs in multi-machine system control applied on hot steel rolling. The electrical drives that used in rolling system are a set of three-phase induction motors (IM controlled by indirect field-oriented control (IFO. The fundamental goal of this type of control is to eliminate the coupling influence though the coordinate transformation in order to make the AC motor behaves like a separately excited DC motor. Then use Fuzzy-Neural Network in control the IM speed and the rolling plant. In this work MATLAB/SIMULINK models are proposed and implemented for the entire structures. Simulation results are presented to verify the effectiveness of the proposed control schemes. It is found that the proposed system is robust in that it eliminates the disturbances considerably.

  15. Design of a multivariable neural controller for control of a nonlinear MIMO plant

    Directory of Open Access Journals (Sweden)

    Bańka Stanisław

    2014-06-01

    Full Text Available The paper presents the training problem of a set of neural nets to obtain a (gain-scheduling, adaptive multivariable neural controller for control of a nonlinear MIMO dynamic process represented by a mathematical model of Low-Frequency (LF motions of a drillship over the drilling point at the sea bottom. The designed neural controller contains a set of neural nets that determine values of its parameters chosen on the basis of two measured auxiliary signals. These are the ship’s current forward speed measured with respect to water and the systematically calculated difference between the course angle and the sea current (yaw angle. Four different methods for synthesis of multivariable modal controllers are used to obtain source data for training the neural controller with parameters reproduced by neural networks. Neural networks are designed on the basis of 3650 modal controllers obtained with the use of the pole placement technique after having linearized the model of LF motions made by the vessel at its nominal operating points in steady states that are dependent on the specified yaw angle and the sea current velocity. The final part of the paper includes simulation results of system operation with a neural controller along with conclusions and final remarks.

  16. Adaptive control of system with hysteresis using neural networks

    Institute of Scientific and Technical Information of China (English)

    Li Chuntao; Tan Yonghong

    2006-01-01

    An adaptive control scheme is developed for a class of single-input nonlinear systems preceded by unknown hysteresis, which is a non-differentiable and multi-value mapping nonlinearity. The controller based on the three-layer neural network (NN), whose weights are derived from Lyapunov stability analysis, guarantees closed-loop semiglobal stability and convergence of the tracking errors to a small residual set. An example is used to confirm the effectiveness of the proposed control scheme.

  17. 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.

  18. Perception Neural Networks for Active Noise Control Systems

    Directory of Open Access Journals (Sweden)

    Wang Xiaoli

    2012-11-01

    Full Text Available In a response to a growing demand for environments of 70dB or less noise levels, many industrial sectors have focused with some form of noise control system. Active noise control (ANC has proven to be the most effective technology. This paper mainly investigates application of neural network on self-adaptation system in active noise control (ANC. An active silencing control system is made which adopts a motional feedback loudspeaker as not a noise controlling source but a detecting sensor. The working fundamentals and the characteristics of the motional feedback loudspeaker are analyzed in detail. By analyzing each acoustical path, identification based adaptive linear neural network is built. This kind of identifying method can be achieved conveniently. The estimated result of each sound channel matches well with its real sound character, respectively.

  19. Terminal Sliding Mode Control Using Adaptive Fuzzy-Neural Observer

    Directory of Open Access Journals (Sweden)

    Dezhi Xu

    2013-01-01

    Full Text Available We propose a terminal sliding mode control (SMC law based on adaptive fuzzy-neural observer for nonaffine nonlinear uncertain system. First, a novel nonaffine nonlinear approximation algorithm is proposed for observer and controller design. Then, an adaptive fuzzy-neural observer is introduced to identify the simplified model and resolve the problem of the unavailability of the state variables. Moreover, based on the information of the adaptive observer, the terminal SMC law is designed. The Lyapunov synthesis approach is used to guarantee a global uniform ultimate boundedness property of the state estimation error and the asymptotic output tracking of the closed-loop control systems in spite of unknown uncertainties/disturbances, as well as all the other signals in the closed-loop system. Finally, using the designed terminal sliding mode controller, the simulation results on the dynamic model demonstrate the effectiveness of the proposed new control techniques.

  20. Modelling and control PEMFC using fuzzy neural networks

    Institute of Scientific and Technical Information of China (English)

    2005-01-01

    Proton exchange membrane generation technology is highly efficient, clean and considered as the most hopeful "green" power technology. The operating principles of proton exchange membrane fuel cell (PEMFC) system involve thermodynamics, electrochemistry, hydrodynamics and mass transfer theory, which comprise a complex nonlinear system, for which it is difficult to establish a mathematical model and control online. This paper first simply analyzes the characters of the PEMFC; and then uses the approach and self-study ability of artificial neural networks to build the model of the nonlinear system, and uses the adaptive neural-networks fuzzy infer system (ANFIS) to build the temperature model of PEMFC which is used as the reference model of the control system, and adjusts the model parameters to control it online. The model and control are implemented in SIMULINK environment. Simulation results showed that the test data and model agreed well, so it will be very useful for optimal and real-time control of PEMFC system.

  1. Neural network learning of optimal Kalman prediction and control

    CERN Document Server

    Linsker, Ralph

    2008-01-01

    Although there are many neural network (NN) algorithms for prediction and for control, and although methods for optimal estimation (including filtering and prediction) and for optimal control in linear systems were provided by Kalman in 1960 (with nonlinear extensions since then), there has been, to my knowledge, no NN algorithm that learns either Kalman prediction or Kalman control (apart from the special case of stationary control). Here we show how optimal Kalman prediction and control (KPC), as well as system identification, can be learned and executed by a recurrent neural network composed of linear-response nodes, using as input only a stream of noisy measurement data. The requirements of KPC appear to impose significant constraints on the allowed NN circuitry and signal flows. The NN architecture implied by these constraints bears certain resemblances to the local-circuit architecture of mammalian cerebral cortex. We discuss these resemblances, as well as caveats that limit our current ability to draw ...

  2. Adaptive control of mobile robots using a neural network.

    Science.gov (United States)

    de Sousa Júnior, C; Hermerly, E M

    2001-06-01

    A Neural Network - based control approach for mobile robot is proposed. The weight adaptation is made on-line, without previous learning. Several possible situations in robot navigation are considered, including uncertainties in the model and presence of disturbance. Weight adaptation laws are presented as well as simulation results.

  3. 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...

  4. Defining a neural network controller structure for a rubbertuator robot.

    Science.gov (United States)

    Ozkan, M; Inoue, K; Negishi, K; Yamanaka, T

    2000-01-01

    Rubbertuator (Rubber-Actuator) robot arm is a pneumatic robot, unique with its lightweight, high power, compliant and spark free nature. Compressibility of air in the actuator tubes and the elastic nature of the rubber, however, are the two major sources of increased non-linearity and complexity in motion control. Soft computing, exploiting the tolerance of uncertainty and vagueness in cognitive reasoning has been offering easy to handle, robust, and low-priced solutions to several non-linear industrial applications. Nonetheless, the black-box approach in these systems results in application specific architectures with some important design parameters left for fine tuning (i.e. number of nodes in a neural network). In this study we propose a more systematic method in defining the structure of a soft computing technique, namely the backpropagation neural network, when used as a controller for rubbertuator robot systems. The structure of the neural network is based on the physical model of the robot, while the neural network itself is trained to learn the trajectory independent parameters of the model that are essential for defining the robot dynamics. The proposed system performance was compared with a well-tuned PID controller and shown to be more accurate in trajectory control for rubbertuator robots.

  5. Robust Neural Sliding Mode Control of Robot Manipulators

    Science.gov (United States)

    Hiep, Nguyen Tran; cat, Pham Thuong

    2009-03-01

    This paper proposes a robust neural sliding mode control method for robot tracking problem to overcome the noises and large uncertainties in robot dynamics. The Lyapunov direct method has been used to prove the stability of the overall system. Simulation results are given to illustrate the applicability of the proposed method

  6. Active control of vibration using a neural network.

    Science.gov (United States)

    Snyder, S D; Tanaka, N

    1995-01-01

    Feedforward control of sound and vibration using a neural network-based control system is considered, with the aim being to derive an architecture/algorithm combination which is capable of supplanting the commonly used finite impulse response filter/filtered-x least mean square (LMS) linear arrangement for certain nonlinear problems. An adaptive algorithm is derived which enables stable adaptation of the neural controller for this purpose, while providing the capacity to maintain causality within the control scheme. The algorithm is shown to be simply a generalization of the linear filtered-x LMS algorithm. Experiments are undertaken which demonstrate the utility of the proposed arrangement, showing that it performs as well as a linear control system for a linear control problem and better for a nonlinear control problem. The experiments also lead to the conclusion that more work is required to improve the predictability and consistency of the performance before the neural network controller becomes a practical alternative to the current linear feedforward systems.

  7. Manipulator Neural Network Control Based on Fuzzy Genetic Algorithm

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    The three-layer forward neural networks are used to establish the inverse kinem a tics models of robot manipulators. The fuzzy genetic algorithm based on the line ar scaling of the fitness value is presented to update the weights of neural net works. To increase the search speed of the algorithm, the crossover probability and the mutation probability are adjusted through fuzzy control and the fitness is modified by the linear scaling method in FGA. Simulations show that the propo sed method improves considerably the precision of the inverse kinematics solutio ns for robot manipulators and guarantees a rapid global convergence and overcome s the drawbacks of SGA and the BP algorithm.

  8. Neural Network Model Based Cluster Head Selection for Power Control

    Directory of Open Access Journals (Sweden)

    Krishan Kumar

    2011-01-01

    Full Text Available Mobile ad-hoc network has challenge of the limited power to prolong the lifetime of the network, because power is a valuable resource in mobile ad-hoc network. The status of power consumption should be continuously monitored after network deployment. In this paper, we propose coverage aware neural network based power control routing with the objective of maximizing the network lifetime. Cluster head selection is proposed using adaptive learning in neural networks followed by coverage. The simulation results show that the proposed scheme can be used in wide area of applications in mobile ad-hoc network.

  9. Controlled exposures to air pollutants and risk of cardiac arrhythmia

    NARCIS (Netherlands)

    Langrish, Jeremy P; Watts, Simon J; Hunter, Amanda J; Shah, Anoop S V; Bosson, Jenny A; Unosson, Jon; Barath, Stefan; Lundbäck, Magnus; Cassee, Flemming R; Donaldson, Ken; Sandström, Thomas; Blomberg, Anders; Newby, David E; Mills, Nicholas L

    2014-01-01

    BACKGROUND: Epidemiological studies have reported associations between air pollution exposure and increases in cardiovascular morbidity and mortality. Exposure to air pollutants can influence cardiac autonomic tone and reduce heart rate variability, and may increase the risk of cardiac arrhythmias,

  10. Sensing and stimulation of the vagus nerve for artificial cardiac control

    NARCIS (Netherlands)

    Ordelman, Simone Cornelia Maria Anna

    2012-01-01

    This thesis focuses on sensing cardiovascular signals from the vagus nerve and electrically stimulating the vagus nerve for cardiovascular effects. Sensing cardiovascular signals was attempted on both spontaneous and evoked neural activity. A cardiac-modulated vagus nerve activity pattern was found

  11. Design of Neural Network Control System for Controlling Trajectory of Autonomous Underwater Vehicles

    Directory of Open Access Journals (Sweden)

    İkbal Eski

    2014-01-01

    Full Text Available A neural network based robust control system design for the trajectory of Autonomous Underwater Vehicles (AUVs is presented in this paper. Two types of control structure were used to control prescribed trajectories of an AUV. The vehicle was tested with random disturbances while taxiing under water. The results of the simulation showed that the proposed neural network based robust control system has superior performance in adapting to large random disturbances such as underwater flow. It is proved that this kind of neural predictor could be used in real-time AUV applications.

  12. Internal models for interpreting neural population activity during sensorimotor control.

    Science.gov (United States)

    Golub, Matthew D; Yu, Byron M; Chase, Steven M

    2015-01-01

    To successfully guide limb movements, the brain takes in sensory information about the limb, internally tracks the state of the limb, and produces appropriate motor commands. It is widely believed that this process uses an internal model, which describes our prior beliefs about how the limb responds to motor commands. Here, we leveraged a brain-machine interface (BMI) paradigm in rhesus monkeys and novel statistical analyses of neural population activity to gain insight into moment-by-moment internal model computations. We discovered that a mismatch between subjects' internal models and the actual BMI explains roughly 65% of movement errors, as well as long-standing deficiencies in BMI speed control. We then used the internal models to characterize how the neural population activity changes during BMI learning. More broadly, this work provides an approach for interpreting neural population activity in the context of how prior beliefs guide the transformation of sensory input to motor output.

  13. Fuzzy-Neural Automatic Daylight Control System

    Directory of Open Access Journals (Sweden)

    Grif H. Şt.

    2011-12-01

    Full Text Available The paper presents the design and the tuning of a CMAC controller (Cerebellar Model Articulation Controller implemented in an automatic daylight control application. After the tuning process of the controller, the authors studied the behavior of the automatic lighting control system (ALCS in the presence of luminance disturbances. The luminance disturbances were produced by the authors in night conditions and day conditions as well. During the night conditions, the luminance disturbances were produced by turning on and off a halogen desk lamp. During the day conditions the luminance disturbances were produced in two ways: by daylight contributions changes achieved by covering and uncovering a part of the office window and by turning on and off a halogen desk lamp. During the day conditions the luminance disturbances, produced by turning on and off the halogen lamp, have a smaller amplitude than those produced during the night conditions. The luminance disturbance during the night conditions was a helpful tool to select the proper values of the learning rate for CMAC controller. The luminance disturbances during the day conditions were a helpful tool to demonstrate the right setting of the CMAC controller.

  14. Torque vector control using neural network controller for synchronous reluctance motor

    Energy Technology Data Exchange (ETDEWEB)

    Feng, X. [Teco-Westinghouse Motor Co, R and D Center, Round Rock, TX (United States); Belmans, R.; Hameyer, K. [Katholieke Universiteit Leuven, Dic. ELEN, Dept. ESAT, Leuven-Heverlee (Belgium)

    2000-08-01

    This paper presents the torque vector control technique using a neural network controller for a synchronous reluctance motor. As the artificial neural network controller has the advantages of faster execution speed, harmonic ripple immunity and fault tolerance compared to a DSP-based controller, different multi-layer neural network controllers are designed and trained to produce a correct target vector when presented with the corresponding input vector. The trained result and calculated flops show that although the designed three layer controller with tansig, purelin and hard limit functions has more processing layers, the neuron number of each layer is less than that of other kinds of neural network controller, the requiring less flops and yielding faster execution and response. (orig.)

  15. Fuzzy Neural Network Based Traffic Prediction and Congestion Control in High-Speed Networks

    Institute of Scientific and Technical Information of China (English)

    费翔; 何小燕; 罗军舟; 吴介一; 顾冠群

    2000-01-01

    Congestion control is one of the key problems in high-speed networks, such as ATM. In this paper, a kind of traffic prediction and preventive congestion control scheme is proposed using neural network approach. Traditional predictor using BP neural network has suffered from long convergence time and dissatisfying error. Fuzzy neural network developed in this paper can solve these problems satisfactorily. Simulations show the comparison among no-feedback control scheme,reactive control scheme and neural network based control scheme.

  16. The PAF1 complex component Leo1 is essential for cardiac and neural crest development in zebrafish.

    Science.gov (United States)

    Nguyen, Catherine T; Langenbacher, Adam; Hsieh, Michael; Chen, Jau-Nian

    2010-05-01

    Leo1 is a component of the Polymerase-Associated Factor 1 (PAF1) complex, an evolutionarily conserved protein complex involved in gene transcription regulation and chromatin remodeling. The role of leo1 in vertebrate embryogenesis has not previously been examined. Here, we report that zebrafish leo1 encodes a nuclear protein that has a similar molecular structure to Leo1 proteins from other species. From a genetic screen, we identified a zebrafish mutant defective in the leo1 gene. The truncated Leo1(LA1186) protein lacks a nuclear localization signal and is distributed mostly in the cytoplasm. Phenotypic analysis showed that while the initial patterning of the primitive heart tube is not affected in leo1(LA1186) mutant embryos, the differentiation of cardiomyocytes at the atrioventricular boundary is aberrant, suggesting a requirement for Leo1 in cardiac differentiation. In addition, the expression levels of markers for neural crest-derived cells such as crestin, gch2, dct and mitfa are greatly reduced in leo1(LA1186) mutants, indicating a requirement for Leo1 in maintaining the neural crest population. Consistent with this finding, melanocyte and xanthophore populations are severely reduced, craniofacial cartilage is barely detectable, and mbp-positive glial cells are absent in leo1(LA1186) mutants after three days of development. Taken together, these results provide the first genetic evidence of the requirement for Leo1 in the development of the heart and neural crest cell populations.

  17. Neural activity of orbitofrontal cortex contributes to control of waiting.

    Science.gov (United States)

    Xiao, Xiong; Deng, Hanfei; Wei, Lei; Huang, Yanwang; Wang, Zuoren

    2016-09-01

    The willingness to wait for delayed reward and information is of fundamental importance for deliberative behaviors. The orbitofrontal cortex (OFC) is thought to be a core component of the neural circuitry underlying the capacity to control waiting. However, the neural correlates of active waiting and the causal role of the OFC in the control of waiting still remain largely unknown. Here, we trained rats to perform a waiting task (waiting for a pseudorandom time to obtain the water reward), and recorded neuronal ensembles in the OFC throughout the task. We observed that subset OFC neurons exhibited ramping activities throughout the waiting process. Receiver operating characteristic analysis showed that neural activities during the waiting period even predicted the trial outcomes (patient vs. impatient) on a trial-by-trial basis. Furthermore, optogenetic activation of the OFC during the waiting period improved the waiting performance, but did not influence rats' movement to obtain the reward. Taken together, these findings reveal that the neural activity in the OFC contributes to the control of waiting.

  18. 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.

  19. Insights into the neural control of eccentric contractions.

    Science.gov (United States)

    Duchateau, Jacques; Baudry, Stéphane

    2014-06-01

    The purpose of this brief review is to examine our current knowledge of the neural control of eccentric contractions. The review focuses on three main issues. The first issue considers the ability of individuals to activate muscles maximally during eccentric contractions. Most studies indicate that, regardless of the experimental approach (surface EMG amplitude, twitch superimposition, and motor unit recordings), it is usually more difficult to achieve full activation of a muscle by voluntary command during eccentric contractions than during concentric and isometric contractions. The second issue is related to the specificity of the control strategy used by the central nervous system during submaximal eccentric contractions. This part underscores that although the central nervous system appears to employ a single size-related strategy to activate motoneurons during the different types of contractions, the discharge rate of motor units is less during eccentric contractions across different loading conditions. The last issue addresses the mechanisms that produce this specific neural activation. This section indicates that neural adjustments at both supraspinal and spinal levels contribute to the specific modulation of voluntary activation during eccentric contractions. Although the available information on the control of eccentric contractions has increased during the last two decades, this review indicates that the exact mechanisms underlying the unique neural modulation observed in this type of contraction at spinal and supraspinal levels remains unknown and their understanding represents, therefore, a major challenge for future research on this topic.

  20. Decentralized Neural Backstepping Control Applied to a Robot Manipulator

    Directory of Open Access Journals (Sweden)

    Ramon Garcia-Hernandez

    2013-01-01

    Full Text Available This paper presents a discrete‐time decentralized control scheme for trajectory tracking of a two degrees of freedom (DOF robot manipulator. A high order neural network (HONN is used to approximate a decentralized control law designed by the backstepping technique as applied to a block strict feedback form (BSFF. The weights for each neural network are adapted online by an extended Kalman filter training algorithm. The motion for each joint is controlled independently using only local angular position and velocity measurements. The stability analysis for the closed‐loop system via the Lyapunov approach is included. Finally, the real‐time results show the feasibility of the proposed control scheme using a robot manipulator.

  1. Variable Neural Adaptive Robust Control: A Switched System Approach

    Energy Technology Data Exchange (ETDEWEB)

    Lian, Jianming; Hu, Jianghai; Zak, Stanislaw H.

    2015-05-01

    Variable neural adaptive robust control strategies are proposed for the output tracking control of a class of multi-input multi-output uncertain systems. The controllers incorporate a variable-structure radial basis function (RBF) network as the self-organizing approximator for unknown system dynamics. The variable-structure RBF network solves the problem of structure determination associated with fixed-structure RBF networks. It can determine the network structure on-line dynamically by adding or removing radial basis functions according to the tracking performance. The structure variation is taken into account in the stability analysis of the closed-loop system using a switched system approach with the aid of the piecewise quadratic Lyapunov function. The performance of the proposed variable neural adaptive robust controllers is illustrated with simulations.

  2. 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.

  3. Cardiac Arrhythmias Classification Method Based on MUSIC, Morphological Descriptors, and Neural Network

    Directory of Open Access Journals (Sweden)

    2009-03-01

    Full Text Available An electrocardiogram (ECG beat classification scheme based on multiple signal classification (MUSIC algorithm, morphological descriptors, and neural networks is proposed for discriminating nine ECG beat types. These are normal, fusion of ventricular and normal, fusion of paced and normal, left bundle branch block, right bundle branch block, premature ventricular concentration, atrial premature contraction, paced beat, and ventricular flutter. ECG signal samples from MIT-BIH arrhythmia database are used to evaluate the scheme. MUSIC algorithm is used to calculate pseudospectrum of ECG signals. The low-frequency samples are picked to have the most valuable heartbeat information. These samples along with two morphological descriptors, which deliver the characteristics and features of all parts of the heart, form an input feature vector. This vector is used for the initial training of a classifier neural network. The neural network is designed to have nine sample outputs which constitute the nine beat types. Two neural network schemes, namely multilayered perceptron (MLP neural network and a probabilistic neural network (PNN, are employed. The experimental results achieved a promising accuracy of 99.03% for classifying the beat types using MLP neural network. In addition, our scheme recognizes NORMAL class with 100% accuracy and never misclassifies any other classes as NORMAL.

  4. Cardiac Arrhythmias Classification Method Based on MUSIC, Morphological Descriptors, and Neural Network

    Science.gov (United States)

    Naghsh-Nilchi, Ahmad R.; Kadkhodamohammadi, A. Rahim

    2009-12-01

    An electrocardiogram (ECG) beat classification scheme based on multiple signal classification (MUSIC) algorithm, morphological descriptors, and neural networks is proposed for discriminating nine ECG beat types. These are normal, fusion of ventricular and normal, fusion of paced and normal, left bundle branch block, right bundle branch block, premature ventricular concentration, atrial premature contraction, paced beat, and ventricular flutter. ECG signal samples from MIT-BIH arrhythmia database are used to evaluate the scheme. MUSIC algorithm is used to calculate pseudospectrum of ECG signals. The low-frequency samples are picked to have the most valuable heartbeat information. These samples along with two morphological descriptors, which deliver the characteristics and features of all parts of the heart, form an input feature vector. This vector is used for the initial training of a classifier neural network. The neural network is designed to have nine sample outputs which constitute the nine beat types. Two neural network schemes, namely multilayered perceptron (MLP) neural network and a probabilistic neural network (PNN), are employed. The experimental results achieved a promising accuracy of 99.03% for classifying the beat types using MLP neural network. In addition, our scheme recognizes NORMAL class with 100% accuracy and never misclassifies any other classes as NORMAL.

  5. Nonlinear Decoupling PID Control Using Neural Networks and Multiple Models

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    For a class of complex industrial processes with strong nonlinearity, serious coupling and uncertainty, a nonlinear decoupling proportional-integral-differential (PID) controller is proposed, which consists of a traditional PID controller, a decoupling compensator and a feedforward compensator for the unmodeled dynamics. The parameters of such controller is selected based on the generalized minimum variance control law. The unmodeled dynamics is estimated and compensated by neural networks, a switching mechanism is introduced to improve tracking performance, then a nonlinear decoupling PID control algorithm is proposed. All signals in such switching system are globally bounded and the tracking error is convergent. Simulations show effectiveness of the algorithm.

  6. Spatial Path Following for AUVs Using Adaptive Neural Network Controllers

    Directory of Open Access Journals (Sweden)

    Jiajia Zhou

    2013-01-01

    Full Text Available The spatial path following control problem of autonomous underwater vehicles (AUVs is addressed in this paper. In order to realize AUVs’ spatial path following control under systemic variations and ocean current, three adaptive neural network controllers which are based on the Lyapunov stability theorem are introduced to estimate uncertain parameters of the vehicle’s model and unknown current disturbances. These controllers are designed to guarantee that all the error states in the path following system are asymptotically stable. Simulation results demonstrated that the proposed controller was effective in reducing the path following error and was robust against the disturbances caused by vehicle's uncertainty and ocean currents.

  7. Deterministic chaos control in neural networks on various topologies

    Science.gov (United States)

    Neto, A. J. F.; Lima, F. W. S.

    2017-01-01

    Using numerical simulations, we study the control of deterministic chaos in neural networks on various topologies like Voronoi-Delaunay, Barabási-Albert, Small-World networks and Erdös-Rényi random graphs by "pinning" the state of a "special" neuron. We show that the chaotic activity of the networks or graphs, when control is on, can become constant or periodic.

  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. 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.

  10. Neural Network Control of a Magnetically Suspended Rotor System

    Science.gov (United States)

    Choi, Benjamin B.

    1998-01-01

    Magnetic bearings offer significant advantages because they do not come into contact with other parts during operation, which can reduce maintenance. Higher speeds, no friction, no lubrication, weight reduction, precise position control, and active damping make them far superior to conventional contact bearings. However, there are technical barriers that limit the application of this technology in industry. One of them is the need for a nonlinear controller that can overcome the system nonlinearity and uncertainty inherent in magnetic bearings. At the NASA Lewis Research Center, a neural network was selected as a nonlinear controller because it generates a neural model without any detailed information regarding the internal working of the magnetic bearing system. It can be used even for systems that are too complex for an accurate system model to be derived. A feed-forward architecture with a back-propagation learning algorithm was selected because of its proven performance, accuracy, and relatively easy implementation.

  11. Distributed Adaptive Neural Control for Stochastic Nonlinear Multiagent Systems.

    Science.gov (United States)

    Wang, Fang; Chen, Bing; Lin, Chong; Li, Xuehua

    2016-11-14

    In this paper, a consensus tracking problem of nonlinear multiagent systems is investigated under a directed communication topology. All the followers are modeled by stochastic nonlinear systems in nonstrict feedback form, where nonlinearities and stochastic disturbance terms are totally unknown. Based on the structural characteristic of neural networks (in Lemma 4), a novel distributed adaptive neural control scheme is put forward. The raised control method not only effectively handles unknown nonlinearities in nonstrict feedback systems, but also copes with the interactions among agents and coupling terms. Based on the stochastic Lyapunov functional method, it is indicated that all the signals of the closed-loop system are bounded in probability and all followers' outputs are convergent to a neighborhood of the output of leader. At last, the efficiency of the control method is testified by a numerical example.

  12. Controlling neural activity in Caenorhabditis elegans to evoke chemotactic behavior

    Science.gov (United States)

    Kocabas, Askin; Shen, Ching-Han; Guo, Zengcai V.; Ramanathan, Sharad

    2013-03-01

    Animals locate and track chemoattractive gradients in the environment to find food. With its simple nervous system, Caenorhabditis elegans is a good model system in which to understand how the dynamics of neural activity control this search behavior. To understand how the activity in its interneurons coordinate different motor programs to lead the animal to food, here we used optogenetics and new optical tools to manipulate neural activity directly in freely moving animals to evoke chemotactic behavior. By deducing the classes of activity patterns triggered during chemotaxis and exciting individual neurons with these patterns, we identified interneurons that control the essential locomotory programs for this behavior. Notably, we discovered that controlling the dynamics of activity in just one interneuron pair was sufficient to force the animal to locate, turn towards and track virtual light gradients.

  13. Neural adaptive attitude tracking controller for flexible spacecraft

    Institute of Scientific and Technical Information of China (English)

    XIAO Bing; HU Qing-lei; MA Guang-fu

    2010-01-01

    In this paper,a neural network adaptive controller is proposed for attitude tracking of flexible spacecraft in presence of unknown inertial matrix and external disturbance.In this approach,neural network technique is employed to approximate the unknown system dynamics with finite combinations of some basis functions,and a robust controller is also designed to attenuate the effect of approximation error,more specially,the knowledge of angular velocity is not required.In the closed-loop system,Lyapunov stability analysis shows that the attitude trajectories asymptotically follow the reference output trajectories.Finally,simulation results are presented for the attitude tracking of a flexible spacecraft to show the excellent performance of the proposed controller and illustrate its robustness in face of external disturbances and unknown dynamics.

  14. Recurrent neural networks-based multivariable system PID predictive control

    Institute of Scientific and Technical Information of China (English)

    ZHANG Yan; WANG Fanzhen; SONG Ying; CHEN Zengqiang; YUAN Zhuzhi

    2007-01-01

    A nonlinear proportion integration differentiation (PID) controller is proposed on the basis of recurrent neural networks,due to the difficulty of tuning the parameters of conventional PID controller.In the control process of nonlinear multivariable system,a decoupling controller was constructed,which took advantage of multi-nonlinear PID controllers in parallel.With the idea of predictive control,two multivariable predictive control strategies were established.One strategy involved the use of the general minimum variance control function on the basis of recursive multi-step predictive method.The other involved the adoption of multistep predictive cost energy to train the weights of the decoupling controller.Simulation studies have shown the efficiency of these strategies.

  15. Optogenetic control of the cardiac conduction system (Conference Presentation)

    Science.gov (United States)

    Crocini, Claudia; Ferrantini, Cecilia; Coppini, Raffaele; Loew, Leslie M.; Cerbai, Elisabetta; Poggesi, Corrado; Pavone, Francesco S.; Sacconi, Leonardo

    2016-03-01

    Fatal cardiac arrhythmias are a major medical and social issue in Western countries. Current implantable pacemaker/defibrillators have limited effectiveness and are plagued by frequent malfunctions and complications. Here, we aim at setting up a new method to map and control the electrical activity of whole isolated mouse hearts. We employ a transgenic mouse model expressing Channel Rhodopsin-2 (ChR2) in the heart coupled with voltage optical mapping to monitor and control action potential propagation. The whole heart is loaded with the fluorinated red-shifted voltage sensitive dye (di-4-ANBDQPQ) and imaged with the central portion (128 x 128 pixel) of sCMOS camera operating at frame rate of 1.6 kHz. The wide-field imaging system is implemented with a random access ChR2 activation developed using two orthogonally-mounted acousto-optical deflectors (AODs). AODs rapidly scan different sites of the sample with a commutation time of 4 μs, allowing us to design ad hoc ChR2-stimulation pattern. First, we demonstrate the capability of our system in manipulating the conduction system of the whole mouse heart by changing the electrical propagation features. Then, we explore the efficacy of the random access ChR2 stimulation in inducing arrhythmias as well as to restore the cardiac sinus rhythm during an arrhythmic event. This work shows the potentiality of this new method for studying the mechanisms of arrhythmias and reentry in healthy and diseased hearts, as well as the basis of intra-ventricular dyssynchrony.

  16. 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.

  17. Neural systems supporting the control of affective and cognitive conflicts.

    Science.gov (United States)

    Ochsner, Kevin N; Hughes, Brent; Robertson, Elaine R; Cooper, Jeffrey C; Gabrieli, John D E

    2009-09-01

    Although many studies have examined the neural bases of controlling cognitive responses, the neural systems for controlling conflicts between competing affective responses remain unclear. To address the neural correlates of affective conflict and their relationship to cognitive conflict, the present study collected whole-brain fMRI data during two versions of the Eriksen flanker task. For these tasks, participants indicated either the valence (affective task) or the semantic category (cognitive task) of a central target word while ignoring flanking words that mapped onto either the same (congruent) or a different (incongruent) response as the target. Overall, contrasts of incongruent > congruent trials showed that bilateral dorsal ACC, posterior medial frontal cortex, and dorsolateral pFC were active during both kinds of conflict, whereas rostral medial pFC and left ventrolateral pFC were differentially active during affective or cognitive conflict, respectively. Individual difference analyses showed that separate regions of rostral cingulate/ventromedial pFC and left ventrolateral pFC were positively correlated with the magnitude of response time interference. Taken together, the findings that controlling affective and cognitive conflicts depends upon both common and distinct systems have important implications for understanding the organization of control systems in general and their potential dysfunction in clinical disorders.

  18. 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.

  19. 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.

  20. Neural mechanisms underlying auditory feedback control of speech.

    Science.gov (United States)

    Tourville, Jason A; Reilly, Kevin J; Guenther, Frank H

    2008-02-01

    The neural substrates underlying auditory feedback control of speech were investigated using a combination of functional magnetic resonance imaging (fMRI) and computational modeling. Neural responses were measured while subjects spoke monosyllabic words under two conditions: (i) normal auditory feedback of their speech and (ii) auditory feedback in which the first formant frequency of their speech was unexpectedly shifted in real time. Acoustic measurements showed compensation to the shift within approximately 136 ms of onset. Neuroimaging revealed increased activity in bilateral superior temporal cortex during shifted feedback, indicative of neurons coding mismatches between expected and actual auditory signals, as well as right prefrontal and Rolandic cortical activity. Structural equation modeling revealed increased influence of bilateral auditory cortical areas on right frontal areas during shifted speech, indicating that projections from auditory error cells in posterior superior temporal cortex to motor correction cells in right frontal cortex mediate auditory feedback control of speech.

  1. Neural rotational speed control for wave energy converters

    Science.gov (United States)

    Amundarain, M.; Alberdi, M.; Garrido, A. J.; Garrido, I.

    2011-02-01

    Among the benefits arising from an increasing use of renewable energy are: enhanced security of energy supply, stimulation of economic growth, job creation and protection of the environment. In this context, this study analyses the performance of an oscillating water column device for wave energy conversion in function of the stalling behaviour in Wells turbines, one of the most widely used turbines in wave energy plants. For this purpose, a model of neural rotational speed control system is presented, simulated and implemented. This scheme is employed to appropriately adapt the speed of the doubly-fed induction generator coupled to the turbine according to the pressure drop entry, so as to avoid the undesired stalling behaviour. It is demonstrated that the proposed neural rotational speed control design adequately matches the desired relationship between the slip of the doubly-fed induction generator and the pressure drop input, improving the power generated by the turbine generator module.

  2. 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.

  3. Neural substrates linking balance control and anxiety

    Science.gov (United States)

    Balaban, Carey D.

    2002-01-01

    This communication provides an update of our understanding of the neurological bases for the close association between balance control and anxiety. New data suggest that a vestibulo-recipient region of the parabrachial nucleus (PBN) contains cells that respond to body rotation and position relative to gravity. The PBN, with its reciprocal relationships with the extended central amygdaloid nucleus, infralimbic cortex, and hypothalamus, appears to be an important node in a primary network that processes convergent vestibular, somatic, and visceral information processing to mediate avoidance conditioning, anxiety, and conditioned fear responses. Noradrenergic and serotonergic projections to the vestibular nuclei also have parallel connections with anxiety pathways. The coeruleo-vestibular pathway originates in caudal locus coeruleus (LC) and provides regionally specialized noradrenergic input to the vestibular nuclei, which likely mediate effects of alerting and vigilance on the sensitivity of vestibulo-motor circuits. Both serotonergic and nonserotonergic pathways from the dorsal raphe nucleus and the nucleus raphe obscurus also project differentially to the vestibular nuclei, and 5-HT(2A) receptors are expressed in amygdaloid and cortical targets of the PBN. It is proposed that the dorsal raphe nucleus pathway contributes to both (a) a tradeoff between motor and sensory (information gathering) aspects of responses to self-motion and (b) a calibration of the sensitivity of affective responses to aversive aspects of motion. This updated neurologic model continues to be a synthetic schema for investigating the neurological and neurochemical bases for comorbidity of balance disorders and anxiety disorders.

  4. Color control using neural networks and its application

    Science.gov (United States)

    Tominaga, Shoji

    1996-03-01

    A method is proposed for solving the mapping problem from the 3D color space to the 4D CMYK space of printer ink signals by means of neural network. The CIE-L*a*b* color system is used as the color space. The color reproduction problem is considered as the problem of controlling an unknown static system with four inputs and three outputs. A controller finds the CMYK signals necessary to produce the desired L*a*b* values from a printer. Our solution method for this control is based on a two-phase procedure. Validity of our method is shown in an experiment using a dye sublimation printer.

  5. Accelerator and feedback control simulation using neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Nguyen, D.; Lee, M.; Sass, R.; Shoaee, H.

    1991-05-01

    Unlike present constant model feedback system, neural networks can adapt as the dynamics of the process changes with time. Using a process model, the Accelerator'' network is first trained to simulate the dynamics of the beam for a given beam line. This Accelerator'' network is then used to train a second Controller'' network which performs the control function. In simulation, the networks are used to adjust corrector magnetics to control the launch angle and position of the beam to keep it on the desired trajectory when the incoming beam is perturbed. 4 refs., 3 figs.

  6. Evenness Control of Rapier Loom Tension by Using Neural Network

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    The neural network model (NNM) is used to control the evenness of rapier loom tension and to model the uncertain parameters of drafting process. This model can estimate recursively the tension and incorporate predictive control strategies. After controlling the effect testing, it can be seen that the behavior is excellent following different set point and variations, such as the diameter of the beam of weaver, air stream, density of warp and weft, type of textile, etc. An optimum steady state scheme has been fixed in keeping evenness of tension of loom. This method improves obviously the efficiency and quality of production.

  7. Neural Control Adaptation to Motor Noise Manipulation

    Directory of Open Access Journals (Sweden)

    Christopher J Hasson

    2016-03-01

    Full Text Available Antagonistic muscular co-activation can compensate for movement variability induced by motor noise at the expense of increased energetic costs. Greater antagonistic co-activation is commonly observed in older adults, which could be an adaptation to increased motor noise. The present study tested this hypothesis by manipulating motor noise in twelve young subjects while they practiced a goal-directed task using a myoelectric virtual arm, which was controlled by their biceps and triceps muscle activity. Motor noise was increased by increasing the coefficient of variation of the myoelectric signals. As hypothesized, subjects adapted by increasing antagonistic co-activation, and this was associated with reduced noise-induced performance decrements. A second hypothesis was that a virtual decrease in motor noise, achieved by smoothing the myoelectric signals, would have the opposite effect: co-activation would decrease and motor performance would improve. However, the results showed that a decrease in noise made performance worse instead of better, with no change in co-activation. Overall, these findings suggest that the nervous system adapts to virtual increases in motor noise by increasing antagonistic co-activation, and this preserves motor performance. Reducing noise may have failed to benefit performance due to characteristics of the filtering process itself, e.g. delays are introduced and muscle activity bursts are attenuated. The observed adaptations to increased noise may explain in part why older adults and many patient populations have greater antagonistic co-activation, which could represent an adaptation to increased motor noise, along with a desire for increased joint stability.

  8. 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.

  9. Adaptive Neural Network Controller for Thermogenerator Angular Velocity Stabilization System

    OpenAIRE

    2013-01-01

    The paper presents an analytical and simulation approach for the selection of activation functions for the class of neural network controllers for ship’s thermogenerator angular velocity stabilization system. Such systems can be found in many ships. A Lyapunov-like stability analysis is performed in order to obtain a weight update law. A number of simulations were performed to find the best activation function using integral error criteria and statistical T-tests.

  10. Speed-Sensorless Control Using Elman Neural Network

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    This paper describes a modified speed-sensorless control for induction motor (IM) based on space vector pulse width modulation and neural network. An Elman ANN method to identify the IM speed is proposed,with IM parameters employed as associated elements. The BP algorithm is used to provide an adaptive estimation of the motor speed. The effectiveness of the proposed method is verified by simulation results. The implementation on TMS320F240 fixed DSP is provided.

  11. Complementary Detection of Embryotoxic Properties of Substances in the Neural and Cardiac Embryonic Stem Cell Tests

    NARCIS (Netherlands)

    Theunissen, P.T.; Pennings, J.L.A.; Dartel, van D.A.M.; Robinson, J.F.; Kleinjans, J.C.S.; Piersma, A.H.

    2013-01-01

    In developmental toxicity testing, in vitro screening assays are highly needed to increase efficiency and to reduce animal use. A promising in vitro assay is the cardiac embryonic stem cell test (ESTc), in which the effect of developmental toxicants on cardiomyocyte differentiation is assessed. Rece

  12. 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.

  13. Implementation of Neural Control for Continuous Stirred Tank Reactor (CSTR

    Directory of Open Access Journals (Sweden)

    Karima M. Putrus

    2011-01-01

    Full Text Available In this paper a dynamic behavior and control of a jacketed continuous stirred tank reactor (CSTR is developed using different control strategies, conventional feedback control (PI and PID, and neural network (NARMA-L2, and NN Predictive control. The dynamic model for CSTR process is described by a first order lag system with dead time.The optimum tuning of control parameters are found by two different methods; Frequency Analysis Curve method (Bode diagram and Process Reaction Curve using the mean of Square Error (MSE method. It is found that the Process Reaction Curve method is better than the Frequency Analysis Curve method and PID feedback controller is better than PI feedback controller. The results show that the artificial neural network is the best method to control the CSTR process and it is better than the conventional method because it has smaller value of mean square error (MSE. MATLAB program is used as a tool of solution for all cases used in the present work.

  14. Neural Network Based Montioring and Control of Fluidized Bed.

    Energy Technology Data Exchange (ETDEWEB)

    Bodruzzaman, M.; Essawy, M.A.

    1996-04-01

    The goal of this project was to develop chaos analysis and neural network-based modeling techniques and apply them to the pressure-drop data obtained from the Fluid Bed Combustion (FBC) system (a small scale prototype model) located at the Federal Energy Technology Center (FETC)-Morgantown. The second goal was to develop neural network-based chaos control techniques and provide a suggestive prototype for possible real-time application to the FBC system. The experimental pressure data were collected from a cold FBC experimental set-up at the Morgantown Center. We have performed several analysis on these data in order to unveil their dynamical and chaotic characteristics. The phase-space attractors were constructed from the one dimensional time series data, using the time-delay embedding method, for both normal and abnormal conditions. Several identifying parameters were also computed from these attractors such as the correlation dimension, the Kolmogorov entropy, and the Lyapunov exponents. These chaotic attractor parameters can be used to discriminate between the normal and abnormal operating conditions of the FBC system. It was found that, the abnormal data has higher correlation dimension, larger Kolmogorov entropy and larger positive Lyapunov exponents as compared to the normal data. Chaotic system control using neural network based techniques were also investigated and compared to conventional chaotic system control techniques. Both types of chaotic system control techniques were applied to some typical chaotic systems such as the logistic, the Henon, and the Lorenz systems. A prototype model for real-time implementation of these techniques has been suggested to control the FBC system. These models can be implemented for real-time control in a next phase of the project after obtaining further measurements from the experimental model. After testing the control algorithms developed for the FBC model, the next step is to implement them on hardware and link them to

  15. Chaotic neural network applied to two-dimensional motion control.

    Science.gov (United States)

    Yoshida, Hiroyuki; Kurata, Shuhei; Li, Yongtao; Nara, Shigetoshi

    2010-03-01

    Chaotic dynamics generated in a chaotic neural network model are applied to 2-dimensional (2-D) motion control. The change of position of a moving object in each control time step is determined by a motion function which is calculated from the firing activity of the chaotic neural network. Prototype attractors which correspond to simple motions of the object toward four directions in 2-D space are embedded in the neural network model by designing synaptic connection strengths. Chaotic dynamics introduced by changing system parameters sample intermediate points in the high-dimensional state space between the embedded attractors, resulting in motion in various directions. By means of adaptive switching of the system parameters between a chaotic regime and an attractor regime, the object is able to reach a target in a 2-D maze. In computer experiments, the success rate of this method over many trials not only shows better performance than that of stochastic random pattern generators but also shows that chaotic dynamics can be useful for realizing robust, adaptive and complex control function with simple rules.

  16. Preface: cardiac control pathways: signaling and transport phenomena.

    Science.gov (United States)

    Sideman, Samuel

    2008-03-01

    Signaling is part of a complex system of communication that governs basic cellular functions and coordinates cellular activity. Transfer of ions and signaling molecules and their interactions with appropriate receptors, transmembrane transport, and the consequent intracellular interactions and functional cellular response represent a complex system of interwoven phenomena of transport, signaling, conformational changes, chemical activation, and/or genetic expression. The well-being of the cell thus depends on a harmonic orchestration of all these events and the existence of control mechanisms that assure the normal behavior of the various parameters involved and their orderly expression. The ability of cells to sustain life by perceiving and responding correctly to their microenvironment is the basis for development, tissue repair, and immunity, as well as normal tissue homeostasis. Natural deviations, or human-induced interference in the signaling pathways and/or inter- and intracellular transport and information transfer, are responsible for the generation, modulation, and control of diseases. The present overview aims to highlight some major topics of the highly complex cellular information transfer processes and their control mechanisms. Our goal is to contribute to the understanding of the normal and pathophysiological phenomena associated with cardiac functions so that more efficient therapeutic modalities can be developed. Our objective in this volume is to identify and enhance the study of some basic passive and active physical and chemical transport phenomena, physiological signaling pathways, and their biological consequences.

  17. Medical Devices; Cardiovascular Devices; Classification of the Steerable Cardiac Ablation Catheter Remote Control System. Final order.

    Science.gov (United States)

    2015-09-30

    The Food and Drug Administration (FDA) is classifying the steerable cardiac ablation catheter remote control system into class II (special controls). The special controls that will apply to the device are identified in this order and will be part of the codified language for the steerable cardiac ablation catheter remote control system's classification. The Agency is classifying the device into class II (special controls) in order to provide a reasonable assurance of safety and effectiveness of the device.

  18. Neural Network Based PID Gain Tuning of Chemical Plant Controller

    Science.gov (United States)

    Abe, Yoshihiro; Konishi, Masami; Imai, Jun; Hasegawa, Ryusaku; Watanabe, Masamori; Kamijo, Hiroaki

    In these years, plant control systems are highly automated and applied to many industries. The control performances change with the passage of time, because of the deterioration of plant facilities. This is why human experts tune the control system to improve the total plant performances. In this study, PID control system for the oil refining chemical plant process is treated. In oil refining, there are thousands of the control loops in the plant to keep the product quality at the desired value and to secure the safety of the plant operation. According to the ambiguity of the interference between control loops, it is difficult to estimate the plant dynamical model accurately. Using neuro emulator and recurrent neural networks model (RNN model) for emulation and tuning parameters, PID gain tuning system of chemical plant controller is constructed. Through numerical experiments using actual plant data, effect of the proposed method was ascertained.

  19. Control on a 2-D Wing Flutter Using an AdaptiveNonlinear Neural Controller

    Directory of Open Access Journals (Sweden)

    Hayder S. Abd Al-Amir

    2011-01-01

    Full Text Available An adaptive nonlinear neural controller to reduce the nonlinear flutter in 2-D wing is proposed in the paper. The nonlinearities in the system come from the quasi steady aerodynamic model and torsional spring in pitch direction. Time domain simulations are used to examine the dynamic aero elastic instabilities of the system (e.g. the onset of flutter and limit cycle oscillation, LCO. The structure of the controller consists of two models :the modified Elman neural network (MENN and the feed forward multi-layer Perceptron (MLP. The MENN model is trained with off-line and on-line stages to guarantee that the outputs of the model accurately represent the plunge and pitch motion of the wing and this neural model acts as the identifier. The feed forward neural controller is trained off-line and adaptive weights are implemented on-line to find the flap angles, which controls the plunge and pitch motion of the wing. The general back propagation algorithm is used to learn the feed forward neural controller and the neural identifier. The simulation results show the effectiveness of the proposed control algorithm; this is demonstrated by the minimized tracking error to zero approximation with very acceptable settling time even with the existence of bounded external disturbances.

  20. Neural networks for advanced control of robot manipulators.

    Science.gov (United States)

    Patino, H D; Carelli, R; Kuchen, B R

    2002-01-01

    Presents an approach and a systematic design methodology to adaptive motion control based on neural networks (NNs) for high-performance robot manipulators, for which stability conditions and performance evaluation are given. The neurocontroller includes a linear combination of a set of off-line trained NNs, and an update law of the linear combination coefficients to adjust robot dynamics and payload uncertain parameters. A procedure is presented to select the learning conditions for each NN in the bank. The proposed scheme, based on fixed NNs, is computationally more efficient than the case of using the learning capabilities of the neural network to be adapted, as that used in feedback architectures that need to propagate back control errors through the model to adjust the neurocontroller. A practical stability result for the neurocontrol system is given. That is, we prove that the control error converges asymptotically to a neighborhood of zero, whose size is evaluated and depends on the approximation error of the NN bank and the design parameters of the controller. In addition, a robust adaptive controller to NN learning errors is proposed, using a sign or saturation switching function in the control law, which leads to global asymptotic stability and zero convergence of control errors. Simulation results showing the practical feasibility and performance of the proposed approach to robotics are given.

  1. Model algorithm control using neural networks for input delayed nonlinear control system

    Institute of Scientific and Technical Information of China (English)

    Yuanliang Zhang; Kil To Chong

    2015-01-01

    The performance of the model algorithm control method is partial y based on the accuracy of the system’s model. It is diffi-cult to obtain a good model of a nonlinear system, especial y when the nonlinearity is high. Neural networks have the ability to“learn”the characteristics of a system through nonlinear mapping to rep-resent nonlinear functions as wel as their inverse functions. This paper presents a model algorithm control method using neural net-works for nonlinear time delay systems. Two neural networks are used in the control scheme. One neural network is trained as the model of the nonlinear time delay system, and the other one pro-duces the control inputs. The neural networks are combined with the model algorithm control method to control the nonlinear time delay systems. Three examples are used to il ustrate the proposed control method. The simulation results show that the proposed control method has a good control performance for nonlinear time delay systems.

  2. Autonomous Defensive Space Control via On-Board Artificial Neural Networks

    Science.gov (United States)

    2007-04-01

    AUTONOMOUS DEFENSIVE SPACE CONTROL VIA ON-BOARD ARTIFICIAL NEURAL NETWORKS Michael T. Manor, Major, USAF April 2007...TITLE AND SUBTITLE Sutonomous Defensive Space Control via On-Board Artificial Neural Networks 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM...11 HOW ARTIFICIAL NEURAL NETWORKS WORK

  3. Neural Network Schemes in Cartesian Space Control of Robot Manipulators

    Directory of Open Access Journals (Sweden)

    Yiannis S. BOUTALIS

    2001-12-01

    Full Text Available In this paper we are studying the Cartesian space robot manipulator control problem by using Neural Networks (NN. Although NN compensation for model uncertainties has been traditionally carried out by modifying the joint torque/force of the robot, it is also possible to achieve the same objective by using the NN to modify other quantities of the controller. We present and evaluate four different NN controller designs to achieve disturbance rejection for an uncertain system. The design perspectives are dependent on the compensated position by NN. There are four quantities that can be compensated: torque , force F, control input U and the input trajectory Xd. By defining a unified training signal all NN control schemes have the same goal of minimizing the same objective functions. We compare the four schemes in respect to their control performance and the efficiency of the NN designs, which is demonstrated via simulations.

  4. A neural fuzzy controller learning by fuzzy error propagation

    Science.gov (United States)

    Nauck, Detlef; Kruse, Rudolf

    1992-01-01

    In this paper, we describe a procedure to integrate techniques for the adaptation of membership functions in a linguistic variable based fuzzy control environment by using neural network learning principles. This is an extension to our work. We solve this problem by defining a fuzzy error that is propagated back through the architecture of our fuzzy controller. According to this fuzzy error and the strength of its antecedent each fuzzy rule determines its amount of error. Depending on the current state of the controlled system and the control action derived from the conclusion, each rule tunes the membership functions of its antecedent and its conclusion. By this we get an unsupervised learning technique that enables a fuzzy controller to adapt to a control task by knowing just about the global state and the fuzzy error.

  5. An integrated architecture of adaptive neural network control for dynamic systems

    Energy Technology Data Exchange (ETDEWEB)

    Ke, Liu; Tokar, R.; Mcvey, B.

    1994-07-01

    In this study, an integrated neural network control architecture for nonlinear dynamic systems is presented. Most of the recent emphasis in the neural network control field has no error feedback as the control input which rises the adaptation problem. The integrated architecture in this paper combines feed forward control and error feedback adaptive control using neural networks. The paper reveals the different internal functionality of these two kinds of neural network controllers for certain input styles, e.g., state feedback and error feedback. Feed forward neural network controllers with state feedback establish fixed control mappings which can not adapt when model uncertainties present. With error feedbacks, neural network controllers learn the slopes or the gains respecting to the error feedbacks, which are error driven adaptive control systems. The results demonstrate that the two kinds of control scheme can be combined to realize their individual advantages. Testing with disturbances added to the plant shows good tracking and adaptation.

  6. 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...

  7. 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.

  8. 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.

  9. Control aspects of motor neural prosthesis: sensory interface.

    Science.gov (United States)

    Popović, Dejan B; Dosen, Strahinja; Popović, Mirjana B; Stefanović, Filip; Kojović, Jovana

    2007-01-01

    A neural prosthesis (NP) has two applications: permanent assistance of function, and temporary assistance that contributes to long-term recovery of function. Here, we address control issues for a therapeutic NP which uses surface electrodes. We suggest that the effective NP for therapy needs to implement rule-based control. Rule-based control relies on the triggering of preprogrammed sequences of electrical stimulation by the sensory signals. The sensory system in the therapeutic NP needs to be simple for installation, allow self-calibration, it must be robust, and sufficiently redundant in order to guarantee safe operation. The sensory signals need to generate control signals; hence, sensory fusion is needed. MEMS technology today provides sensors that fulfill the technical requirements (accelerometers, gyroscopes, force sensing resistors). Therefore, the task was to design a sensory signal processing method from the mentioned solid state sensors that would recognize phases during the gait cycle. This is necessary for the control of multi channel electrical stimulation. The sensory fusion consists of the following two phases: 1) estimation of vertical and horizontal components of the ground reaction force, center of pressure, and joint angles from the solid-state sensors, and 2) fusion of the estimated signals into a sequence of command signals. The first phase was realized by the use of artificial neural networks and adaptive neuro-fuzzy inference systems, while the second by the use of inductive learning described in our earlier work [1].

  10. Neural correlates of executive control in the avian brain.

    Directory of Open Access Journals (Sweden)

    Jonas Rose

    2005-06-01

    Full Text Available Executive control, the ability to plan one's behaviour to achieve a goal, is a hallmark of frontal lobe function in humans and other primates. In the current study we report neural correlates of executive control in the avian nidopallium caudolaterale, a region analogous to the mammalian prefrontal cortex. Homing pigeons (Columba livia performed a working memory task in which cues instructed them whether stimuli should be remembered or forgotten. When instructed to remember, many neurons showed sustained activation throughout the memory period. When instructed to forget, the sustained activation was abolished. Consistent with the neural data, the behavioural data showed that memory performance was high after instructions to remember, and dropped to chance after instructions to forget. Our findings indicate that neurons in the avian nidopallium caudolaterale participate in one of the core forms of executive control, the control of what should be remembered and what should be forgotten. This form of executive control is fundamental not only to working memory, but also to all cognition.

  11. 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.

  12. Graded vascular autonomic control versus discontinuous cardiac control during gradual upright tilt.

    Science.gov (United States)

    Bahjaoui-Bouhaddi, M; Cappelle, S; Henriet, M T; Dumoulin, G; Wolf, J P; Regnard, J

    2000-03-15

    Indexes of heart rate variability (HRV) and the slope of cardiac baroreflex are extensively used for non invasive assessment of circulatory autonomic control in pathophysiology. We performed this study (1) to assess the sensitivity of these indexes towards small graded postural stimulations and (2) to delineate the informations provided about the settings of both vascular tone and cardiac activity. Twenty healthy subjects were randomly tilted for eight minutes at each of the six angles: -10 degrees, 0 degrees (supine), 10 degrees, 30 degrees, 45 degrees, and 60 degrees. Instant RR-interval and finger blood pressure (BP) were continuously recorded, and venous blood was collected at the end of each 8 min position for catecholamines determination. Group average heart rate, noradrenaline and diastolic BP (DBP) increased linearly with head-up tilt angle from 10 degrees. Systolic BP (SBB) ranked only two distinct series -10 degrees, 0 degrees, 10 degrees versus 30 degrees, 45 degrees, 60 degrees, as did the number of spontaneous baroreflex (SBR) sequences. The spectral power of the low-frequency (LF) and high-frequency (HF) of RR variability and the ratio LF/HF changed rather abruptly from either 30 degrees or 45 degrees, depending on each individual. Both HF/tot i.e. the ratio of HF to total spectral RR variability and the slope of SBR decreased markedly from 10 degrees to 30 degrees and less but more gradually from 30 degrees to 60 degrees. Thus, our observations argue for gradual adjustments of vascular tone as reflected by highly consistent changes in plasma noradrenaline and diastolic arterial pressure, contrasting with a main discontinuous autonomic setting of cardiac activity as reflected by changes in the harmonic components of spectral RR variability and in the slope of cardiac baroreflex. The pattern of changes in systolic arterial pressure attested the discontinuous cardiac autonomic control rather than the gradual setting of arterial tone. We submit that

  13. Chaos Control and Anti-control via a Fuzzy Neural Network Inverse System Method

    Institute of Scientific and Technical Information of China (English)

    任海鹏; 刘丁

    2002-01-01

    We propose a new method for chaos control and anti-control, which is referred to as the fuzzy-neural network inverse system method (FNNIS). The Sugeno-type fuzzy-neural network (FNN) is employed to learn the kinetics of the system to be controlled. Then the FNN model is used with the inverse system method to make the system to be controlled to track the reference input. If the system to be controlled is chaotic and the reference input is non-chaotic, chaos control can be implemented via the FNNIS method. If the system to be controlled is nonchaotic and the reference input is chaotic, chaos anti-control can be implemented. Theorems about the effect of the FNN model error upon control are established. The simulation results show that this method is feasible and effective for chaos control and anti-control.

  14. Research on Power Control of Wind Power Generation Based on Neural Network Adaptive Control

    Institute of Scientific and Technical Information of China (English)

    Hai-ying DONG; Chuan-hua SUN

    2010-01-01

    -For the characteristics of wind power generation system is multivariable,nonlinear and random,in this paper the neural network PID adaptive control is adopted.The size of pitch angle is adjusted in time to improve the performance of power control.The PID parameters are corrected by the gradient descent method,and Radial Basis Functinn(RBF)neural network is used as the system identifier in this method.Simulation results shaw that by using neural adaptive PID controller the generator power control can inhibit effectively the speed and affect the output power of generator.The dynamic performance and robustness of the controlled system is good,and the performance of wind power system is improved.

  15. Optimal Control Problem of Feeding Adaptations of Daphnia and Neural Network Simulation

    Science.gov (United States)

    Kmet', Tibor; Kmet'ov, Mria

    2010-09-01

    A neural network based optimal control synthesis is presented for solving optimal control problems with control and state constraints and open final time. The optimal control problem is transcribed into nonlinear programming problem, which is implemented with adaptive critic neural network [9] and recurrent neural network for solving nonlinear proprojection equations [10]. The proposed simulation methods is illustrated by the optimal control problem of feeding adaptation of filter feeders of Daphnia. Results show that adaptive critic based systematic approach and neural network solving of nonlinear equations hold promise for obtaining the optimal control with control and state constraints and open final time.

  16. Spatiotemporal control of cardiac anisotropy using dynamic nanotopographic cues.

    Science.gov (United States)

    Mengsteab, Paulos Y; Uto, Koichiro; Smith, Alec S T; Frankel, Sam; Fisher, Elliot; Nawas, Zeid; Macadangdang, Jesse; Ebara, Mitsuhiro; Kim, Deok-Ho

    2016-04-01

    Coordinated extracellular matrix spatiotemporal reorganization helps regulate cellular differentiation, maturation, and function in vivo, and is therefore vital for the correct formation, maintenance, and healing of complex anatomic structures. In order to evaluate the potential for cultured cells to respond to dynamic changes in their in vitro microenvironment, as they do in vivo, the collective behavior of primary cardiac muscle cells cultured on nanofabricated substrates with controllable anisotropic topographies was studied. A thermally induced shape memory polymer (SMP) was employed to assess the effects of a 90° transition in substrate pattern orientation on the contractile direction and structural organization of cardiomyocyte sheets. Cardiomyocyte sheets cultured on SMPs exhibited anisotropic contractions before shape transition. 48 h after heat-induced shape transition, the direction of cardiomyocyte contraction reoriented significantly and exhibited a bimodal distribution, with peaks at ∼45 and -45° (P < 0.001). Immunocytochemical analysis highlighted the significant structural changes that the cells underwent in response to the shift in underlying topography. The presented results demonstrate that initial anisotropic nanotopographic cues do not permanently determine the organizational fate or contractile properties of cardiomyocytes in culture. Given the importance of surface cues in regulating primary and stem cell development, investigation of such tunable nanotopographies may have important implications for advancing cellular maturation and performance in vitro, as well as improving our understanding of cellular development in response to dynamic biophysical cues.

  17. Cardiac autonomic control in adolescents with primary hypertension

    Directory of Open Access Journals (Sweden)

    Havlíceková Z

    2009-12-01

    Full Text Available Abstract Background Impairment in cardiovascular autonomic regulation participates in the onset and maintenance of primary hypertension. Objective The aim of the present study was to evaluate cardiac autonomic control using long-term heart rate variability (HRV analysis in adolescents with primary hypertension. Subjects and methods Twenty two adolescent patients with primary hypertension (5 girls/17 boys aged 14-19 years and 22 healthy subjects matched for age and gender were enrolled. Two periods from 24-hour ECG recording were evaluated by HRV analysis: awake state and sleep. HRV analysis included spectral power in low frequency band (LF, in high frequency band (HF, and LF/HF ratio. Results In awake state, adolescents with primary hypertension had lower HF and higher LF and LF/HF ratio. During sleep, HF was lower and LF/HF ratio was higher in patients with primary hypertension. Conclusions A combination of sympathetic predominance and reduced vagal activity might represent a potential link between psychosocial factors and primary hypertension, associated with increased cardiovascular morbidity.

  18. Decentralized Control of Dynamic Routing with a Neural Network Algorithm

    Institute of Scientific and Technical Information of China (English)

    2000-01-01

    A state-dependent routing algorithm based on the neural network model, which takes advantage of other dynamic routing algorithm for circuit-switched network, is given in [1]. But, the Algorithm in [1] is a centralized control model with complex O (N7), therefore, is difficult to realize by hardware. A simplified algorithm is put forward in this paper, in which routing can be controlled decentralizedly, and its complexity is reduced to O (10N3). Computer simulations are made in a fully connected test network with eight nodes. The results show that the centralized control model has very effective performance that can match RTNR, and the centralized control model is not as good as the centralized one but better than DAR-1.

  19. Adaptive Neural Network Control with Control Allocation for A Manned Submersible in Deep Sea

    Institute of Scientific and Technical Information of China (English)

    YU Jian-cheng; ZHANG Ai-qun; WANG Xiao-hui; WU Bao-ju

    2007-01-01

    This paper thoroughly studies a control system with control allocation for a manned submersible in deep sea being developed in China. The proposed control system consists of a neural-network-based direct adaptive controller and a dynamic control allocation module. A control energy cost function is used as the optimization criteria of the control allocation module, and weighted pseudo-inverse is used to find the solution of the control allocation problem. In the presence of bounded unknown disturbance and neural networks approximation error, stability of the closed-loop control system of manned submersible is proved with Lyaponov theory. The feasibility and validity of the proposed control system is further verified through experiments conducted on a semi-physical simulation platform for the manned submersible in deep sea.

  20. 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.

  1. CONTROL OF NONLINEAR PROCESS USING NEURAL NETWORK BASED MODEL PREDICTIVE CONTROL

    Directory of Open Access Journals (Sweden)

    Dr.A.TRIVEDI

    2011-04-01

    Full Text Available This paper presents a Neural Network based Model Predictive Control (NNMPC strategy to control nonlinear process. Multilayer Perceptron Neural Network (MLP is chosen to represent a Nonlinear Auto Regressive with eXogenous signal (NARX model of a nonlinear system. NARX dynamic model is based on feed-forward architecture and offers good approximation capabilities along with robustness and accuracy. Based on the identified neural model, a generalized predictive control (GPC algorithm is implemented to control the composition in acontinuous stirred tank reactor (CSTR, whose parameters are optimally determined by solving quadratic performance index using well known Levenberg-Marquardt and Quasi-Newton algorithm. NNMPC is tuned by selecting few horizon parameters and weighting factor. The tracking performance of the NNMPC is tested using different amplitude function as a reference signal on CSTR application. Also the robustness and performance is tested in the presence of disturbance on random reference signal.

  2. Autonomous Traffic Signal Control Model with Neural Network Analogy

    CERN Document Server

    Ohira, T

    1997-01-01

    We propose here an autonomous traffic signal control model based on analogy with neural networks. In this model, the length of cycle time period of traffic lights at each signal is autonomously adapted. We find a self-organizing collective behavior of such a model through simulation on a one-dimensional lattice model road: traffic congestion is greatly diffused when traffic signals have such autonomous adaptability with suitably tuned parameters. We also find that effectiveness of the system emerges through interactions between units and shows a threshold transition as a function of proportion of adaptive signals in the model.

  3. Electric Voltage Control as an Implementation of Neural Network Applications

    Directory of Open Access Journals (Sweden)

    A. A. Al-Rababah

    2008-01-01

    Full Text Available Present study was proposed the monitoring of mathematical model of electric voltage source with using neural network for application in control systems as sensor and command signal. The monitoring system, consist of toroidal choke or transformer with high saturated ferromagnetic cores. The input information we receive from current periodic curves. The current was distributed into Fourier or walsh series. The combination of these harmonics and their amplitude values determine monitoring voltage value directly. For increase of this system precision, the mathematical model was constructed on basis of partial differential quasi-stationary electromagnetic field equations and ordi-nary differential electromagnetic circuit equations combination.

  4. Controllability of time-varying cellular neural networks

    Directory of Open Access Journals (Sweden)

    Wadie Aziz

    2005-11-01

    Full Text Available In this work, we consider the model of Cellular Neural Network (CNN introduced by Chua and Yang in 1988, but with the cloning templates $omega$-periodic in time. By imposing periodic boundary conditions the matrices involved in the system become circulant and $omega$-periodic. We show some results on the controllability of the linear model using a Theorem by Brunovsky for the case of linear and $omega$-periodic system. Also we use this approach in image detection, specifically foreground, background and contours of figures in different scales of grey.

  5. Murine dishevelled 3 functions in redundant pathways with dishevelled 1 and 2 in normal cardiac outflow tract, cochlea, and neural tube development.

    Directory of Open Access Journals (Sweden)

    S Leah Etheridge

    2008-11-01

    Full Text Available Dishevelled (Dvl proteins are important signaling components of both the canonical beta-catenin/Wnt pathway, which controls cell proliferation and patterning, and the planar cell polarity (PCP pathway, which coordinates cell polarity within a sheet of cells and also directs convergent extension cell (CE movements that produce narrowing and elongation of the tissue. Three mammalian Dvl genes have been identified and the developmental roles of Dvl1 and Dvl2 were previously determined. Here, we identify the functions of Dvl3 in development and provide evidence of functional redundancy among the three murine Dvls. Dvl3(-/- mice died perinatally with cardiac outflow tract abnormalities, including double outlet right ventricle and persistent truncus arteriosis. These mutants also displayed a misorientated stereocilia in the organ of Corti, a phenotype that was enhanced with the additional loss of a single allele of the PCP component Vangl2/Ltap (LtapLp/+. Although neurulation appeared normal in both Dvl3(-/- and LtapLp/+ mutants, Dvl3(+/-;LtapLp/+ combined mutants displayed incomplete neural tube closure. Importantly, we show that many of the roles of Dvl3 are also shared by Dvl1 and Dvl2. More severe phenotypes were observed in Dvl3 mutants with the deficiency of another Dvl, and increasing Dvl dosage genetically with Dvl transgenes demonstrated the ability of Dvls to compensate for each other to enable normal development. Interestingly, global canonical Wnt signaling appeared largely unaffected in the double Dvl mutants, suggesting that low Dvl levels are sufficient for functional canonical Wnt signals. In summary, we demonstrate that Dvl3 is required for cardiac outflow tract development and describe its importance in the PCP pathway during neurulation and cochlea development. Finally, we establish several developmental processes in which the three Dvls are functionally redundant.

  6. Murine dishevelled 3 functions in redundant pathways with dishevelled 1 and 2 in normal cardiac outflow tract, cochlea, and neural tube development.

    Science.gov (United States)

    Etheridge, S Leah; Ray, Saugata; Li, Shuangding; Hamblet, Natasha S; Lijam, Nardos; Tsang, Michael; Greer, Joy; Kardos, Natalie; Wang, Jianbo; Sussman, Daniel J; Chen, Ping; Wynshaw-Boris, Anthony

    2008-11-01

    Dishevelled (Dvl) proteins are important signaling components of both the canonical beta-catenin/Wnt pathway, which controls cell proliferation and patterning, and the planar cell polarity (PCP) pathway, which coordinates cell polarity within a sheet of cells and also directs convergent extension cell (CE) movements that produce narrowing and elongation of the tissue. Three mammalian Dvl genes have been identified and the developmental roles of Dvl1 and Dvl2 were previously determined. Here, we identify the functions of Dvl3 in development and provide evidence of functional redundancy among the three murine Dvls. Dvl3(-/-) mice died perinatally with cardiac outflow tract abnormalities, including double outlet right ventricle and persistent truncus arteriosis. These mutants also displayed a misorientated stereocilia in the organ of Corti, a phenotype that was enhanced with the additional loss of a single allele of the PCP component Vangl2/Ltap (LtapLp/+). Although neurulation appeared normal in both Dvl3(-/-) and LtapLp/+ mutants, Dvl3(+/-);LtapLp/+ combined mutants displayed incomplete neural tube closure. Importantly, we show that many of the roles of Dvl3 are also shared by Dvl1 and Dvl2. More severe phenotypes were observed in Dvl3 mutants with the deficiency of another Dvl, and increasing Dvl dosage genetically with Dvl transgenes demonstrated the ability of Dvls to compensate for each other to enable normal development. Interestingly, global canonical Wnt signaling appeared largely unaffected in the double Dvl mutants, suggesting that low Dvl levels are sufficient for functional canonical Wnt signals. In summary, we demonstrate that Dvl3 is required for cardiac outflow tract development and describe its importance in the PCP pathway during neurulation and cochlea development. Finally, we establish several developmental processes in which the three Dvls are functionally redundant.

  7. Adaptive Neural Control Design For a Class of Nonlinear Time-delay Systems

    Institute of Scientific and Technical Information of China (English)

    FENG Ling-ling; ZHANG Wei

    2014-01-01

    This paper proposes an indirect adaptive neural control scheme for a class of nonlinear systems with time delays. Based on the backstepping technique and Lyapunov–Krasovskii functional method are combined to construct the indirect adaptive neural controller. The proposed indirect adaptive neural controller guarantees that the state variables converge to a small neighborhood of the origin and all the signals of the closed-loop system are bounded. Finally, an example is used to show the effectiveness of the proposed control strategy.

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

    Directory of Open Access Journals (Sweden)

    Y.A. Ahmed

    2015-09-01

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

  9. 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.

  10. Some historical reflections on the neural control of locomotion.

    Science.gov (United States)

    Clarac, François

    2008-01-01

    Thought on the neural control of locomotion dates back to antiquity. In this article, however, the focus is more recent by starting with some major 17th century concepts, which were developed by René Descartes, a French philosopher; Thomas Willis, an English anatomist; and Giovanni Borelli, an Italian physiologist and physicist. Each relied on his personal expertise to theorize on the organization and control of movements. The 18th and early 19th centuries saw work on both the central and peripheral control of movement: the former most notably by Johann Unzer, Marie Jean-Pierre Flourens and Julien-Jean-César Legallois, and the latter by Unzer, Jirí Procháska and many others. Next in the 19th century, neurologists used human locomotion as a precise tool for characterizing motor pathologies: e.g., Guillaume Duchenne de Boulogne's description of locomotor ataxia. Jean-Martin Charcot considered motor control to be organized at two levels of the central nervous system: the cerebral cortex and the spinal cord. Maurice Philippson's defined the dog's step cycle and considered that locomotion used both central and reflex mechanisms. Charles Sherrington explained that locomotor control was usually thought to consist of a succession of peripheral reflexes (e.g., the stepping reflexes). Thomas Graham Brown's then contemporary evidence for the spinal origin of locomotor rhythmicity languished in obscurity until the early 1960s. By then the stage was set for an international assault on the neural control of locomotion, which featured research conducted on both invertebrate and vertebrate animal models. These contributions have progressively became more integrated and interactive, with current work emphasizing that locomotor control involves a seamless integration between central locomotor networks and peripheral feedback.

  11. Towards a general neural controller for quadrupedal locomotion.

    Science.gov (United States)

    Maufroy, Christophe; Kimura, Hiroshi; Takase, Kunikatsu

    2008-05-01

    Our study aims at the design and implementation of a general controller for quadruped locomotion, allowing the robot to use the whole range of quadrupedal gaits (i.e. from low speed walking to fast running). A general legged locomotion controller must integrate both posture control and rhythmic motion control and have the ability to shift continuously from one control method to the other according to locomotion speed. We are developing such a general quadrupedal locomotion controller by using a neural model involving a CPG (Central Pattern Generator) utilizing ground reaction force sensory feedback. We used a biologically faithful musculoskeletal model with a spine and hind legs, and computationally simulated stable stepping motion at various speeds using the neuro-mechanical system combining the neural controller and the musculoskeletal model. We compared the changes of the most important locomotion characteristics (stepping period, duty ratio and support length) according to speed in our simulations with the data on real cat walking. We found similar tendencies for all of them. In particular, the swing period was approximately constant while the stance period decreased with speed, resulting in a decreasing stepping period and duty ratio. Moreover, the support length increased with speed due to the posterior extreme position that shifted progressively caudally, while the anterior extreme position was approximately constant. This indicates that we succeeded in reproducing to some extent the motion of a cat from the kinematical point of view, even though we used a 2D bipedal model. We expect that such computational models will become essential tools for legged locomotion neuroscience in the future.

  12. Remote Neural Pendants In A Welding-Control System

    Science.gov (United States)

    Venable, Richard A.; Bucher, Joseph H.

    1995-01-01

    Neural network integrated circuits enhance functionalities of both remote terminals (called "pendants") and communication links, without necessitating installation of additional wires in links. Makes possible to incorporate many features into pendant, including real-time display of critical welding parameters and other process information, capability for communication between technician at pendant and host computer or technician elsewhere in system, and switches and potentiometers through which technician at pendant exerts remote control over such critical aspects of welding process as current, voltage, rate of travel, flow of gas, starting, and stopping. Other potential manufacturing applications include control of spray coating and of curing of composite materials. Potential nonmanufacturing uses include remote control of heating, air conditioning, and lighting in electrically noisy and otherwise hostile environments.

  13. Optimized biogas-fermentation by neural network control.

    Science.gov (United States)

    Holubar, P; Zani, L; Hager, M; Fröschl, W; Radak, Z; Braun, R

    2003-01-01

    In this work several feed-forward back-propagation neural networks (FFBP) were trained in order to model, and subsequently control, methane production in anaerobic digesters. To produce data for the training of the neural nets, four anaerobic continuous stirred tank reactors (CSTR) were operated in steady-state conditions at organic loading rates (Br) of about 2 kg x m(-3) x d(-1) chemical oxygen demand (COD), and disturbed by pulse-like increase of the organic loading rate. For the pulses additional carbon sources were added to the basic feed (surplus- and primary sludge) to simulate cofermentation and to increase the COD. Measured parameters were: gas composition, methane production rate, volatile fatty acid concentration, pH, redox potential, volatile suspended solids and COD of feed and effluent. A hierarchical system of neural nets was developed and embedded in a Decision Support System (DSS). A 3-3-1 FFBP simulated the pH with a regression coefficient of 0.82. A 9-3-3 FFBP simulated the volatile fatty acid concentration in the sludge with a regression coefficient of 0.86. And a 9-3-2 FFBP simulated the gas production and gas composition with a regression coefficient of 0.90 and 0.80 respectively. A lab-scale anaerobic CSTR controlled by this tool was able to maintain a methane concentration of about 60% at a rather high gas production rate of between 5 to 5.6 m3 x m(-3) x d(-1).

  14. 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.

  15. Percutaneous autonomic neural modulation: A novel technique to treat cardiac arrhythmia

    Energy Technology Data Exchange (ETDEWEB)

    DeSimone, Christopher V.; Madhavan, Malini [Cardiovascular Diseases, Department of Medicine, Mayo Clinic, Rochester, MN (United States); Venkatachalam, Kalpathi L. [Cardiovascular Diseases, Department of Medicine, Mayo Clinic, Jacksonville, FL (United States); Knudson, Mark B. [Mayo Clinic, Rochester, MN (United States); EnteroMedics, EnteroMedics, St. Paul, MN (United States); Asirvatham, Samuel J., E-mail: asirvatham.samuel@mayo.edu [Cardiovascular Diseases, Department of Medicine, Mayo Clinic, Rochester, MN (United States); Pediatric Cardiology, Department of Pediatric and Adolescent Medicine, Mayo Clinic, Rochester, MN (United States)

    2013-05-15

    Ablation and anti-arrhythmic medications have shown promise but have been met with varying success and unwanted side effects such as myocardial injury, arrhythmias, and morbidity from invasive surgical intervention. The answer to improving efficacy of ablation may include modulation of the cardiac aspect of the autonomic nervous system. Our lab has developed a novel approach and device to navigate the oblique sinus and to use DC current and saline/alcohol irrigation to selectively stimulate and block the autonomic ganglia found on the epicardial side of the heart. This novel approach minimizes myocardial damage from thermal injury and provides a less invasive and targeted approach. For feasibility, proof-of-concept, and safety monitoring, we carried out canine studies to test this novel application. Our results suggest a safer and less invasive way of modulating arrhythmogenic substrate that may lead to improved treatment of AF in humans.

  16. Cardiac autonomic control in the obstructive sleep apnea

    Directory of Open Access Journals (Sweden)

    Nouha Gammoudi

    2015-04-01

    Full Text Available Introduction: The sympathetic activation is considered to be the main mechanism involved in the development of cardiovascular diseases in obstructive sleep apnea (OSA. The heart rate variability (HRV analysis represents a non-invasive tool allowing the study of the autonomic nervous system. The impairment of HRV parameters in OSA has been documented. However, only a few studies tackled the dynamics of the autonomic nervous system during sleep in patients having OSA. Aims: To analyze the HRV over sleep stages and across sleep periods in order to clarify the impact of OSA on cardiac autonomic modulation. The second objective is to examine the nocturnal HRV of OSA patients to find out which HRV parameter is the best to reflect the symptoms severity. Methods: The study was retrospective. We have included 30 patients undergoing overnight polysomnography. Subjects were categorized into two groups according to apnea–hypopnea index (AHI: mild-to-moderate OSAS group (AHI: 5–30 and severe OSAS group (AHI>30. The HRV measures for participants with low apnea–hypopnea indices were compared to those of patients with high rates of apnea–hypopnea across the sleep period and sleep stages. Results: HRV measures during sleep stages for the group with low rates of apnea–hypopnea have indicated a parasympathetic activation during non-rapid eye movement (NREM sleep. However, no significant difference has been observed in the high AHI group except for the mean of RR intervals (mean RR. The parasympathetic activity tended to increase across the night but without a statistical difference. After control of age and body mass index, the most significant correlation found was for the mean RR (p=0.0001, r=−0.248. Conclusion: OSA affects sympathovagal modulation during sleep, and this impact has been correlated to the severity of the disease. The mean RR seemed to be a better index allowing the sympathovagal balance appreciation during the night in OSA.

  17. Steroids In caRdiac Surgery (SIRS) trial: acute kidney injury substudy protocol of an international randomised controlled trial

    National Research Council Canada - National Science Library

    Garg, Amit X; Vincent, Jessica; Cuerden, Meaghan; Parikh, Chirag; Devereaux, P J; Teoh, Kevin; Yusuf, Salim; Hildebrand, Ainslie; Lamy, Andre; Zuo, Yunxia; Sessler, Daniel I; Shah, Pallav; Abbasi, Seyed Hesameddin; Quantz, Mackenzie; Yared, Jean-Pierre; Noiseux, Nicolas; Tagarakis, Georgios; Rochon, Antoine; Pogue, Janice; Walsh, Michael; Chan, Matthew T V; Lamontagne, Francois; Salehiomran, Abbas; Whitlock, Richard

    2014-01-01

    Steroids In caRdiac Surgery trial (SIRS) is a large international randomised controlled trial of methylprednisolone or placebo in patients undergoing cardiac surgery with the use of a cardiopulmonary bypass pump...

  18. Robust Adaptive Neural Sliding Mode Approach for Tracking Control of a MEMS Triaxial Gyroscope

    Directory of Open Access Journals (Sweden)

    Juntao Fei

    2012-05-01

    Full Text Available In this paper, a neural network adaptive sliding mode control is proposed for an MEMS triaxial gyroscope with unknown system nonlinearities. An input‐output linearization technique is incorporated into the neural adaptive tracking control to cancel the nonlinearities, and the neural network whose parameters are updated from the Lyapunov approach is used to perform the linearization control law. The sliding mode control is utilized to\tcompensate the neural network’s approximation errors. The stability of the closed‐loop system can be guaranteed with the proposed adaptive neural sliding mode control. Numerical simulations are investigated to verify the effectiveness of the proposed adaptive neural sliding mode control scheme.

  19. Neural network modeling and control of proton exchange membrane fuel cell

    Institute of Scientific and Technical Information of China (English)

    CHEN Yue-hua; CAO Guang-yi; ZHU Xin-jian

    2007-01-01

    A neural network model and fuzzy neural network controller was designed to control the inner impedance of a proton exchange membrane fuel cell(PEMFC)stack. A radial basis function(RBF)neural network model was trained by the input-output data of impedance. A fuzzy neural network controller Was designed to control the impedance response.The RBF neural network model was used to test the fuzzy neural network controller.The results show that the RBF model output Can imitate actual output well, themaximal errorisnotbeyond 20 mΩ, thetrainingtime is about 1 s by using 20 neurons, and the mean squared errors is 141.9 mΩ2.The impedance of the PEMFC stack is controlled within the optimum range when the load changes, and the adjustive time is ahnllt 3 rain.

  20. 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

  1. ARTIFICIAL NEURAL NETWORK AND FUZZY LOGIC CONTROLLER FOR GTAW MODELING AND CONTROL

    Institute of Scientific and Technical Information of China (English)

    2002-01-01

    An artificial neural network(ANN) and a self-adjusting fuzzy logic controller(FLC) for modeling and control of gas tungsten arc welding(GTAW) process are presented. The discussion is mainly focused on the modeling and control of the weld pool depth with ANN and the intelligent control for weld seam tracking with FLC. The proposed neural network can produce highly complex nonlinear multi-variable model of the GTAW process that offers the accurate prediction of welding penetration depth. A self-adjusting fuzzy controller used for seam tracking adjusts the control parameters on-line automatically according to the tracking errors so that the torch position can be controlled accurately.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. Fuzzy neural networks for arc welding quality control

    Institute of Scientific and Technical Information of China (English)

    2000-01-01

    Fuzzy Logic Control (FLC) is a promising control strategy in welding process control due to its ability for solving control problem with uncertainty as well as its independence on the analytical mathematics model. However, in basic FLC, the fuzzy rule relies heavily on the experts' (e.g. advanced welders') experience. In addition to this, the membership function for fuzzy set is non-adaptive, i.e. it remains unchanged as long as they are determined by experience or other means. For welding process, which is time-variable systems and strong disturbance exists in it, fixed membership function may not guarantee the required system performance, and attempts should be made to improve the system performance by adopting adaptive membership function. Therefore, the automatically determination of the fuzzy rule and in-process adaptation of membership function are required for the advanced welding process control. This paper discussed the possibility by using the combination between FLC and neural network (NN) to realize the above propose. The adaptation of membership function as well as the self-organizing of fuzzy rule are realized by the self-learning and competitiveness of the NN. Taking GTAW process welds bead width regulating system as the controlled plant, the proposed algorithm was testified for such a process. Computer simulations showed the improvement of the system characteristics.

  7. Biomimetic Hybrid Feedback Feedforward Neural-Network Learning Control.

    Science.gov (United States)

    Pan, Yongping; Yu, Haoyong

    2017-06-01

    This brief presents a biomimetic hybrid feedback feedforward neural-network learning control (NNLC) strategy inspired by the human motor learning control mechanism for a class of uncertain nonlinear systems. The control structure includes a proportional-derivative controller acting as a feedback servo machine and a radial-basis-function (RBF) NN acting as a feedforward predictive machine. Under the sufficient constraints on control parameters, the closed-loop system achieves semiglobal practical exponential stability, such that an accurate NN approximation is guaranteed in a local region along recurrent reference trajectories. Compared with the existing NNLC methods, the novelties of the proposed method include: 1) the implementation of an adaptive NN control to guarantee plant states being recurrent is not needed, since recurrent reference signals rather than plant states are utilized as NN inputs, which greatly simplifies the analysis and synthesis of the NNLC and 2) the domain of NN approximation can be determined a priori by the given reference signals, which leads to an easy construction of the RBF-NNs. Simulation results have verified the effectiveness of this approach.

  8. Neural Network Controllers in DTC of Synchronous Motor Drives

    Directory of Open Access Journals (Sweden)

    Sudhakar Ambarapu

    2013-07-01

    Full Text Available In recent times, permanent magnet synchronous motors (PMSM have gained numerous industrial applications, because of simple structure, high efficiency and ease of maintenance. But these motors have a nonlinear mathematical model. To resolve this problem several studies have suggested the application of vector control (VC and direct torque control (DTC with soft-computing (SC techniques. This paper presents neuro direct torque control (NDTC of PMSM. Hence this paper aims to present a technique to control speed and torque with reduced ripple compared to previous techniques. The outputs of Artificial Neural Network(ANN controller mechanism is compared with that of classical DTC and the results demonstrate the influence of ANN is improved compared to classical DTC topology. The system is also verified and proved to be operated stably with reduced torque ripple, very low speed, sudden speed reversals, at low torque and at high torque. The proposed method validity and effectiveness has been verified by computer simulations using Matlab/Simulink®. These results are compared with the ones obtained with a classical DTC using PI speed controller.

  9. Dofetilide: a new drug to control cardiac arrhythmia

    DEFF Research Database (Denmark)

    Elming, Hanne; Brendorp, Bente; Pedersen, Ole Dyg

    2003-01-01

    Atrial fibrillation (AF) is the most common cardiac arrhythmia. Mortality, and especially morbidity caused by AF, are major and growing health problems in the western world. AF is strongly associated with arterial hypertension, congestive heart failure, valvular heart disease, ischaemic heart...

  10. Interline power flow controller (IPFC) based damping recurrent neural network controllers for enhancing stability

    Energy Technology Data Exchange (ETDEWEB)

    Banaei, M.R., E-mail: m.banaei@azaruniv.ed [Electrical Engineering Department, Faculty of Engineering, Azarbaijan University of Tarbiat Moallem, Tabriz (Iran, Islamic Republic of); Kami, A. [Electrical Engineering Department, Faculty of Engineering, Azarbaijan University of Tarbiat Moallem, Tabriz (Iran, Islamic Republic of)

    2011-07-15

    Highlights: {yields} A method is presented to improve power system stability using IPFC. {yields} Recurrent neural network controllers damp oscillations in a power system. {yields} Training is based on back propagation with adaptive training parameters. {yields} Selection of effectiveness damping control signal carried out using SVD method. -- Abstract: This paper presents a method to improve power system stability using IPFC based damping online learning recurrent neural network controllers for damping oscillations in a power system. Parameters of equipped controllers for enhancing dynamical stability at the IPFC are tuned using mathematical methods. Therefore these control parameters are often fixed and are set for particular system configurations or operating points. Multilayer recurrent neural network, which can be tuned for changing system conditions, is used in this paper for effectively damp the oscillations. Training is based on back propagation with adaptive training parameters. This controller is tested to variations in system loading and fault in the power system and its performance is compared with performance of a controller that the phase compensation method is used to set its parameters. Selection of effectiveness damping control signal for the design of robust IPFC damping controller carried out through singular value decomposition (SVD) method. Simulation studies show the superior robustness and stabilizing effect of the proposed controller in comparison with phase compensation method.

  11. Induced pluripotent stem cells and their use in cardiac and neural regenerative medicine.

    Science.gov (United States)

    Skalova, Stepanka; Svadlakova, Tereza; Shaikh Qureshi, Wasay Mohiuddin; Dev, Kapil; Mokry, Jaroslav

    2015-02-13

    Stem cells are unique pools of cells that are crucial for embryonic development and maintenance of adult tissue homeostasis. The landmark Nobel Prize winning research by Yamanaka and colleagues to induce pluripotency in somatic cells has reshaped the field of stem cell research. The complications related to the usage of pluripotent embryonic stem cells (ESCs) in human medicine, particularly ESC isolation and histoincompatibility were bypassed with induced pluripotent stem cell (iPSC) technology. The human iPSCs can be used for studying embryogenesis, disease modeling, drug testing and regenerative medicine. iPSCs can be diverted to different cell lineages using small molecules and growth factors. In this review we have focused on iPSC differentiation towards cardiac and neuronal lineages. Moreover, we deal with the use of iPSCs in regenerative medicine and modeling diseases like myocardial infarction, Timothy syndrome, dilated cardiomyopathy, Parkinson's, Alzheimer's and Huntington's disease. Despite the promising potential of iPSCs, genome contamination and low efficacy of cell reprogramming remain significant challenges.

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

    NARCIS (Netherlands)

    Velde, van der 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

  13. 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

  14. Application of Neural network PID Controller in Constant Temperature and Constant Liquid-level System

    Institute of Scientific and Technical Information of China (English)

    Chen,Guochu; Zhang,Lin; Hao,Ninmei; Liu,Xianguang; Wang,Junhong

    2003-01-01

    Guided by the principle of neural network, an intelligent PID controller based on neural network is devised and applied to control of constant temperature and constant liquidlevel system. The experiment results show that this controller has high accuracy and strong robustness and good characters.

  15. 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…

  16. 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.

  17. Synchronization Control of Neural Networks With State-Dependent Coefficient Matrices.

    Science.gov (United States)

    Zhang, Junfeng; Zhao, Xudong; Huang, Jun

    2016-11-01

    This brief is concerned with synchronization control of a class of neural networks with state-dependent coefficient matrices. Being different from the existing drive-response neural networks in the literature, a novel model of drive-response neural networks is established. The concepts of uniformly ultimately bounded (UUB) synchronization and convex hull Lyapunov function are introduced. Then, by using the convex hull Lyapunov function approach, the UUB synchronization design of the drive-response neural networks is proposed, and a delay-independent control law guaranteeing the bounded synchronization of the neural networks is constructed. All present conditions are formulated in terms of bilinear matrix inequalities. By comparison, it is shown that the neural networks obtained in this brief are less conservative than those ones in the literature, and the bounded synchronization is suitable for the novel drive-response neural networks. Finally, an illustrative example is given to verify the validity of the obtained results.

  18. Neural control of small intestinal giant migrating contractions.

    Science.gov (United States)

    Otterson, M F; Sarna, S K

    1994-04-01

    We investigated the neural mechanisms of control of giant migrating contractions (GMCs) in five conscious dogs. After control recordings, a Thiry-Vella loop was prepared from the middle segment, and the remaining two segments were reanastomosed. GMCs were stimulated by intravenous administration of fentanyl and erythromycin lactobionate, oral administration of loperamide and erythromycin stearate, and gastric or intraluminal administration of cider vinegar in the loop. In the intact state, the agents stimulated GMCs in all three segments, and they propagated uninterruptedly from the point of their origin to the terminal ileum. The propagation velocity of GMCs increased, whereas that of migrating motor complexes (MMCs) decreased distally. After Thiry-Vella loop formation, the agents stimulated GMCs independently in the three segments, and they propagated only to the end of the segment in which they started. In the intact small intestine, the GMCs produced ascending and descending inhibition of spontaneous phase II contractions but did not interrupt the caudad propagation of the ongoing MMC. After Thiry-Vella loop formation, the ascending inhibition was unaltered, but the descending inhibition occurred only in the segment containing the GMC. We conclude that the propagation of GMCs in the small intestine is controlled by the enteric nerves. The extrinsic nerves control the ascending inhibition produced by GMCs, whereas the enteric nerves control the descending inhibition.

  19. Neural net robot controller with guaranteed tracking performance.

    Science.gov (United States)

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

    1995-01-01

    A neural net (NN) controller for a general serial-link robot arm is developed. The NN has two layers so that linearity in the parameters holds, but the "net functional reconstruction error" and robot disturbance input are taken as nonzero. The structure of the NN controller is derived using a filtered error/passivity approach, leading to new NN passivity properties. Online weight tuning algorithms including a correction term to backpropagation, plus an added robustifying signal, guarantee tracking as well as bounded NN weights. The NN controller structure has an outer tracking loop so that the NN weights are conveniently initialized at zero, with learning occurring online in real-time. It is shown that standard backpropagation, when used for real-time closed-loop control, can yield unbounded NN weights if (1) the net cannot exactly reconstruct a certain required control function or (2) there are bounded unknown disturbances in the robot dynamics. The role of persistency of excitation is explored.

  20. Adaptive fuzzy-neural-network control for maglev transportation system.

    Science.gov (United States)

    Wai, Rong-Jong; Lee, Jeng-Dao

    2008-01-01

    A magnetic-levitation (maglev) transportation system including levitation and propulsion control is a subject of considerable scientific interest because of highly nonlinear and unstable behaviors. In this paper, the dynamic model of a maglev transportation system including levitated electromagnets and a propulsive linear induction motor (LIM) based on the concepts of mechanical geometry and motion dynamics is developed first. Then, a model-based sliding-mode control (SMC) strategy is introduced. In order to alleviate chattering phenomena caused by the inappropriate selection of uncertainty bound, a simple bound estimation algorithm is embedded in the SMC strategy to form an adaptive sliding-mode control (ASMC) scheme. However, this estimation algorithm is always a positive value so that tracking errors introduced by any uncertainty will cause the estimated bound increase even to infinity with time. Therefore, it further designs an adaptive fuzzy-neural-network control (AFNNC) scheme by imitating the SMC strategy for the maglev transportation system. In the model-free AFNNC, online learning algorithms are designed to cope with the problem of chattering phenomena caused by the sign action in SMC design, and to ensure the stability of the controlled system without the requirement of auxiliary compensated controllers despite the existence of uncertainties. The outputs of the AFNNC scheme can be directly supplied to the electromagnets and LIM without complicated control transformations for relaxing strict constrains in conventional model-based control methodologies. The effectiveness of the proposed control schemes for the maglev transportation system is verified by numerical simulations, and the superiority of the AFNNC scheme is indicated in comparison with the SMC and ASMC strategies.

  1. 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.)

  2. Neural network setpoint control of an advanced test reactor experiment loop simulation

    Energy Technology Data Exchange (ETDEWEB)

    Cordes, G.A.; Bryan, S.R.; Powell, R.H.; Chick, D.R.

    1990-09-01

    This report describes the design, implementation, and application of artificial neural networks to achieve temperature and flow rate control for a simulation of a typical experiment loop in the Advanced Test Reactor (ATR) located at the Idaho National Engineering Laboratory (INEL). The goal of the project was to research multivariate, nonlinear control using neural networks. A loop simulation code was adapted for the project and used to create a training set and test the neural network controller for comparison with the existing loop controllers. The results for three neural network designs are documented and compared with existing loop controller action. The neural network was shown to be as accurate at loop control as the classical controllers in the operating region represented by the training set. 9 refs., 28 figs., 2 tabs.

  3. Research on the controller of an arc welding process based on a PID neural network

    Institute of Scientific and Technical Information of China (English)

    Kuanfang HE; Shisheng HUANG

    2008-01-01

    A controller based on a PID neural network(PIDNN)is proposed for an arc welding power source whose output characteristic in responding to a given value is quickly and intelligently controlled in the welding process.The new method syncretizes the PID control strategy and neural network to control the welding process intelligently,so it has the merit of PID control rules and the trait of better information disposal ability of the neural network.The results of simulation show that the controller has the properties of quick response,low overshoot quick convergence and good stable accuracy,which meet the requirements for control of the welding process.

  4. Transient Air-Fuel Ratio Control in a CNG Engine Using Fuzzy Neural Networks

    Institute of Scientific and Technical Information of China (English)

    LI Guo-xiu; ZHANG Xin

    2005-01-01

    The fuzzy neural networks has been used as means of precisely controlling the air-fuel ratio of a lean-burn compressed natural gas (CNG) engine. A control algorithm, without based on engine model, has been utilized to construct a feedforward/feedback control scheme to regulate the air-fuel ratio. Using fuzzy neural networks, a fuzzy neural hybrid controller is obtained based on PI controller. The new controller, which can adjust parameters online, has been tested in transient air-fuel ratio control of a CNG engine.

  5. Reconfigurable Flight Control Design using a Robust Servo LQR and Radial Basis Function Neural Networks

    Science.gov (United States)

    Burken, John J.

    2005-01-01

    This viewgraph presentation reviews the use of a Robust Servo Linear Quadratic Regulator (LQR) and a Radial Basis Function (RBF) Neural Network in reconfigurable flight control designs in adaptation to a aircraft part failure. The method uses a robust LQR servomechanism design with model Reference adaptive control, and RBF neural networks. During the failure the LQR servomechanism behaved well, and using the neural networks improved the tracking.

  6. 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

  7. Neural Networks for Self-tuning Control Systems

    Directory of Open Access Journals (Sweden)

    A. Noriega Ponce

    2004-01-01

    Full Text Available In this paper, we presented a self-tuning control algorithm based on a three layers perceptron type neural network. The proposed algorithm is advantageous in the sense that practically a previous training of the net is not required and some changes in the set-point are generally enough to adjust the learning coefficient. Optionally, it is possible to introduce a self-tuning mechanism of the learning coefficient although by the moment it is not possible to give final conclusions about this possibility. The proposed algorithm has the special feature that the regulation error instead of the net output error is retropropagated for the weighting coefficients modifications. 

  8. Pinning-controlled synchronization of delayed neural networks with distributed-delay coupling via impulsive control.

    Science.gov (United States)

    He, Wangli; Qian, Feng; Cao, Jinde

    2017-01-01

    This paper investigates pinning synchronization of coupled neural networks with both current-state coupling and distributed-delay coupling via impulsive control. A novel impulse pinning strategy involving pinning ratio is proposed and a general criterion is derived to ensure an array of neural networks with two different topologies synchronizes with the desired trajectory. In order to handle the difficulties of high-dimension criteria, some inequality techniques and matrix decomposition methods through simultaneous diagonalization of two matrices are introduced and low-dimensional criteria are obtained. Finally, an illustrative example is given to show the effectiveness of the proposed method. Copyright © 2016 Elsevier Ltd. All rights reserved.

  9. Effect of dihydrofolate reductase gene knock-down on the expression of heart and neural crest derivatives expressed transcript 2 in zebrafish cardiac development

    Institute of Scientific and Technical Information of China (English)

    SUN Shu-na; GUI Yong-hao; WANG Yue-xiang; QIAN Lin-xi; JIANG Qiu; LIU Dong; SONG Hou-yan

    2007-01-01

    Background Folic acid is very important for embryonic development and dihydrofolate reductase is one of the key enzymes in the process of folic acid performing its biological function. Therefore, the dysfunction of dihydrofolate reductase can inhibit the function of folic acid and finally cause the developmental malformations. In this study, we observed the abnormal cardiac phenotypes in dihydrofolate reductase (DHFR) gene knock-down zebrafish embryos,investigated the effect of DHFR on the expression of heart and neural crest derivatives expressed transcript 2 (HAND2)and explored the possible mechanism of DHFR knock-down inducing zebrafish cardiac malformations.Methods Morpholino oligonucleotides were microinjected into fertilized eggs to knock down the functions of DHFR or HAND2. Full length of HAND2 mRNA which was transcribed in vitro was microinjected into fertilized eggs to overexpress HAND2. The cardiac morphologies, the heart rates and the ventricular shortening fraction were observed and recorded under the microscope at 48 hours post fertilization. Whole-mount in situ hybridization and real-time PCR were performed to detect HAND2 expression.Results DHFR or HAND2 knock-down caused the cardiac malformation in zebrafish. The expression of HAND2 was obviously reduced in DHFR knock-down embryos (P<0.05). Microinjecting HAND2 mRNA into fertilized eggs can induce HAND2 overexpression. HAND2 overexpression rescued the cardiac malformation phenotypes of DHFR knock-down embryos.Conclusions DHFR plays a crucial role in cardiac development. The down-regulation of HAND2 caused by DHFR knock-down is the possible mechanism of DHFR knock-down inducing the cardiac malformation.

  10. Control of the Coagulation Process in a Paper-mill Wastewater Treatment Process Using a Fuzzy Neural Network

    OpenAIRE

    Wan, J.-Q.; Huang, M.-Z.; Ma, Y.-W.; Guo, W. J.; Y. Wang; Zhang, H.-P.

    2010-01-01

    In this paper, an integrated neural-fuzzy process controller was developed to study the coagulation of wastewater treatment in a paper mill. In order to improve the fuzzy neural network performance, the self-learning ability embedded in the fuzzy neural network model was emphasized for improving the rule extraction performance. It proves the fuzzy neural network more effective in modeling the coagulation performance than artificial neural networks (ANN). For comparing between the fuzzy neural...

  11. Control of spiral waves and turbulent states in a cardiac model by travelling-wave perturbations

    Institute of Scientific and Technical Information of China (English)

    王鹏业; 谢平; 尹华伟

    2003-01-01

    We propose a travelling-wave perturbation method to control the spatiotemporal dynamics in a cardiac model.It is numerically demonstrated that the method can successfully suppress the wave instability(alternans in action potential duration) in the one-dimensional case and convert spiral waves and turbulent states to the normal travelling wave states in the two-dimensional case.An experimental scheme is suggested which may provide a new design for a cardiac defibrillator.

  12. Hybrid neural network fraction integral terminal sliding mode control of an Inchworm robot manipulator

    Science.gov (United States)

    Rahmani, Mehran; Ghanbari, Ahmad; Ettefagh, Mir Mohammad

    2016-12-01

    This paper proposes a control scheme based on the fraction integral terminal sliding mode control and adaptive neural network. It deals with the system model uncertainties and the disturbances to improve the control performance of the Inchworm robot manipulator. A fraction integral terminal sliding mode control applies to the Inchworm robot manipulator to obtain the initial stability. Also, an adaptive neural network is designed to approximate the system uncertainties and unknown disturbances to reduce chattering phenomena. The weight matrix of the proposed adaptive neural network can be updated online, according to the current state error information. The stability of the proposed control method is proved by Lyapunov theory. The performance of the adaptive neural network fraction integral terminal sliding mode control is compared with three other conventional controllers such as sliding mode control, integral terminal sliding mode control and fraction integral terminal sliding mode control. Simulation results show the effectiveness of the proposed control method.

  13. A neural network controller for the path tracking control of a hopping robot involving time delays.

    Science.gov (United States)

    Chaitanya, V Sree Krishna; Reddy, M Srinivas

    2006-02-01

    In this paper a hopping robot motion with offset mass is discussed. A mathematical model has been considered and an efficient single layered neural network has been developed to suit to the dynamics of the hopping robot, which ensures guaranteed tracking performance leading to the stability of the otherwise unstable system. The neural network takes advantage of the robot regressor dynamics that expresses the highly nonlinear robot dynamics in a linear form in terms of the known and unknown robot parameters. Time delays in the control mechanism play a vital role in the motion of hopping robots. The present work also enables us to estimate the maximum time delay admissible with out losing the guaranteed tracking performance. Further this neural network does not require offline training procedures. The salient features are highlighted by appropriate simulations.

  14. Control of a Uniform Step Asymmetrical 9-Level Inverter Based on Artificial Neural Network Strategy

    Directory of Open Access Journals (Sweden)

    Rachid Taleb

    2009-12-01

    Full Text Available A neural implementation of a harmonic elimination strategy for the control auniform step asymmetrical 9-level inverter is proposed and described in this paper. AMulti-Layer Perceptrons (MLP neural network is used to approximate the mappingbetween the modulation rate and the required switching angles. After learning, the neuralnetwork generates the appropriate switching angles for the inverter. This leads to a lowcomputational-cost neural controller which is therefore well suited for real-timeapplications. This neural approach is compared to the well-known Multi-Carrier Pulse-Width Modulation (MCPWM. Simulation results demonstrate the technical advantages ofthe neural implementation of the harmonic elimination strategy over the conventionalmethod for the control of an uniform step asymmetrical 9-level inverter. The approach isused to supply an asynchronous machine and results show that the neural method ensures ahighest quality torque by efficiently canceling the harmonics generated by the inverter.

  15. 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...

  16. 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

    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...... 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...

  17. 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.

  18. 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...

  19. Neural network control method for thermoelectric converters with controlled profile temperature field

    OpenAIRE

    Кочан, Орест Володимирович

    2012-01-01

    There is method of control of temperature field of thermocouple based sensor with controlled profile of temperature field (TBS with CPTF) along electrodes of main thermocouple (MTC) considered in this paper. This mentioned above method is based on neural networks. MTC measure temperature of an object directly.Stable profile of the temperature field along electrodes of MTC doesn’t allow to the heterogeneity error of thermoelectrodes of MTC appear itself. Such stability of the temperature field...

  20. P-Q decoupled control schemes using fuzzy neural networks for the unified power flow controller

    Energy Technology Data Exchange (ETDEWEB)

    Ma, Tsao-Tsung [Department of Electrical Engineering, CEECS, National United University, 1 Lien-Da, Kung-Ching Li, MiaoLi 36003 (China)

    2007-12-15

    This paper presents a new P-Q decoupled control scheme using fuzzy neural networks for the unified power flow controller (UPFC) to improve the dynamic control performance of power systems with the aim of reducing the inevitable interactions between the real and reactive power flow control parameters. In this paper, a set of equivalent controlled current and voltage sources is adopted for mathematically modeling the UPFC and the test power systems. To simplify the theoretical analysis of the control system the 3-phase description of a two-bus test power system model embedded with a UPFC is transformed into d-q components based on a synchronously rotating reference frame. For the control systems with inherent nonlinear coupling features, a feed-forward control scheme based on fuzzy neural controllers is developed to realize the decoupling control objectives. Based on the simulation results, the proposed control scheme is able to overcome the drawbacks of the traditional power flow controllers on small disturbance linearizing method. Comprehensive simulation results on the PSCAD and MATLAB programs are presented and discussed to verify the effectiveness of the proposed control scheme. (author)

  1. 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.

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

    Institute of Scientific and Technical Information of China (English)

    SUN Cheng-shun; ZHANG Jian-wu

    2005-01-01

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

  3. 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.

  4. Artificial Neural Network Based Controller for Speed Control of An Induction Motor (IM using Indirect Vector Control Method

    Directory of Open Access Journals (Sweden)

    Ashutosh Mishra

    2012-10-01

    Full Text Available

    In this paper, an implementation of intelligent controller for speed control of an induction motor (IM using indirect vector control method has been developed and analyzed in detail. The project is complete mathematical model of field orientation control (FOC induction motor is described and simulated in MATLAB for studies a 50 HP(37KW, cage type induction motor has been considered .The comparative  performance of PI, Fuzzy and Neural network control techniques have been  presented and analyzed in this work.  The present approach avoids the use of flux and speed sensor which increase the installation cost and mechanical robustness .The neural network based controller is found to be a very useful technique to obtain a high performance speed control. The scheme consist of neural network controller, reference modal, an algorithm for changing the neural network weight in order that  speed of the derive can track performance speed.  The indirect vector controlled induction motor drive involve decoupling of the stator current in to torque and flux producing components.

  5. 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.

  6. 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

  7. Chemo-mechanical control of neural stem cell differentiation

    Science.gov (United States)

    Geishecker, Emily R.

    Cellular processes such as adhesion, proliferation, and differentiation are controlled in part by cell interactions with the microenvironment. Cells can sense and respond to a variety of stimuli, including soluble and insoluble factors (such as proteins and small molecules) and externally applied mechanical stresses. Mechanical properties of the environment, such as substrate stiffness, have also been suggested to play an important role in cell processes. The roles of both biochemical and mechanical signaling in fate modification of stem cells have been explored independently. However, very few studies have been performed to study well-controlled chemo-mechanotransduction. The objective of this work is to design, synthesize, and characterize a chemo-mechanical substrate to encourage neuronal differentiation of C17.2 neural stem cells. In Chapter 2, Polyacrylamide (PA) gels of varying stiffnesses are functionalized with differing amounts of whole collagen to investigate the role of protein concentration in combination with substrate stiffness. As expected, neurons on the softest substrate were more in number and neuronal morphology than those on stiffer substrates. Neurons appeared locally aligned with an expansive network of neurites. Additional experiments would allow for statistical analysis to determine if and how collagen density impacts C17.2 differentiation in combination with substrate stiffness. Due to difficulties associated with whole protein approaches, a similar platform was developed using mixed adhesive peptides, derived from fibronectin and laminin, and is presented in Chapter 3. The matrix elasticity and peptide concentration can be individually modulated to systematically probe the effects of chemo-mechanical signaling on differentiation of C17.2 cells. Polyacrylamide gel stiffness was confirmed using rheological techniques and found to support values published by Yeung et al. [1]. Cellular growth and differentiation were assessed by cell counts

  8. Filtered-X Radial Basis Function Neural Networks for Active Noise Control

    Directory of Open Access Journals (Sweden)

    Bambang Riyanto

    2004-05-01

    Full Text Available This paper presents active control of acoustic noise using radial basis function (RBF networks and its digital signal processor (DSP real-time implementation. The neural control system consists of two stages: first, identification (modeling of secondary path of the active noise control using RBF networks and its learning algorithm, and secondly neural control of primary path based on neural model obtained in the first stage. A tapped delay line is introduced in front of controller neural, and another tapped delay line is inserted between controller neural networks and model neural networks. A new algorithm referred to as Filtered X-RBF is proposed to account for secondary path effects of the control system arising in active noise control. The resulting algorithm turns out to be the filtered-X version of the standard RBF learning algorithm. We address centralized and decentralized controller configurations and their DSP implementation is carried out. Effectiveness of the neural controller is demonstrated by applying the algorithm to active noise control within a 3 dimension enclosure to generate quiet zones around error microphones. Results of the real-time experiments show that 10-23 dB noise attenuation is produced with moderate transient response.

  9. Study on the Robot Robust Adaptive Control Based on Neural Networks

    Institute of Scientific and Technical Information of China (English)

    温淑焕; 王洪瑞; 吴丽艳

    2003-01-01

    Force control based on neural networks is presented. Under the framework of hybrid control, an RBF neural network is used to compensate for all the uncertainties from robot dynamics and unknown environment first. The technique will improve the adaptability to environment stiffness when the end-effector is in contact with the environment, and does not require any a priori knowledge on the upper bound of syste uncertainties. Moreover, it need not compute the inverse of inertia matrix. Learning algorithms for neural networks to minimize the force error directly are designed. Simulation results have shown a better force/position tracking when neural network is used.

  10. 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

    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...

  11. Tracking performance and global stability guaranteed neural control of uncertain hypersonic flight vehicle

    Directory of Open Access Journals (Sweden)

    Tao Teng

    2016-02-01

    Full Text Available In this article, a global adaptive neural dynamic surface control with predefined tracking performance is developed for a class of hypersonic flight vehicles, whose accurate dynamics is hard to obtain. The control scheme developed in this paper overcomes the limitations of neural approximation region by employing a switching mechanism which incorporates an additional robust controller outside the neural approximation region to pull the transient state variables back when they overstep the neural approximation region, such that globally uniformly ultimately bounded stability can be guaranteed. Especially, the developed global adaptive neural control also improves the tracking performance by introducing an error transformation mechanism, such that both transient and steady-state performance can be shaped according to the predefined bounds. Simulation studies on the hypersonic flight vehicle validate that the designed controller has good velocity modulation and velocity stability performance.

  12. Outcome of kidney transplantation between controlled cardiac death and brain death donors: a meta-analysis

    Institute of Scientific and Technical Information of China (English)

    Ming Yingzi; Shao Mingjie; Tian Tingting; She Xingguo; Liu Hong; Ye Shaojun; Ye Qifa

    2014-01-01

    Background Our goal was to evaluate the outcomes of kidney transplants from controlled cardiac death donors compared with brain death donors by conducting a meta-analysis of cohort studies.Methods The PubMed database and EMBASE were searched from January 1980 to July 2013 to identify studies that met pre-stated inclusion criteria.Reference lists of retrieved articles were also reviewed.Two authors independently extracted information on the designs of the studies,the characteristics of the study participants,and outcome assessments.Results Nine cohort studies involving 84 398 participants were included in this meta-analysis; 3 014 received kidneys from controlled cardiac death donors and 80 684 from brain death donors.Warm ischemia time was significantly longer for the controlled cardiac death donor group.The incidence of delayed graft function was 2.74 times (P <0.001) greater in the controlled cardiac death donor group.The results are in favor of the brain death donor group on short-term patient and graft survival while this difference became nonsignificant at mid-term and long term.Sensitivity analysis yielded similar results.No evidence of publication bias was observed.Conclusion This meta-analysis of retrospective cohort studies suggests that the outcome after controlled cardiac death donors is comparable with that obtained using kidneys from brain death donors.

  13. Insulin receptor substrate signaling controls cardiac energy metabolism and heart failure.

    Science.gov (United States)

    Guo, Cathy A; Guo, Shaodong

    2017-06-01

    The heart is an insulin-dependent and energy-consuming organ in which insulin and nutritional signaling integrates to the regulation of cardiac metabolism, growth and survival. Heart failure is highly associated with insulin resistance, and heart failure patients suffer from the cardiac energy deficiency and structural and functional dysfunction. Chronic pathological conditions, such as obesity and type 2 diabetes mellitus, involve various mechanisms in promoting heart failure by remodeling metabolic pathways, modulating cardiac energetics and impairing cardiac contractility. Recent studies demonstrated that insulin receptor substrates 1 and 2 (IRS-1,-2) are major mediators of both insulin and insulin-like growth factor-1 (IGF-1) signaling responsible for myocardial energetics, structure, function and organismal survival. Importantly, the insulin receptor substrates (IRS) play an important role in the activation of the phosphatidylinositide-3-dependent kinase (PI-3K) that controls Akt and Foxo1 signaling cascade, regulating the mitochondrial function, cardiac energy metabolism and the renin-angiotensin system. Dysregulation of this branch in signaling cascades by insulin resistance in the heart through the endocrine system promotes heart failure, providing a novel mechanism for diabetic cardiomyopathy. Therefore, targeting this branch of IRS→PI-3K→Foxo1 signaling cascade and associated pathways may provide a fundamental strategy for the therapeutic and nutritional development in control of metabolic and cardiovascular diseases. In this review, we focus on insulin signaling and resistance in the heart and the role energetics play in cardiac metabolism, structure and function. © 2017 Society for Endocrinology.

  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. Robust Neural Network Control of Electrically Driven Robot Manipulator using Backstepping Approach

    Directory of Open Access Journals (Sweden)

    Seyed Ehsan Shafiei

    2010-02-01

    Full Text Available A novel approach to neural network based tracking-control of robot manipulator including actuator dynamics is proposed by using of backstepping method. A simple two-step backstepping is considered for an nlink robotic system, and a feedforward neural controller is designed at second step where structured and unstructured uncertainties in robot dynamics and actuator model are approximated by this neural controller. Bounds of network reconstruction error and other imprecisions are estimated adaptively and for compensating them, a robust control signal is added and modified. Stability analysis is performed by the Lyapunov direct method and performance efficiency of the proposed controller is justified by the simulations.

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

    DEFF Research Database (Denmark)

    Schiøler, Henrik

    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...

  17. 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.

  18. Integrating multiple sensory systems to modulate neural networks controlling posture.

    Science.gov (United States)

    Lavrov, I; Gerasimenko, Y; Burdick, J; Zhong, H; Roy, R R; Edgerton, V R

    2015-12-01

    In this study we investigated the ability of sensory input to produce tonic responses in hindlimb muscles to facilitate standing in adult spinal rats and tested two hypotheses: 1) whether the spinal neural networks below a complete spinal cord transection can produce tonic reactions by activating different sensory inputs and 2) whether facilitation of tonic and rhythmic responses via activation of afferents and with spinal cord stimulation could engage similar neuronal mechanisms. We used a dynamically controlled platform to generate vibration during weight bearing, epidural stimulation (at spinal cord level S1), and/or tail pinching to determine the postural control responses that can be generated by the lumbosacral spinal cord. We observed that a combination of platform displacement, epidural stimulation, and tail pinching produces a cumulative effect that progressively enhances tonic responses in the hindlimbs. Tonic responses produced by epidural stimulation alone during standing were represented mainly by monosynaptic responses, whereas the combination of epidural stimulation and tail pinching during standing or epidural stimulation during stepping on a treadmill facilitated bilaterally both monosynaptic and polysynaptic responses. The results demonstrate that tonic muscle activity after complete spinal cord injury can be facilitated by activation of specific combinations of afferent inputs associated with load-bearing proprioception and cutaneous input in the presence of epidural stimulation and indicate that whether activation of tonic or rhythmic responses is generated depends on the specific combinations of sources and types of afferents activated in the hindlimb muscles.

  19. 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.

  20. 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.

  1. 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

  2. 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 network,

  3. SOFM Neural Network Based Hierarchical Topology Control for Wireless Sensor Networks

    OpenAIRE

    2014-01-01

    Well-designed network topology provides vital support for routing, data fusion, and target tracking in wireless sensor networks (WSNs). Self-organization feature map (SOFM) neural network is a major branch of artificial neural networks, which has self-organizing and self-learning features. In this paper, we propose a cluster-based topology control algorithm for WSNs, named SOFMHTC, which uses SOFM neural network to form a hierarchical network structure, completes cluster head selection by the...

  4. Robust adaptive backstepping neural networks control for spacecraft rendezvous and docking with input saturation.

    Science.gov (United States)

    Xia, Kewei; Huo, Wei

    2016-05-01

    This paper presents a robust adaptive neural networks control strategy for spacecraft rendezvous and docking with the coupled position and attitude dynamics under input saturation. Backstepping technique is applied to design a relative attitude controller and a relative position controller, respectively. The dynamics uncertainties are approximated by radial basis function neural networks (RBFNNs). A novel switching controller consists of an adaptive neural networks controller dominating in its active region combined with an extra robust controller to avoid invalidation of the RBFNNs destroying stability of the system outside the neural active region. An auxiliary signal is introduced to compensate the input saturation with anti-windup technique, and a command filter is employed to approximate derivative of the virtual control in the backstepping procedure. Globally uniformly ultimately bounded of the relative states is proved via Lyapunov theory. Simulation example demonstrates effectiveness of the proposed control scheme.

  5. 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.

  6. Pharyngeal mesoderm regulatory network controls cardiac and head muscle morphogenesis.

    Science.gov (United States)

    Harel, Itamar; Maezawa, Yoshiro; Avraham, Roi; Rinon, Ariel; Ma, Hsiao-Yen; Cross, Joe W; Leviatan, Noam; Hegesh, Julius; Roy, Achira; Jacob-Hirsch, Jasmine; Rechavi, Gideon; Carvajal, Jaime; Tole, Shubha; Kioussi, Chrissa; Quaggin, Susan; Tzahor, Eldad

    2012-11-13

    The search for developmental mechanisms driving vertebrate organogenesis has paved the way toward a deeper understanding of birth defects. During embryogenesis, parts of the heart and craniofacial muscles arise from pharyngeal mesoderm (PM) progenitors. Here, we reveal a hierarchical regulatory network of a set of transcription factors expressed in the PM that initiates heart and craniofacial organogenesis. Genetic perturbation of this network in mice resulted in heart and craniofacial muscle defects, revealing robust cross-regulation between its members. We identified Lhx2 as a previously undescribed player during cardiac and pharyngeal muscle development. Lhx2 and Tcf21 genetically interact with Tbx1, the major determinant in the etiology of DiGeorge/velo-cardio-facial/22q11.2 deletion syndrome. Furthermore, knockout of these genes in the mouse recapitulates specific cardiac features of this syndrome. We suggest that PM-derived cardiogenesis and myogenesis are network properties rather than properties specific to individual PM members. These findings shed new light on the developmental underpinnings of congenital defects.

  7. Direct cardiac potential trigger for chronic control of a ventricular assist device.

    Science.gov (United States)

    Kitamura, M; Hanzawa, K; Aoki, K; Saitoh, M; Hayashi, J

    2001-01-01

    As a new trigger method for chronic drive control of a ventricular assist device (VAD), a direct cardiac potential trigger was assessed under various conditions in a chronic experimental model. A pneumatic pulsatile VAD was implanted as circulatory support between the left ventricular apex and the ascending aorta in 12 adult pigs. Hemodynamic parameters and pump output were continuously monitored. Two tips of a bipolar electrode were set on the RV anterior wall and the LV posterior wall for recording direct cardiac potential. Counterpulsation drive of the VAD was applied by using the R wave in a standard electrocardiogram (ECG) or the direct cardiac potential as an ECG trigger. As special conditions, various artifacts on ECG, electromusculogram, arrhythmia, irregular ventilation, and passive vibration (simulation of exercise) were set for assessing the ECG trigger modes. Artifacts of irregular ventilation and passive vibration made the drive control poor using a standard ECG trigger. In contrast, the direct cardiac potential trigger maintained the counterpulsation control of the VAD well in all conditions of this study, and was a safe and reliable support for the native heart. It also supported animals for up to 48 hours after operation. The above results suggested that the direct cardiac potential trigger might be useful for monitoring native heart beats and adjusting the support cycle to the native heart cycle as a chronic control method for various VADs.

  8. Synchronization criteria for generalized reaction-diffusion neural networks via periodically intermittent control.

    Science.gov (United States)

    Gan, Qintao; Lv, Tianshi; Fu, Zhenhua

    2016-04-01

    In this paper, the synchronization problem for a class of generalized neural networks with time-varying delays and reaction-diffusion terms is investigated concerning Neumann boundary conditions in terms of p-norm. The proposed generalized neural networks model includes reaction-diffusion local field neural networks and reaction-diffusion static neural networks as its special cases. By establishing a new inequality, some simple and useful conditions are obtained analytically to guarantee the global exponential synchronization of the addressed neural networks under the periodically intermittent control. According to the theoretical results, the influences of diffusion coefficients, diffusion space, and control rate on synchronization are analyzed. Finally, the feasibility and effectiveness of the proposed methods are shown by simulation examples, and by choosing different diffusion coefficients, diffusion spaces, and control rates, different controlled synchronization states can be obtained.

  9. Batch Process Modelling and Optimal Control Based on Neural Network Models

    Institute of Scientific and Technical Information of China (English)

    Jie Zhang

    2005-01-01

    This paper presents several neural network based modelling, reliable optimal control, and iterative learning control methods for batch processes. In order to overcome the lack of robustness of a single neural network, bootstrap aggregated neural networks are used to build reliable data based empirical models. Apart from improving the model generalisation capability, a bootstrap aggregated neural network can also provide model prediction confidence bounds. A reliable optimal control method by incorporating model prediction confidence bounds into the optimisation objective function is presented. A neural network based iterative learning control strategy is presented to overcome the problem due to unknown disturbances and model-plant mismatches. The proposed methods are demonstrated on a simulated batch polymerisation process.

  10. ADAPTIVE FLIGHT CONTROL SYSTEM OF ARMED HELICOPTER USING WAVELET NEURAL NETWORK METHOD

    Institute of Scientific and Technical Information of China (English)

    ZHURong-gang; JIANGChangsheng; FENGBin

    2004-01-01

    A discussion is devoted to the design of an adaptive flight control system of the armed helicopter using wavelet neural network method. Firstly, the control loop of the attitude angle is designed with a dynamic inversion scheme in a quick loop and a slow loop. respectively. Then, in order to compensate the error caused by dynamic inversion, the adaptive flight control system of the armed helicopter using wavelet neural network method is put forward, so the BP wavelet neural network and the Lyapunov stable wavelet neural network are used to design the helicopter flight control system. Finally, the typical maneuver flight is simulated to demonstrate its validity and effectiveness. Result proves that the wavelet neural network has an engineering practical value and the effect of WNN is good.

  11. Glycemic control in cardiac surgery: implementing an evidence-based insulin infusion protocol.

    Science.gov (United States)

    Hargraves, Joelle D

    2014-05-01

    Acute hyperglycemia following cardiac surgery increases the risk of deep sternal wound infection, significant early morbidity, and mortality. Insulin infusion protocols that target tight glycemic control to treat hyperglycemia have been linked to hypoglycemia and increased mortality. Recently published studies examining glycemic control in critical illness and clinical practice guidelines from professional organizations support moderate glycemic control. To measure critical care nurses' knowledge of glycemic control in cardiac surgery before and after education. To evaluate the safety and effectiveness of an evidence-based insulin infusion protocol targeting moderate glycemic control in cardiac surgery patients. This evidence-based practice change was implemented in the cardiovascular unit in a community teaching hospital. Nurses completed a self-developed questionnaire to measure knowledge of glycemic control. Blood glucose data, collected (retrospectively) from anesthesia end time through 11:59 PM on postoperative day 2, were compared from 2 months before to 2 months after the practice change. Nurses' knowledge (test scores) increased significantly after education (pretest mean = 53.10, SD = 11.75; posttest mean = 79.10, SD = 12.02; t54 = -8.18, P nurses' knowledge of glycemic control and implementing an insulin infusion protocol targeting moderate glycemic control were effective for treating acute hyperglycemia following cardiac surgery with decreased incidence of hypoglycemia.

  12. AN INTELLIGENT CONTROL SYSTEM BASED ON RECURRENT NEURAL FUZZY NETWORK AND ITS APPLICATION TO CSTR

    Institute of Scientific and Technical Information of China (English)

    JIA Li; YU Jinshou

    2005-01-01

    In this paper, an intelligent control system based on recurrent neural fuzzy network is presented for complex, uncertain and nonlinear processes, in which a recurrent neural fuzzy network is used as controller (RNFNC) to control a process adaptively and a recurrent neural network based on recursive predictive error algorithm (RNNM) is utilized to estimate the gradient information (ey)/(e)u for optimizing the parameters of controller.Compared with many neural fuzzy control systems, it uses recurrent neural network to realize the fuzzy controller. Moreover, recursive predictive error algorithm (RPE) is implemented to construct RNNM on line. Lastly, in order to evaluate the performance of theproposed control system, the presented control system is applied to continuously stirred tank reactor (CSTR). Simulation comparisons, based on control effect and output error,with general fuzzy controller and feed-forward neural fuzzy network controller (FNFNC),are conducted. In addition, the rates of convergence of RNNM respectively using RPE algorithm and gradient learning algorithm are also compared. The results show that the proposed control system is better for controlling uncertain and nonlinear processes.

  13. Brushless DC Motor Drive during Speed Regulation with Artificial Neural Network Controller

    OpenAIRE

    Sakshi Solanki

    2016-01-01

    Brushless DC motor, at this moment is extensively used being many industrial functions due to the different features like high efficiency and dynamic response and high speed range. This paper is proposing a technology named as Artificial Neural Network controller to control the speed of the brushless DC motor. Here the paper contributes an analysis of performance Artificial Neural Network controller. Because it is difficult to handle by the use of conventional PID controller as BL...

  14. A hyperstable neural network for the modelling and control of nonlinear systems

    Indian Academy of Sciences (India)

    K Warwick; Q M Zhu; Z Ma

    2000-04-01

    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 uncertainties of the system are identified on-line by a neural network. The identified results are taken as compensation signals such that the robust adaptive control of nonlinear systems is realised. Simulation results are given.

  15. Adaptive control of chaotic systems based on a single layer neural network

    Energy Technology Data Exchange (ETDEWEB)

    Shen Liqun [Space Control and Inertia Technology Research Center, Harbin Institute of Technology, Harbin 150001 (China)], E-mail: liqunshen@gmail.com; Wang Mao [Space Control and Inertia Technology Research Center, Harbin Institute of Technology, Harbin 150001 (China)

    2007-08-27

    This Letter presents an adaptive neural network control method for the chaos control problem. Based on a single layer neural network, the dynamic about the unstable fixed period point of the chaotic system can be adaptively identified without detailed information about the chaotic system. And the controlled chaotic system can be stabilized on the unstable fixed period orbit. Simulation results of Henon map and Lorenz system verify the effectiveness of the proposed control method.

  16. Autonomic cardiac modulation in obstructive sleep apnea: effect of an oral jaw-positioning appliance

    National Research Council Canada - National Science Library

    Coruzzi, Paolo; Gualerzi, Massimo; Bernkopf, Edoardo; Brambilla, Lorenzo; Brambilla, Valerio; Broia, Vanna; Lombardi, Carolina; Parati, Gianfranco

    2006-01-01

    ... with other therapeutic approaches. The aim of our study was to investigate the responses of autonomic indexes of neural cardiac control to another type of OSA treatment based on an oral jaw-positioning appliance...

  17. Adaptive neural control for a class of perturbed strict-feedback nonlinear time-delay systems.

    Science.gov (United States)

    Wang, Min; Chen, Bing; Shi, Peng

    2008-06-01

    This paper proposes a novel adaptive neural control scheme for a class of perturbed strict-feedback nonlinear time-delay systems with unknown virtual control coefficients. Based on the radial basis function neural network online approximation capability, an adaptive neural controller is presented by combining the backstepping approach and Lyapunov-Krasovskii functionals. The proposed controller guarantees the semiglobal boundedness of all the signals in the closed-loop system and contains minimal learning parameters. Finally, three simulation examples are given to demonstrate the effectiveness and applicability of the proposed scheme.

  18. Neural signal processing and closed-loop control algorithm design for an implanted neural recording and stimulation system.

    Science.gov (United States)

    Hamilton, Lei; McConley, Marc; Angermueller, Kai; Goldberg, David; Corba, Massimiliano; Kim, Louis; Moran, James; Parks, Philip D; Sang Chin; Widge, Alik S; Dougherty, Darin D; Eskandar, Emad N

    2015-08-01

    A fully autonomous intracranial device is built to continually record neural activities in different parts of the brain, process these sampled signals, decode features that correlate to behaviors and neuropsychiatric states, and use these features to deliver brain stimulation in a closed-loop fashion. In this paper, we describe the sampling and stimulation aspects of such a device. We first describe the signal processing algorithms of two unsupervised spike sorting methods. Next, we describe the LFP time-frequency analysis and feature derivation from the two spike sorting methods. Spike sorting includes a novel approach to constructing a dictionary learning algorithm in a Compressed Sensing (CS) framework. We present a joint prediction scheme to determine the class of neural spikes in the dictionary learning framework; and, the second approach is a modified OSort algorithm which is implemented in a distributed system optimized for power efficiency. Furthermore, sorted spikes and time-frequency analysis of LFP signals can be used to generate derived features (including cross-frequency coupling, spike-field coupling). We then show how these derived features can be used in the design and development of novel decode and closed-loop control algorithms that are optimized to apply deep brain stimulation based on a patient's neuropsychiatric state. For the control algorithm, we define the state vector as representative of a patient's impulsivity, avoidance, inhibition, etc. Controller parameters are optimized to apply stimulation based on the state vector's current state as well as its historical values. The overall algorithm and software design for our implantable neural recording and stimulation system uses an innovative, adaptable, and reprogrammable architecture that enables advancement of the state-of-the-art in closed-loop neural control while also meeting the challenges of system power constraints and concurrent development with ongoing scientific research designed

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

    Science.gov (United States)

    Long, Lijun; Zhao, Jun

    2016-05-02

    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.

  20. 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.

  1. Electromyogram-based neural network control of transhumeral prostheses

    Directory of Open Access Journals (Sweden)

    Christopher L. Pulliam, MS

    2011-07-01

    Full Text Available Upper-limb amputation can cause a great deal of functional impairment for patients, particularly for those with amputation at or above the elbow. Our long-term objective is to improve functional outcomes for patients with amputation by integrating a fully implanted electromyographic (EMG recording system with a wireless telemetry system that communicates with the patient's prosthesis. We believe that this should generate a scheme that will allow patients to robustly control multiple degrees of freedom simultaneously. The goal of this study is to evaluate the feasibility of predicting dynamic arm movements (both flexion/extension and pronation/supination based on EMG signals from a set of muscles that would likely be intact in patients with transhumeral amputation. We recorded movement kinematics and EMG signals from seven muscles during a variety of movements with different complexities. Time-delayed artificial neural networks were then trained offline to predict the measured arm trajectories based on features extracted from the measured EMG signals. We evaluated the relative effectiveness of various muscle subsets. Predicted movement trajectories had average root-mean-square errors of approximately 15.7° and 24.9° and average R2 values of approximately 0.81 and 0.46 for elbow flexion/extension and forearm pronation/supination, respectively.

  2. 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.

  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...

  4. Implementation of Neural Networks for Intelligent Sensors and Control Using MATLAB

    Directory of Open Access Journals (Sweden)

    NAW KHU SAY WAH

    2015-03-01

    Full Text Available This system is concerned with the design, sensing and intelligent control of robot that moves in synchronization with the movement of the natural eye. The system deals with a path planning and intelligent control of an autonomous robot which should move safely in partially structured environment. Signal processing techniques used in sensor are studied using statistical methods and artificial neural network based techniques. Multilayer neural networks have been successfully applied as intelligent sensors for process modeling and control. This environment may involve any number of obstacles of arbitrary shape and size; some of them are allowed to move. We describe our approach to solving the motion-planning problem in mobile robot control using neural networks-based technique. Artificial neural networks application builds intelligent soft sensors to estimate variables and detect and process data screening and analysis. Our method of the construction of a collision-free path for moving robot among obstacles is based on two neural networks. The first neural network is used to determine the “free” space using ultrasound range finder data. The second neural network “finds” a safe direction for the next robot section of the path in the workspace while avoiding the nearest obstacles. Simulation examples of generated path with proposed techniques will be presented. To show the potential of the proposed neural network based framework, the system is implemented by using MATLAB.

  5. 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…

  6. Adaptive Control Law Development for Failure Compensation Using Neural Networks on a NASA F-15 Aircraft

    Science.gov (United States)

    Burken, John J.

    2005-01-01

    This viewgraph presentation covers the following topics: 1) Brief explanation of Generation II Flight Program; 2) Motivation for Neural Network Adaptive Systems; 3) Past/ Current/ Future IFCS programs; 4) Dynamic Inverse Controller with Explicit Model; 5) Types of Neural Networks Investigated; and 6) Brief example

  7. 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…

  8. Suppressing Halo-chaos for Intense Ion Beamby Neural Network Adaptation Control Strategy

    Institute of Scientific and Technical Information of China (English)

    FANGJin-qing; LUOXiao-shu; WENGJia-qiang; ZHULun-wu

    2003-01-01

    Neural network has some advantages of adaptation, learn-self, self-organization and suitable for high-dimension for various applications in many fields, especially among them the feed-forward back-propagating neural network self-adaptation method is suitable for control of nonlinear systems.

  9. A study of interceptor attitude control based on adaptive wavelet neural networks

    Science.gov (United States)

    Li, Da; Wang, Qing-chao

    2005-12-01

    This paper engages to study the 3-DOF attitude control problem of the kinetic interceptor. When the kinetic interceptor enters into terminal guidance it has to maneuver with large angles. The characteristic of interceptor attitude system is nonlinearity, strong-coupling and MIMO. A kind of inverse control approach based on adaptive wavelet neural networks was proposed in this paper. Instead of using one complex neural network as the controller, the nonlinear dynamics of the interceptor can be approximated by three independent subsystems applying exact feedback-linearization firstly, and then controllers for each subsystem are designed using adaptive wavelet neural networks respectively. This method avoids computing a large amount of the weights and bias in one massive neural network and the control parameters can be adaptive changed online. Simulation results betray that the proposed controller performs remarkably well.

  10. Temperature modeling and control of Direct Methanol Fuel Cell based on adaptive neural fuzzy technology

    Institute of Scientific and Technical Information of China (English)

    Qi Zhidong; Zhu Xinjian; Cao Guangyi

    2006-01-01

    Aiming at on-line controlling of Direct Methanol Fuel Cell (DMFC) stack, an adaptive neural fuzzy inference technology is adopted in the modeling and control of DMFC temperature system. In the modeling process, an Adaptive Neural Fuzzy Inference System (ANFIS) identification model of DMFC stack temperature is developed based on the input-output sampled data, which can avoid the internal complexity of DMFC stack. In the controlling process, with the network model trained well as the reference model of the DMFC control system, a novel fuzzy genetic algorithm is used to regulate the parameters and fuzzy rules of a neural fuzzy controller. In the simulation, compared with the nonlinear Proportional Integral Derivative (PID) and traditional fuzzy algorithm, the improved neural fuzzy controller designed in this paper gets better performance, as demonstrated by the simulation results.

  11. Chaos Synchronization Using Adaptive Dynamic Neural Network Controller with Variable Learning Rates

    Directory of Open Access Journals (Sweden)

    Chih-Hong Kao

    2011-01-01

    Full Text Available This paper addresses the synchronization of chaotic gyros with unknown parameters and external disturbance via an adaptive dynamic neural network control (ADNNC system. The proposed ADNNC system is composed of a neural controller and a smooth compensator. The neural controller uses a dynamic RBF (DRBF network to online approximate an ideal controller. The DRBF network can create new hidden neurons online if the input data falls outside the hidden layer and prune the insignificant hidden neurons online if the hidden neuron is inappropriate. The smooth compensator is designed to compensate for the approximation error between the neural controller and the ideal controller. Moreover, the variable learning rates of the parameter adaptation laws are derived based on a discrete-type Lyapunov function to speed up the convergence rate of the tracking error. Finally, the simulation results which verified the chaotic behavior of two nonlinear identical chaotic gyros can be synchronized using the proposed ADNNC scheme.

  12. Design And Analysis Of Artificial Neural Network Based Controller For Speed Control Of Induction Motor Using D T C

    Directory of Open Access Journals (Sweden)

    Kusuma Gottapu

    2014-04-01

    Full Text Available This paper presents an improved version of direct torque control (DTC based on Artificial Neural Network technique used for flux position estimation and sector selection. This controller mainly reduces the torque and flux ripples. Direct torque control of induction motor drive has quick torque response without complex orientation transformation and inner loop current control. The major problem associated with DTC drive is the high torque ripples. The important point in ANN based DTC is the right selection of voltage vector. This project presents simple structured neural network for flux position estimation and sector selection for induction motor. The Levenberg-Marquardt back propagation technique has been used to train the neural networks. The simple structure network facilitates a short training and processing times. The neural network based controller is found to be a very useful technique to obtain high performance speed control.

  13. Fuzzy Control System of Hydraulic Roll Bending Based on Genetic Neural Network

    Institute of Scientific and Technical Information of China (English)

    JIA Chun-yu; LIU Hong-min; ZHOU Hui-feng

    2005-01-01

    For nonlinear hydraulic roll bending control, a new fuzzy intelligent control method was proposed based on the genetic neural network. The method taking account of dynamic and static characteristics of control system has settled the problems of recognizing and controlling the unknown, uncertain and nonlinear system successfully,and has been applied to hydraulic roll bending control. The simulation results indicate that the system has good performance and strong robustness, and is better than traditional PID and neural-fuzzy control. The method is an effective tool to control roll bending force with increased dynamic response speed of control system and enhanced tracking accuracy.

  14. A Sliding Mode Control-based on a RBF Neural Network for Deburring Industry Robotic Systems

    OpenAIRE

    Yong Tao; Jiaqi Zheng; Yuanchang Lin

    2016-01-01

    A sliding mode control method based on radial basis function (RBF) neural network is proposed for the deburring of industry robotic systems. First, a dynamic model for deburring the robot system is established. Then, a conventional SMC scheme is introduced for the joint position tracking of robot manipulators. The RBF neural network based sliding mode control (RBFNN-SMC) has the ability to learn uncertain control actions. In the RBFNN-SMC scheme, the adaptive tuning algorithms for network par...

  15. 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...... samples from the system, after which linearized state space models are extracted from the neural network in a number of operating points according to a simple and computationally cheap scheme. Robust observer-based controllers can then be designed in each of these operating points,and gain scheduling...

  16. STUDY ON INJECTION AND IGNITION CONTROL OF GASOLINE ENGINE BASED ON BP NEURAL NETWORK

    Institute of Scientific and Technical Information of China (English)

    Zhang Cuiping; Yang Qingfo

    2003-01-01

    According to advantages of neural network and characteristics of operating procedures of engine, a new strategy is represented on the control of fuel injection and ignition timing of gasoline engine based on improved BP network algorithm. The optimum ignition advance angle and fuel injection pulse band of engine under different speed and load are tested for the samples training network, focusing on the study of the design method and procedure of BP neural network in engine injection and ignition control. The results show that artificial neural network technique can meet the requirement of engine injection and ignition control. The method is feasible for improving power performance, economy and emission performances of gasoline engine.

  17. Extended Kalman Filter Based Neural Networks Controller For Hot Strip Rolling mill

    Science.gov (United States)

    Moussaoui, A. K.; Abbassi, H. A.; Bouazza, S.

    2008-06-01

    The present paper deals with the application of an Extended Kalman filter based adaptive Neural-Network control scheme to improve the performance of a hot strip rolling mill. The suggested Neural Network model was implemented using Bayesian Evidence based training algorithm. The control input was estimated iteratively by an on-line extended Kalman filter updating scheme basing on the inversion of the learned neural networks model. The performance of the controller is evaluated using an accurate model estimated from real rolling mill input/output data, and the usefulness of the suggested method is proved.

  18. NEURAL NETWORK SPEED CONTROLLER FOR DIRECT VECTOR CONTROL OF INDUCTION MOTORS

    Directory of Open Access Journals (Sweden)

    BEN HAMED MOUNA,

    2010-12-01

    Full Text Available This paper presents a new method for the implementation of a direct rotor flux control (DRFOC of induction motor (IM drives. It is based on the rotor flux components regulation. In fact, the d and q axis flux components feed a proportional integral (PI controller. The outputs of which are the targets start voltages (vdsref andvqsref. While, the synchronous speed is depicted at the output of rotor speed controller. In order to accomplish variable speed operation, conventional PI lie controller is commonly used. This controller provides limited good performances over a wide range of operation even under ideal field oriented conditions. An alternate approach is to use the so called neural network controller. The overall investigated system is implemented using dSpace system based on digital signal processor (DSP. Simulation and experimental results have been presented for one kw IM drives to confirm the validity of the proposed algorithms.

  19. The Relationship of Aluminium and Silver to Neural Tube Defects; a Case Control

    Directory of Open Access Journals (Sweden)

    María de Jesús Ramírez-Altamirano

    2012-09-01

    Full Text Available Objective: The purpose of this study was to identify the relationship of neurotoxic inorganic elements in the hair of patients with the diagnosis of Neural Tube Defects. Our initial hypothesis was that neurotoxic inorganic elements were associated with Neural Tube Defects.Methods: Twenty-three samples of hair from newborns were obtained from the General Hospital, “Aurelio Valdivieso” in the city of Oaxaca, Mexico. The study group included 8 newborn infants with neural tube pathology. The control group was composed of 15 newborns without this pathology. The presence of inorganic elements in the hair samples was determined by inductively-coupled plasma spectroscopy(spectroscopic emission of the plasma.Findings: The population of newborns with Neural Tube Defects showed significantly higher values of the following elements than the control group: Aluminium, Neural Tube Defects 152.77±51.06 μg/g, control group 76.24±27.89 μg/g; Silver, Neural Tube Defects 1.45±0.76, control group 0.25±0.53 μg/g; Potassium,Neural Tube Defects 553.87±77.91 μg/g, control group 341.13±205.90 μg/g. Association was found at 75 percentile between aluminium plus silver, aluminium plus potassium, silver plus potassium, and potassium plus sodium.Conclusion: In the hair of newborns with Neural Tube Defects, the following metals were increased:aluminium, silver. Given the neurotoxicity of the same, and association of Neural Tube Defects with aluminum and silver, one may infer that they may be participating as factors in the development of Neural Tube Defects.

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

    Institute of Scientific and Technical Information of China (English)

    Tian Sheping; Ding Guoqing; Yan Detian; Lin Liangming

    2004-01-01

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

  1. 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.

  2. Adaptive Neural Control of Uncertain MIMO Nonlinear Systems With State and Input Constraints.

    Science.gov (United States)

    Chen, Ziting; Li, Zhijun; Chen, C L Philip

    2016-03-17

    An adaptive neural control strategy for multiple input multiple output nonlinear systems with various constraints is presented in this paper. To deal with the nonsymmetric input nonlinearity and the constrained states, the proposed adaptive neural control is combined with the backstepping method, radial basis function neural network, barrier Lyapunov function (BLF), and disturbance observer. By ensuring the boundedness of the BLF of the closed-loop system, it is demonstrated that the output tracking is achieved with all states remaining in the constraint sets and the general assumption on nonsingularity of unknown control coefficient matrices has been eliminated. The constructed adaptive neural control has been rigorously proved that it can guarantee the semiglobally uniformly ultimate boundedness of all signals in the closed-loop system. Finally, the simulation studies on a 2-DOF robotic manipulator system indicate that the designed adaptive control is effective.

  3. 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.

  4. Adaptive neural control of nonlinear time-delay systems with unknown virtual control coefficients.

    Science.gov (United States)

    Ge, Shuzhi Sam; Hong, Fan; Lee, Tong Heng

    2004-02-01

    In this paper, adaptive neural control is presented for a class of strict-feedback nonlinear systems with unknown time delays. The proposed design method does not require a priori knowledge of the signs of the unknown virtual control coefficients. The unknown time delays are compensated for using appropriate Lyapunov-Krasovskii functionals in the design. It is proved that the proposed backstepping design method is able to guarantee semiglobal uniformly ultimately boundedness of all the signals in the closed-loop. In addition, the output of the system is proven to converge to a small neighborhood of the origin. Simulation results are provided to show the effectiveness of the proposed approach.

  5. Slit and Robo control cardiac cell polarity and morphogenesis.

    Science.gov (United States)

    Qian, Li; Liu, Jiandong; Bodmer, Rolf

    2005-12-20

    Basic aspects of heart morphogenesis involving migration, cell polarization, tissue alignment, and lumen formation may be conserved between Drosophila and humans, but little is known about the mechanisms that orchestrate the assembly of the heart tube in either organism. The extracellular-matrix molecule Slit and its Robo-family receptors are conserved regulators of axonal guidance. Here, we report a novel role of the Drosophila slit, robo, and robo2 genes in heart morphogenesis. Slit and Robo proteins specifically accumulate at the dorsal midline between the bilateral myocardial progenitors forming a linear tube. Manipulation of Slit localization or its overexpression causes disruption in heart tube alignment and assembly, and slit-deficient hearts show disruptions in cell-polarity marker localization within the myocardium. Similar phenotypes are observed when Robo and Robo2 are manipulated. Rescue experiments suggest that Slit is secreted from the myocardial progenitors and that Robo and Robo2 act in myocardial and pericardial cells, respectively. Genetic interactions suggest a cardiac morphogenesis network involving Slit/Robo, cell-polarity proteins, and other membrane-associated proteins. We conclude that Slit and Robo proteins contribute significantly to Drosophila heart morphogenesis by guiding heart cell alignment and adhesion and/or by inhibiting cell mixing between the bilateral compartments of heart cell progenitors and ensuring proper polarity of the myocardial epithelium.

  6. Comparison between K-nearest-neighbor approaches for conditional entropy estimation: Application to the assessment of the cardiac control in amyotrophic lateral sclerosis patients.

    Science.gov (United States)

    Porta, Alberto; De Maria, Beatrice; Bari, Vlasta; Marchi, Andrea; Marinou, Kalliopi; Sideri, Riccardo; Mora, Gabriele; Dalla Vecchia, Laura

    2016-08-01

    The study evaluates the k-nearest-neighbor (KNN) strategy for the assessment of complexity of the cardiac neural control from spontaneous fluctuations of heart period (HP). Two different procedures were assessed: i) the KNN estimation of the conditional entropy (CE) proposed by Porta et al; ii) the KNN estimation of mutual information proposed by Kozachenko-Leonenko, refined by Kraskov-Stögbauer-Grassberger and here adapted for the CE estimation. The two procedures were compared over HP variability recordings obtained at rest in supine position and during head-up tilt (HUT) in amyotrophic lateral sclerosis patients and healthy subjects. We found that the indexes derived from the two procedures were significantly correlated and both methods were able to detect the effect of HUT on HP complexity within the same group and distinguish the two populations within the same experimental condition. We recommend the use of the KNN strategy to quantify the dynamical complexity of cardiac neural control in addition to more traditional approaches.

  7. Parallel Neural Network-Based Motion Controller for Autonomous Underwater Vehicles

    Institute of Scientific and Technical Information of China (English)

    GAN Yong; WANG Li-rong; WAN Lei; XU Yu-ru

    2005-01-01

    A parallel neural network-based controller (PNNC) is presented for the motion control of underwater vehicles in this paper. It consists of a real-time part, a self-learning part and a desired-state programmer, and it is different from normal adaptive neural network controller in structure. Owing to the introduction of the self-learning part, on-line learning can be performed without sample data in several sample periods, resulting in high learning speed of the controller and good control performance. The desired-state programmer is utilized to obtain better learning samples of the neural network to keep the stability of the controller. The developed controller is applied to the 4-degree of freedom control of the AUV "IUV-IV" and is successful on the simulation platform. The control performance is also compared with that of neural network controller with different structures such as normal adaptive neural network and different learning methods. Current effects and surge velocity control are also included to demonstrate the controller's performance. It is shown that the PNNC has a great possibility to solve the problems in the control system design of underwater vehicles.

  8. Clinical nurse specialists lead teams to impact glycemic control after cardiac surgery.

    Science.gov (United States)

    Klinkner, Gwen; Murray, Margaret

    2014-01-01

    The purpose of this evidence-based practice improvement project was to improve patients' blood glucose control after cardiac surgery, specifically aiming to keep blood glucose levels less than 200 mg/dL. Glycemic control is essential for wound healing and infection prevention. Multiple factors including the use of corticosteroids and the stress of critical illness put cardiac surgery patients at greater risk for elevated blood glucose levels postoperatively. A Surgical Care Improvement Project measure related to infection prevention calls for the morning blood glucose level (closest to 6:00 AM) to be less than 200 mg/dL on postoperative days 0 to 2. Patients on our cardiothoracic surgery unit were experiencing blood glucose levels greater than benchmark goals. A practice improvement effort was designed to decrease the number of blood glucose results greater than 200 mg/dL after cardiac surgery. The clinical nurse specialists for diabetes and cardiac surgery worked with nursing staff and the interdisciplinary team to implement a 4-pronged approach to improve efficiency in care processes: (1) increase frequency of glucose monitoring, (2) improve accessibility of insulin orders, (3) develop delegation protocol to facilitate nurse-initiated insulin infusion, and (4) implement revised insulin infusion protocol. Hyperglycemia was identified more quickly, and a nurse-initiated protocol prompted more timely use of revised insulin infusion orders and involvement of the diabetes specialty team. Clinically significant improvement in postoperative glycemic control was achieved. Empowering nurses to initiate hyperglycemia treatment and consultation by diabetes specialists may greatly improve efficiency in care processes and clinical outcomes for cardiac surgery patients. Clinical nurse specialists are well positioned to plan and implement interventions that facilitate an evidence-based approach to glycemic management after cardiac surgery.

  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. Vector control of wind turbine on the basis of the fuzzy selective neural net*

    Science.gov (United States)

    Engel, E. A.; Kovalev, I. V.; Engel, N. E.

    2016-04-01

    An article describes vector control of wind turbine based on fuzzy selective neural net. Based on the wind turbine system’s state, the fuzzy selective neural net tracks an maximum power point under random perturbations. Numerical simulations are accomplished to clarify the applicability and advantages of the proposed vector wind turbine’s control on the basis of the fuzzy selective neuronet. The simulation results show that the proposed intelligent control of wind turbine achieves real-time control speed and competitive performance, as compared to a classical control model with PID controllers based on traditional maximum torque control strategy.

  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.

  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. Classification of Atrial Septal Defect and Ventricular Septal Defect with Documented Hemodynamic Parameters via Cardiac Catheterization by Genetic Algorithms and Multi-Layered Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Mustafa Yıldız

    2012-08-01

    Full Text Available Introduction: We aimed to develop a classification method to discriminate ventricular septal defect and atrial septal defect by using severalhemodynamic parameters.Patients and Methods: Forty three patients (30 atrial septal defect, 13 ventricular septal defect; 26 female, 17 male with documentedhemodynamic parameters via cardiac catheterization are included to study. Such parameters as blood pressure values of different areas,gender, age and Qp/Qs ratios are used for classification. Parameters, we used in classification are determined by divergence analysismethod. Those parameters are; i pulmonary artery diastolic pressure, ii Qp/Qs ratio, iii right atrium pressure, iv age, v pulmonary arterysystolic pressure, vi left ventricular sistolic pressure, vii aorta mean pressure, viii left ventricular diastolic pressure, ix aorta diastolicpressure, x aorta systolic pressure. Those parameters detected from our study population, are uploaded to multi-layered artificial neuralnetwork and the network was trained by genetic algorithm.Results: Trained cluster consists of 14 factors (7 atrial septal defect and 7 ventricular septal defect. Overall success ratio is 79.2%, andwith a proper instruction of artificial neural network this ratio increases up to 89%.Conclusion: Parameters, belonging to artificial neural network, which are needed to be detected by the investigator in classical methods,can easily be detected with the help of genetic algorithms. During the instruction of artificial neural network by genetic algorithms, boththe topology of network and factors of network can be determined. During the test stage, elements, not included in instruction cluster, areassumed as in test cluster, and as a result of this study, we observed that multi-layered artificial neural network can be instructed properly,and neural network is a successful method for aimed classification.

  14. Temporary epicardial cardiac resynchronisation versus conventional right ventricular pacing after cardiac surgery: study protocol for a randomised control trial

    Directory of Open Access Journals (Sweden)

    Russell Stuart J

    2012-02-01

    Full Text Available Abstract Background Heart failure patients with stable angina, acute coronary syndromes and valvular heart disease may benefit from revascularisation and/or valve surgery. However, the mortality rate is increased- 5-30%. Biventricular pacing using temporary epicardial wires after surgery is a potential mechanism to improve cardiac function and clinical endpoints. Method/design A multi-centred, prospective, randomised, single-blinded, intervention-control trial of temporary biventricular pacing versus standard pacing. Patients with ischaemic cardiomyopathy, valvular heart disease or both, an ejection fraction ≤ 35% and a conventional indication for cardiac surgery will be recruited from 2 cardiac centres. Baseline investigations will include: an electrocardiogram to confirm sinus rhythm and measure QRS duration; echocardiogram to evaluate left ventricular function and markers of mechanical dyssynchrony; dobutamine echocardiogram for viability and blood tests for renal function and biomarkers of myocardial injury- troponin T and brain naturetic peptide. Blood tests will be repeated at 18, 48 and 72 hours. The principal exclusions will be subjects with permanent atrial arrhythmias, permanent pacemakers, infective endocarditis or end-stage renal disease. After surgery, temporary pacing wires will be attached to the postero-lateral wall of the left ventricle, the right atrium and right ventricle and connected to a triple chamber temporary pacemaker. Subjects will be randomised to receive either temporary biventricular pacing or standard pacing (atrial inhibited pacing or atrial-synchronous right ventricular pacing for 48 hours. The primary endpoint will be the duration of level 3 care. In brief, this is the requirement for invasive ventilation, multi-organ support or more than one inotrope/vasoconstrictor. Haemodynamic studies will be performed at baseline, 6, 18 and 24 hours after surgery using a pulmonary arterial catheter. Measurements will be

  15. Efficacy of pasireotide in controlling severe hypercortisolism until cardiac transplantation

    Directory of Open Access Journals (Sweden)

    Roberto Attanasio

    2017-03-01

    Full Text Available Cushing’s syndrome is associated with increased morbidity and mortality. Although surgery is the first-line treatment, drugs can still play a role as an ancillary treatment to be employed while waiting for surgery, after unsuccessful operation or in patients unsuitable for surgery. We were asked to evaluate a 32-year-old male waiting for cardiac transplantation. Idiopathic hypokinetic cardiomyopathy had been diagnosed since 6 years. He was on treatment with multiple drugs, had a pacemaker, an implantable cardioverter and an external device for the support of systolic function. Physical examination showed severely impaired general status, signs of hypercortisolism and multiple vertebral compression fractures. We administered teriparatide, and the few evaluable parameters supported the diagnosis of ACTH-dependent hypercortisolism: serum cortisol was 24.2 μg/dL in the morning and 20.3 μg/dL after overnight 1 mg dexamethasone, urinary free cortisol (UFC was 258 μg/24 h and ACTH 125 pg/mL. Pituitary CT was negative. Pasireotide 300 μg bid was administered and uptitrated to 600 μg bid. Treatment was well tolerated, achieving dramatic improvement of clinical picture with progressive normalization of serum cortisol and ACTH levels as well as UFC. After 4 months, the patient underwent successful heart transplantation. Many complications ensued and were overcome. Pituitary MRI was negative. On pasireotide 300 μg bid and prednisone 2.5 mg/day (as part of immunosuppressive therapy, morning serum cortisol and ACTH were 15.6 μg/dL and 54 pg/mL respectively, UFC was 37 μg/24 h, fasting glucose: 107 mg/dL and HbA1c: 6.5%. In conclusion, primary treatment with pasireotide achieved remission of hypercortisolism, thus allowing the patient to undergo heart transplantation.

  16. [Donation protocol following controlled cardiac death (Maastricht type III donation). First experience].

    Science.gov (United States)

    Rubio-Muñoz, J J; Pérez-Redondo, M; Alcántara-Carmona, S; Lipperheide-Vallhonrat, I; Fernández-Simón, I; Valdivia-de la Fuente, M; Villanueva-Fernández, H; Balandín-Moreno, B; Ortega-López, A; Romera-Ortega, M A; Galdos-Anuncibay, P

    2014-03-01

    To present our experience with the implementation of a donation protocol following controlled cardiac death (Maastricht type III donation). A retrospective descriptive and observational study was made. Intensive Care Unit of a third-level university hospital. Eight patients in an irreversible state, in which withdrawal of all life support had been agreed, were evaluated as potential donors. Application of the adopted protocol. Clinical data of donors, evaluation of a donation protocol following cardiac death, warm ischemia times, and short-term outcome of the recipients. Eight patients were evaluated. In one case donation was not possible because no cardiac arrest developed in the 120 minutes after extubation. The 7 remaining patients were effective kidney donors. Warm ischemia times were less than 23 minutes in all cases. Although 7 of the 14 recipients suffered delayed graft function, all of them achieved good renal function. Donation after cardiac death in patients in an overwhelming and irreversible state represents a potential source of donors not previously considered in this country. The prior development of a consensus-based protocol can help increase the number of organs in combination with those obtained after brain death. In our experience, the results of kidney transplants obtained from donors after cardiac death are good, and the success of these types of protocols could be extended to other organs such as the liver and lungs. Copyright © 2012 Elsevier España, S.L. and SEMICYUC. All rights reserved.

  17. Randomised controlled trials of iron chelators for the treatment of cardiac siderosis in thalassaemia major

    Directory of Open Access Journals (Sweden)

    Arun John Baksi

    2014-09-01

    Full Text Available In conditions requiring repeated blood transfusion or where iron metabolism is abnormal, heart failure may result from accumulation of iron in the heart (cardiac siderosis. Death due to heart failure from cardiac iron overload has accounted for considerable early mortality in β-thalassemia major. The ability to detect iron loading in the heart by cardiovascular magnetic resonance using T2* sequences has created an opportunity to intervene in the natural history of such conditions. However, effective and well tolerated therapy is required to remove iron from the heart. There are currently 3 approved commercially available iron chelators: deferoxamine, deferiprone and deferasirox. We review the high quality randomised controlled trials in this area for iron chelation therapy in the management of cardiac siderosis.

  18. 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.

  19. 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.

  20. Neural Network-Based Resistance Spot Welding Control and Quality Prediction

    Energy Technology Data Exchange (ETDEWEB)

    Allen, J.D., Jr.; Ivezic, N.D.; Zacharia, T.

    1999-07-10

    This paper describes the development and evaluation of neural network-based systems for industrial resistance spot welding process control and weld quality assessment. The developed systems utilize recurrent neural networks for process control and both recurrent networks and static networks for quality prediction. The first section describes a system capable of both welding process control and real-time weld quality assessment, The second describes the development and evaluation of a static neural network-based weld quality assessment system that relied on experimental design to limit the influence of environmental variability. Relevant data analysis methods are also discussed. The weld classifier resulting from the analysis successfldly balances predictive power and simplicity of interpretation. The results presented for both systems demonstrate clearly that neural networks can be employed to address two significant problems common to the resistance spot welding industry, control of the process itself, and non-destructive determination of resulting weld quality.

  1. NEURAL NETWORKS CONTROL OF THE HYBRID POWER UNIT BASED ON THE METHOD OF ADAPTIVE CRITICS

    Directory of Open Access Journals (Sweden)

    S. Serikov

    2012-01-01

    Full Text Available The formal statement of the optimization problem of hybrid vehicle power unit control is given. Its solving by neural networks method application on the basis of adaptive critic is considered.

  2. 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.

  3. Power spectrum scale invariance as a neural marker of cocaine misuse and altered cognitive control

    Directory of Open Access Journals (Sweden)

    Jaime S. Ide

    2016-01-01

    Conclusions: These findings suggest disrupted connectivity dynamics in the fronto-parietal areas in association with post-signal behavioral adjustment in cocaine addicts. These new findings support PSSI as a neural marker of impaired cognitive control in cocaine addiction.

  4. 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.

  5. Building the artificial neural network environment : Artificial Neural Networks in plane control

    OpenAIRE

    Naumetc, Daniil

    2017-01-01

    These days Artificial Neural Networks have penetrated into all digital technologies that surround us. Mostly every online service like Facebook, Google, Instagram are using Artificial Intelligence to build better service for their users. Google Self-Driving Car Project that started several years ago already have results as driverless cars already moving on the streets of California. Artificial Intelligence makes a breakthrough in Medicine as well. Such programs already successfully find disea...

  6. Neural activity control of neural stem cells and SVZ niche response to brain injury

    OpenAIRE

    Páez González, Patricia

    2014-01-01

    Patricia Paez-Gonzalez Kuo Lab, Dept. of Cell Biology, Duke University Medical Center, NC,USA. Date: 11/16/2014 Utilizing stem cells in the adult brain hold great promise for regenerative medicine. Harnessing ability of adult neural stem cells (NSCs) to generate new neurons or other types of brain cells may provide much needed therapies for patients suffering from brain injuries or neuro-degenerative diseases such as Parkinson’s, Scizophrenia, or Alzheimer’s disease. However...

  7. Exponential synchronization of delayed memristor-based chaotic neural networks via periodically intermittent control.

    Science.gov (United States)

    Zhang, Guodong; Shen, Yi

    2014-07-01

    This paper investigates the exponential synchronization of coupled memristor-based chaotic neural networks with both time-varying delays and general activation functions. And here, we adopt nonsmooth analysis and control theory to handle memristor-based chaotic neural networks with discontinuous right-hand side. In particular, several new criteria ensuring exponential synchronization of two memristor-based chaotic neural networks are obtained via periodically intermittent control. In addition, the new proposed results here are very easy to verify and also complement, extend the earlier publications. Numerical simulations on the chaotic systems are presented to illustrate the effectiveness of the theoretical results.

  8. Synchronization control of memristor-based recurrent neural networks with perturbations.

    Science.gov (United States)

    Wang, Weiping; Li, Lixiang; Peng, Haipeng; Xiao, Jinghua; Yang, Yixian

    2014-05-01

    In this paper, the synchronization control of memristor-based recurrent neural networks with impulsive perturbations or boundary perturbations is studied. We find that the memristive connection weights have a certain relationship with the stability of the system. Some criteria are obtained to guarantee that memristive neural networks have strong noise tolerance capability. Two kinds of controllers are designed so that the memristive neural networks with perturbations can converge to the equilibrium points, which evoke human's memory patterns. The analysis in this paper employs the differential inclusions theory and the Lyapunov functional method. Numerical examples are given to show the effectiveness of our results.

  9. Finite-time synchronization control of a class of memristor-based recurrent neural networks.

    Science.gov (United States)

    Jiang, Minghui; Wang, Shuangtao; Mei, Jun; Shen, Yanjun

    2015-03-01

    This paper presents a global and local finite-time synchronization control law for memristor neural networks. By utilizing the drive-response concept, differential inclusions theory, and Lyapunov functional method, we establish several sufficient conditions for finite-time synchronization between the master and corresponding slave memristor-based neural network with the designed controller. In comparison with the existing results, the proposed stability conditions are new, and the obtained results extend some previous works on conventional recurrent neural networks. Two numerical examples are provided to illustrate the effective of the design method.

  10. Method for neural network control of motion using real-time environmental feedback

    Science.gov (United States)

    Buckley, Theresa M. (Inventor)

    1997-01-01

    A method of motion control for robotics and other automatically controlled machinery using a neural network controller with real-time environmental feedback. The method is illustrated with a two-finger robotic hand having proximity sensors and force sensors that provide environmental feedback signals. The neural network controller is taught to control the robotic hand through training sets using back- propagation methods. The training sets are created by recording the control signals and the feedback signal as the robotic hand or a simulation of the robotic hand is moved through a representative grasping motion. The data recorded is divided into discrete increments of time and the feedback data is shifted out of phase with the control signal data so that the feedback signal data lag one time increment behind the control signal data. The modified data is presented to the neural network controller as a training set. The time lag introduced into the data allows the neural network controller to account for the temporal component of the robotic motion. Thus trained, the neural network controlled robotic hand is able to grasp a wide variety of different objects by generalizing from the training sets.

  11. Improvement of Power System Stability using Artificial Neural Network based HVDC Controls

    Directory of Open Access Journals (Sweden)

    Nagu Bhookya

    2013-06-01

    Full Text Available In this paper, investigation is carried out for the improvement of power system stability by utilizing auxiliary controls for controlling HVDC power flow. The current controller model and the line dynamics are considered in the stability analysis. Transient stability analysis is done on a multi-machine system, where, a neural network controller is developed to improve the stability of the power system and to improve the response time of the controller to the changing conditions in power system. The results show the application of the neural network controller in AC-DC power systems.

  12. The neural crest stem cells: control of neural crest cell fate and plasticity by endothelin-3

    Directory of Open Access Journals (Sweden)

    ELISABETH DUPIN

    2001-12-01

    Full Text Available How the considerable diversity of neural crest (NC-derived cell types arises in the vertebrate embryo has long been a key question in developmental biology. The pluripotency and plasticity of differentiation of the NC cell population has been fully documented and it is well-established that environmental cues play an important role in patterning the NC derivatives throughout the body. Over the past decade, in vivo and in vitro cellular approaches have unravelled the differentiation potentialities of single NC cells and led to the discovery of NC stem cells. Although it is clear that the final fate of individual cells is in agreement with their final position within the embryo, it has to be stressed that the NC cells that reach target sites are pluripotent and further restrictions occur only late in development. It is therefore a heterogenous collection of cells that is submitted to local environmental signals in the various NC-derived structures. Several factors were thus identified which favor the development of subsets of NC-derived cells in vitro. Moreover, the strategy of gene targeting in mouse has led at identifying new molecules able to control one or several aspects of NC cell differentiation in vivo. Endothelin peptides (and endothelin receptors are among those. The conjunction of recent data obtained in mouse and avian embryos and reviewed here contributes to a better understanding of the action of the endothelin signaling pathway in the emergence and stability of NC-derived cell phenotypes.O modo como a diversidade dos tipos celulares derivados da crista neural (CN surge, no embrião de vertebrado, tem sido uma pergunta chave na biologia do desenvolvimento. A pluripotência e a plasticidade na diferenciação da população de células da CN têm sido intensivamente documentadas, ficando deste modo estabelecido que os factores ambientais têm um papel importante na correta diferenciação dos derivados da CN no organismo. Na d

  13. Adaptive Neural Network Dynamic Inversion with Prescribed Performance for Aircraft Flight Control

    OpenAIRE

    Wendong Gai; Honglun Wang; Jing Zhang; Yuxia Li

    2013-01-01

    An adaptive neural network dynamic inversion with prescribed performance method is proposed for aircraft flight control. The aircraft nonlinear attitude angle model is analyzed. And we propose a new attitude angle controller design method based on prescribed performance which describes the convergence rate and overshoot of the tracking error. Then the model error is compensated by the adaptive neural network. Subsequently, the system stability is analyzed in detail. Finally, the proposed meth...

  14. A Neural Network with Minimal Structure for Maglev System Modeling and Control

    OpenAIRE

    1999-01-01

    6 pages; International audience; The paper is concerned with the determination of a minimal structure of a one hidden layer perceptron for system identification and control. Structural identification is a key issue in neural modeling. Decreasing the size of the neural networks is a way to avoid overfitting and bad generalization and leads moreover to simpler models which are required for real time applications, particularly in control. A learning algorithm and a pruning method both based on a...

  15. Real-time feedback based control of cardiac restitution using optical mapping.

    Science.gov (United States)

    Kulkarni, Kanchan; Tolkacheva, Elena G

    2015-01-01

    Cardiac restitution is the shortening of the action potential duration with an increase in the heart rate. A shorter action potential duration enables a longer diastolic interval which ensures that the heart gets adequate time to refill with blood. At higher rates however, restitution becomes steep and thus, can lead to unstable electrical activity (alternans) in the heart, leading to fatal cardiac rhythms. It has been proposed that maintaining a shallow slope of cardiac restitution could have potentially anti-arrhythmic effects. Previous studies involved the control of action potential duration (APD) or diastolic interval (DI) in isolated tissue samples based on the feedback from single microelectrode recordings. This limited the spatial resolution of the feedback system. Here, we aimed to develop a real time feedback control system that enabled the detection of APDs from various single pixels based on optical mapping recordings. Stimuli were applied after a predefined fixed DI after detection of an APD. We validated our algorithm using optical mapping movies from an ex-vivo rabbit heart. Thus, we provide an optical mapping based approach for the control of cardiac restitution and a potential means to validate its anti-arrhythmic effects.

  16. Chaos control applied to cardiac rhythms represented by ECG signals

    Science.gov (United States)

    Borem Ferreira, Bianca; Amorim Savi, Marcelo; Souza de Paula, Aline

    2014-10-01

    The control of irregular or chaotic heartbeats is a key issue in cardiology. In this regard, chaos control techniques represent a good alternative since they suggest treatments different from those traditionally used. This paper deals with the application of the extended time-delayed feedback control method to stabilize pathological chaotic heart rhythms. Electrocardiogram (ECG) signals are employed to represent the cardiovascular behavior. A mathematical model is employed to generate ECG signals using three modified Van der Pol oscillators connected with time delay couplings. This model provides results that qualitatively capture the general behavior of the heart. Controlled ECG signals show the ability of the strategy either to control or to suppress the chaotic heart dynamics generating less-critical behaviors.

  17. Applications of control theory to the dynamics and propagation of cardiac action potentials.

    Science.gov (United States)

    Muñoz, Laura M; Stockton, Jonathan F; Otani, Niels F

    2010-09-01

    Sudden cardiac arrest is a widespread cause of death in the industrialized world. Most cases of sudden cardiac arrest are due to ventricular fibrillation (VF), a lethal cardiac arrhythmia. Electrophysiological abnormalities such as alternans (a beat-to-beat alternation in action potential duration) and conduction block have been suspected to contribute to the onset of VF. This study focuses on the use of control-systems techniques to analyze and design methods for suppressing these precursor factors. Control-systems tools, specifically controllability analysis and Lyapunov stability methods, were applied to a two-variable Karma model of the action-potential (AP) dynamics of a single cell, to analyze the effectiveness of strategies for suppressing AP abnormalities. State-feedback-integral (SFI) control was then applied to a Purkinje fiber simulated with the Karma model, where only one stimulating electrode was used to affect the system. SFI control converted both discordant alternans and 2:1 conduction block back toward more normal patterns, over a wider range of fiber lengths and pacing intervals compared with a Pyragas-type chaos controller. The advantages conferred by using feedback from multiple locations in the fiber, and using integral (i.e., memory) terms in the controller, are discussed.

  18. 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.

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

    Directory of Open Access Journals (Sweden)

    R. S.M.N. Malar

    2009-01-01

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

  20. Decentralized neural identifier and control for nonlinear systems based on extended Kalman filter.

    Science.gov (United States)

    Castañeda, Carlos E; Esquivel, P

    2012-07-01

    A time-varying learning algorithm for recurrent high order neural network in order to identify and control nonlinear systems which integrates the use of a statistical framework is proposed. The learning algorithm is based in the extended Kalman filter, where the associated state and measurement noises covariance matrices are composed by the coupled variance between the plant states. The formulation allows identification of interactions associate between plant state and the neural convergence. Furthermore, a sliding window-based method for dynamical modeling of nonstationary systems is presented to improve the neural identification in the proposed methodology. The efficiency and accuracy of the proposed method is assessed to a five degree of freedom (DOF) robot manipulator where based on the time-varying neural identifier model, the decentralized discrete-time block control and sliding mode techniques are used to design independent controllers and develop the trajectory tracking for each DOF.

  1. 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 ......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 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....

  2. Adaptive Backstepping Output Feedback Control for SISO Nonlinear System Using Fuzzy Neural Networks

    Institute of Scientific and Technical Information of China (English)

    Shao-Cheng Tong; Yong-Ming Li

    2009-01-01

    In this paper, a new fuzzy-neural adaptive control approach is developed for a class of single-input and single-output (SISO) nonlinear systems with unmeasured states. Using fuzzy neural networks to approximate the unknown nonlinear functions, a fuzzy-neural adaptive observer is introduced for state estimation as well as system identification. Under the framework of the backstepping design, fuzzy-neural adaptive output feedback control is constructed rccursively. It is proven that the proposed fuzzy adaptive control approach guarantees the global boundedness property for all the signals, driving the tracking error to a small neighbordhood of the origin. Simulation example is included to illustrate the effectiveness of the proposed approach.

  3. 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.

  4. Manipulator adaptive control by neural networks in an orange picking robot.

    Science.gov (United States)

    Cavalieri, S; Plebe, A

    1996-12-01

    The paper focuses on the use of neural networks for process identification in an orange-picking robot adaptive control system. The results that will be shown in the paper refer to a study carried out under the European Community ESPRIT project "CONNY", dealing with the application of neural networks to robotics. The aim of the research is to verify the possibility of integrating a neural identification module in a traditional system to control the movement of the manipulators of the robot. The paper illustrates integration of neural identification in the existing orange-picking robot control system, highlighting the improvement of performance obtainable. Although the proposal refers to a specific robot, it can be applied to any system with the same dynamic features.

  5. The optimum design of the pressure control spring of the relief valve based on neural networks

    Institute of Scientific and Technical Information of China (English)

    FU Xiao-jin

    2006-01-01

    Based on the traditional optimization methods about the pressure control spring of the relief valve and combined with the advantages of neural network, this paper put forward the optimization method with many parameters and a lot of constraints based on neural network. The object function of optimization is transformed into the energy function of the neural network and the mathematical model of neural network optimization about the pressure control spring of the relief valve is set up in this method which also puts forward its own algorithm. An example of application shows that network convergence gets stable state of minimization object function E, and object function converges to the utmost minimum point with steady function, then best solution is gained, which makes the design plan better. The algorithm of solution for the problem is effective about the optimum design of the pressure control spring and improves the performance target.

  6. Neural Network for Image-to-Image Control of Optical Tweezers

    Science.gov (United States)

    Decker, Arthur J.; Anderson, Robert C.; Weiland, Kenneth E.; Wrbanek, Susan Y.

    2004-01-01

    A method is discussed for using neural networks to control optical tweezers. Neural-net outputs are combined with scaling and tiling to generate 480 by 480-pixel control patterns for a spatial light modulator (SLM). The SLM can be combined in various ways with a microscope to create movable tweezers traps with controllable profiles. The neural nets are intended to respond to scattered light from carbon and silicon carbide nanotube sensors. The nanotube sensors are to be held by the traps for manipulation and calibration. Scaling and tiling allow the 100 by 100-pixel maximum resolution of the neural-net software to be applied in stages to exploit the full 480 by 480-pixel resolution of the SLM. One of these stages is intended to create sensitive null detectors for detecting variations in the scattered light from the nanotube sensors.

  7. 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 exp

  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. Neural network-based control using Lyapunov functions

    Science.gov (United States)

    Luxemburg, Leon A.

    1993-01-01

    We have successfully demonstrated how the problem of stabilization of plants can be reduced to a problem of approximation of functions. Neural networks have been shown to have approximating and interpolating properties. This approach is good for linear and nonlinear plants. Software has been generated to demonstrate this approach.

  10. The Ca2+-induced methyltransferase xPRMT1b controls neural fate in amphibian embryo.

    Science.gov (United States)

    Batut, Julie; Vandel, Laurence; Leclerc, Catherine; Daguzan, Christiane; Moreau, Marc; Néant, Isabelle

    2005-10-18

    We have previously shown that an increase in intracellular Ca2+ is both necessary and sufficient to commit ectoderm to a neural fate in Xenopus embryos. However, the relationship between this Ca2+ increase and the expression of early neural genes has yet to be defined. Using a subtractive cDNA library between untreated and caffeine-treated animal caps, i.e., control ectoderm and ectoderm induced toward a neural fate by a release of Ca2+, we have isolated the arginine N-methyltransferase, xPRMT1b, a Ca2+-induced target gene, which plays a pivotal role in this process. First, we show in embryo and in animal cap that xPRMT1b expression is Ca2+-regulated. Second, overexpression of xPRMT1b induces the expression of early neural genes such as Zic3. Finally, in the whole embryo, antisense approach with morpholino oligonucleotide against xPRMT1b impairs neural development and in animal caps blocks the expression of neural markers induced by a release of internal Ca2+. Our results implicate an instructive role of an enzyme, an arginine methyltransferase protein, in the embryonic choice of determination between epidermal and neural fate. The results presented provide insights by which a Ca2+ increase induces neural fate.

  11. 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

  12. Fuzzy Control Based on Neural Networks for Armored Vehicle Electric Drive System

    Institute of Scientific and Technical Information of China (English)

    MA Xiao-jun; LI Hua; ZHANG Jian; ZHANG Yu-nan

    2006-01-01

    In order to meet rigorous demands of control of electric motors in armored vehicle electric drive system and make the system of strong robustness and antijamming capability, a fuzzy control method based on neural networks is put forward. The simulation model of the armored vehicle electric drive system is built up to test the validity of the control. Simulation experiments show that when load is increased or decreased suddenly, the system adopting fuzzy control based on neural networks is insensitive to parameter change and has little overshooting and oscillation compared with PID control.

  13. Zero phase error control based on neural compensation for flight simulator servo system

    Institute of Scientific and Technical Information of China (English)

    Liu Jinkun; He Peng; Er Lianjie

    2006-01-01

    Using the future desired input value, zero phase error controller enables the overall system's frequency response exhibit zero phase shift for all frequencies and a small gain error at low frequency range, and based on this, a new algorithm is presented to design the feedforward controller. However, zero phase error controller is only suitable for certain linear system. To reduce the tracking error and improve robustness, the design of the proposed feedforward controller uses a neural compensation based on diagonal recurrent neural network. Simulation and real-time control results for flight simulator servo system show the effectiveness of the proposed approach.

  14. Bio-Inspired Controller for a Robot Cheetah with a Neural Mechanism Controlling Leg Muscles

    Institute of Scientific and Technical Information of China (English)

    Xin Wang; Mantian Li; Pengfei Wang; Wei Guo; Lining Sun

    2012-01-01

    The realization of a high-speed running robot is one of the most challenging problems in developing legged robots.The excellent performance of cheetahs provides inspiration for the control and mechanical design of such robots.This paper presents a three-dimensional model of a cheetah that predicts the locomotory behaviors of a running cheetah.Applying biological knowledge of the neural mechanism,we control the muscle flexion and extension during the stance phase,and control the positions of the joints in the flight phase via a PD controller to minimize complexity.The proposed control strategy is shown to achieve similar locomotion of a real cheetah.The simulation realizes good biological properties,such as the leg retraction,ground reaction force,and spring-like leg behavior.The stable bounding results show the promise of the controller in high-speed locomotion.The model can reach 2.7 m·s- 1 as the highest speed,and can accelerate from 0 to 1.5 m·s -1 in one stride cycle.A mechanical structure based on this simulation is designed to demonstrate the control approach,and the most recently developed hindlimb controlled by the proposed controller is presented in swinging-leg experiments and jump-force experiments.

  15. Neural network and fuzzy control in FES-assisted locomotion for the hemiplegic.

    Science.gov (United States)

    Chen, Yu-Luen; Chen, Shih-Ching; Chen, Weoi-Luen; Hsiao, Chin-Chih; Kuo, Te-Son; Lai, Jin-Shin

    2004-01-01

    This study is aimed at establishing a neural network and fuzzy feedback control FES system used for adjusting the optimum electrical stimulating current to control the motion of an ankle joint. The proposed method further improves the drop-foot problem existing in hemiplegia patients. The proposed system includes both hardware and software. The hardware system determines the patient's ankle joint angle using a position sensor located in the patient's affected side. This sensor stimulates the tibialis anterior with an electrical stimulator that induces the dorsiflexion action and achieves the ideal ankle joint trace motion. The software system estimates the stimulating current using a neural network. The fuzzy controller solves the nonlinear problem by compensating the motion trace errors between the neural network control and actual system. The control qualities of various controllers for four subjects were compared in the clinical test. It was found that both the root mean square error and the mean error were minimal when using the neural network and fuzzy controller. The drop-foot problem in hemiplegic's locomotion was effectively improved by incorporating the neural network and fuzzy controller with the functional electrical simulator.

  16. Robust adaptive fuzzy neural tracking control for a class of unknown chaotic systems

    Indian Academy of Sciences (India)

    Abdurahman Kadir; Xing-Yuan Wang; Yu-Zhang Zhao

    2011-06-01

    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 online estimation of the controlled system dynamics by tuning the parameters of fuzzy neural network (FNN). The Gaussian function, a specific example of radial basis function, is adopted here as a membership function. So, the tuning parameters include the weighting factors in the consequent part and the means and variances of the Gaussian membership functions in the antecedent part of fuzzy implications. To tune the parameters online, the back-propagation (BP) algorithm is developed. The robust controller is used to guarantee the stability and to control the performance of the closed-loop adaptive system, which is achieved always. Finally, simulation results show that the AFNC can achieve favourable tracking performances.

  17. Computer-controlled stimulator for clinical cardiac studies

    NARCIS (Netherlands)

    Heethaar, R.M.; Poelgeest, R. van; Meijler, F.L.; Woudstra, E.S.; Zouw, C. van de

    A computer-controlled stimulator is described to generate the complex stimulation patterns required for a detailed analysis of ventricular myocardial function and the responses of the conduction system to applied stimuli, Pulse interval, height and width can be generated on a beat-to-beat basis.

  18. Computer-controlled stimulator for clinical cardiac studies

    NARCIS (Netherlands)

    Heethaar, R.M.; Poelgeest, R. van; Meijler, F.L.; Woudstra, E.S.; Zouw, C. van de

    1977-01-01

    A computer-controlled stimulator is described to generate the complex stimulation patterns required for a detailed analysis of ventricular myocardial function and the responses of the conduction system to applied stimuli, Pulse interval, height and width can be generated on a beat-to-beat basis. For

  19. A realistic 3-D gated cardiac phantom for quality control of gated myocardial perfusion SPET: the Amsterdam gated (AGATE) cardiac phantom

    Energy Technology Data Exchange (ETDEWEB)

    Visser, Jacco J.N.; Busemann Sokole, Ellinor; Verberne, Hein J.; Habraken, Jan B.A.; Eck-Smit, Berthe L.F. van [Department of Nuclear Medicine, Academic Medical Center, Meibergdreef 9, 1105 AZ, Amsterdam (Netherlands); Stadt, Huybert J.F. van de; Jaspers, Joris E.N.; Shehata, Morgan; Heeman, Paul M. [Department of Medical Technological Development, Academic Medical Center, Amsterdam (Netherlands)

    2004-02-01

    A realistic 3-D gated cardiac phantom with known left ventricular (LV) volumes and ejection fractions (EFs) was produced to evaluate quantitative measurements obtained from gated myocardial single-photon emission tomography (SPET). The 3-D gated cardiac phantom was designed and constructed to fit into the Data Spectrum anthropomorphic torso phantom. Flexible silicone membranes form the inner and outer walls of the simulated left ventricle. Simulated LV volumes can be varied within the range 45-200 ml. The LV volume curve has a smooth and realistic clinical shape that is produced by a specially shaped cam connected to a piston. A fixed 70-ml stroke volume is applied for EF measurements. An ECG signal is produced at maximum LV filling by a controller unit connected to the pump. This gated cardiac phantom will be referred to as the Amsterdam 3-D gated cardiac phantom, or, in short, the AGATE cardiac phantom. SPET data were acquired with a triple-head SPET system. Data were reconstructed using filtered back-projection following pre-filtering and further processed with the Quantitative Gated SPECT (QGS) software to determine LV volume and EF values. Ungated studies were performed to measure LV volumes ranging from 45 ml to 200 ml. The QGS-determined LV volumes were systematically underestimated. For different LV combinations, the stroke volumes measured were consistent at 60-61 ml for 8-frame studies and 63-65 ml for 16-frame studies. QGS-determined EF values were slightly overestimated between 1.25% EF units for 8-frame studies and 3.25% EF units for 16-frame studies. In conclusion, the AGATE cardiac phantom offers possibilities for quality control, testing and validation of the whole gated cardiac SPET sequence, and testing of different acquisition and processing parameters and software. (orig.)

  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.

  1. 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.

  2. Congestion Control for ATM Networks Based on Diagonal Recurrent Neural Networks

    Institute of Scientific and Technical Information of China (English)

    HuangYunxian; YanWei

    1997-01-01

    An adaptive control model and its algorithms based on simple diagonal recurrent neural networks are presented for the dynamic congestion control in broadband ATM networks.Two simple dynamic queuing models of real networks are used to test the performance of the suggested control scheme.

  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. 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.

  5. Rethinking neural efficiency: effects of controlling for strategy use.

    Science.gov (United States)

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

    2007-10-01

    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 processing patterns during task performance seem to depend on the strategy used for task execution, preparation for task processing may depend on IQ. (PsycINFO Database Record (c) 2007 APA, all rights reserved).

  6. Control of liquid column height in electromagnetic casting with fuzzy neural network model

    Institute of Scientific and Technical Information of China (English)

    李朝霞; 郑贤淑

    2002-01-01

    The control of suitable and stable height of liquid column is the crucial point to operate the electromagnetic casting(EMC) process and to obtain ingots with desirable shape and dimensional accuracy. But due to the complicated interact parameters and special circumstances, the measure and control of liquid column are quite difficult. A fuzzy neural network was used to help control the liquid column by predicting its height on line. The results show that the stabilization of the height of liquid column and surface quality of the ingot are remarkably improved by using the neural network based control system.

  7. A Study of Maneuvering Control for an Air Cushion Vehicle Based on Back Propagation Neural Network

    Institute of Scientific and Technical Information of China (English)

    LU Jun; HUANG Guo-liang; LI Shu-zhi

    2009-01-01

    A back propagation (BP) neural network mathematical model was established to investigate the maneuvering control of an air cushion vehicle (ACV). The calculation was based on four-freedom-degree model experiments of hydrodynamics and aerodynamics. It is necessary for the ACV to control the velocity and the yaw rate as well as the velocity angle at the same time. The yaw rate and the velocity angle must be controlled correspondingly because of the whipping, which is a special characteristic for the ACV. The calculation results show that it is an efficient way for the ACV's maneuvering control by using a BP neural network to adjust PID parameters online.

  8. Nonlinear model predictive control with guaraneed stability based on pesudolinear neural networks

    Institute of Scientific and Technical Information of China (English)

    WANG Yongji; WANG Hong

    2004-01-01

    A nonlinear model predictive control problem based on pseudo-linear neural network (PNN) is discussed, in which the second order on-line optimization method is adopted. The recursive computation of Jacobian matrix is investigated. The stability of the closed loop model predictive control system is analyzed based on Lyapunov theory to obtain the sufficient condition for the asymptotical stability of the neural predictive control system. A simulation was carried out for an exothermic first-order reaction in a continuous stirred tank reactor. It is demonstrated that the proposed control strategy is applicable to some of nonlinear systems.

  9. 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.

  10. Controlling chaos in balanced neural circuits with input spike trains

    Science.gov (United States)

    Engelken, Rainer; Wolf, Fred

    The cerebral cortex can be seen as a system of neural circuits driving each other with spike trains. Here we study how the statistics of these spike trains affects chaos in balanced target circuits.Earlier studies of chaos in balanced neural circuits either used a fixed input [van Vreeswijk, Sompolinsky 1996, Monteforte, Wolf 2010] or white noise [Lajoie et al. 2014]. We study dynamical stability of balanced networks driven by input spike trains with variable statistics. The analytically obtained Jacobian enables us to calculate the complete Lyapunov spectrum. We solved the dynamics in event-based simulations and calculated Lyapunov spectra, entropy production rate and attractor dimension. We vary correlations, irregularity, coupling strength and spike rate of the input and action potential onset rapidness of recurrent neurons.We generally find a suppression of chaos by input spike trains. This is strengthened by bursty and correlated input spike trains and increased action potential onset rapidness. We find a link between response reliability and the Lyapunov spectrum. Our study extends findings in chaotic rate models [Molgedey et al. 1992] to spiking neuron models and opens a novel avenue to study the role of projections in shaping the dynamics of large neural circuits.

  11. Physical therapy intervention in patients with non-cardiac chest pain following a recent cardiac event: A randomized controlled trial

    Directory of Open Access Journals (Sweden)

    Astrid T Berg

    2015-04-01

    Full Text Available Objectives: To assess the effect of two different physical therapy interventions in patients with stable coronary heart disease and non-cardiac chest pain. Methods: A randomized controlled trial was carried out at a university hospital in Norway. A total of 30 patients with known and stable coronary heart disease and self-reported persistent chest pain reproduced by palpation of intercostal trigger points were participating in the study. The intervention was deep friction massage and heat pack versus heat pack only. The primary outcome was pain intensity after the intervention period and 3 months after the last treatment session, measured by Visual Analogue Scale, 0 to 100. Secondary outcome was health-related quality of life. Results: Treatment with deep friction massage and heat pack gave significant pain reduction compared to heat pack only (–17.6, 95% confidence interval: –30.5, –4.7; p < 0.01, and the reduction was persistent at 3 months’ follow-up (–15.2, 95% confidence interval: –28.5, –1.8; p = 0.03. Health-related quality of life improved in all three domains in patients with no significant difference between groups. Conclusion: Deep friction massage combined with heat pack is an efficient treatment of musculoskeletal chest pain in patients with stable coronary heart disease.

  12. GA-BASED PID NEURAL NETVVORK CONTROL FOR MAGNETIC BEARING SYSTEMS

    Institute of Scientific and Technical Information of China (English)

    LI Guodong; ZHANG Qingchun; LIANG Yingchun

    2007-01-01

    In order to overcome the system non-linearity and uncertainty inherent in magnetic bearing systems, a GA(genetic algorithm)-based PID neural network controller is designed and trained to emulate the operation of a complete system (magnetic beating, controller, and power amplifiers).The feasibility of using a neural network to control nonlinear magnetic beating systems with unknown dynamics is demonstrated. The key concept of the control scheme is to use GA to evaluate the candidate solutions (chromosomes), increase the generalization ability of PID neural network and avoid suffering from the local minima problem in network learning due to the use of gradient descent learning method. The simulation results show that the proposed architecture provides well robust performance and better reinforcement learning capability in controlling magnetic bearing systems.

  13. Effect of Rosa aromatherapy on anxiety before cardiac catheterization: A randomized controlled trial

    Directory of Open Access Journals (Sweden)

    Atye Babaii

    2015-09-01

    Full Text Available Background and Objectives: Most patients experience moderate to severe anxiety before cardiac catheterization. This study aimed to investigate the effect of Rosa aromatherapy on anxiety before cardiac catheterization. Methods: In this randomized controlled trial, 60 patients who met the inclusion criteria were conveniently sampled and randomly allocated to the experimental and control groups. Patients in the control group received routine care. In the experimental group, patients received routine care and Rosa aromatherapy for eighteen minutes. The level of anxiety was measured immediately before, and after the treatment. Results: In the stages before and after the study, there were no significant differences between the two groups in the terms of the mean scores of state and total anxiety. However, the mean score of trait anxiety in the experimental group was significantly lower than the control group. Furthermore, there was no significant difference between pre- and post-treatment in both groups. Conclusion: Most of the patients experience moderate to severe anxiety before cardiac catheterization. The findings of this study demonstrate that aromatherapy, as administered in this study, is not beneficial.

  14. The effect of endotoxin on the controllability of cardiac rhythm in rats.

    Science.gov (United States)

    Mazloom, Roham; Shirazi, Amir H; Hajizadeh, Sohrab; Dehpour, Ahmad R; Mani, Ali R

    2014-03-01

    Decreased heart rate variability (HRV) has both diagnostic and prognostic value in patients with sepsis. However, it is not known whether reduced HRV in sepsis reflects an altered input from the autonomic nervous system or a remodeling of the cardiac pacemaker cells by inflammatory mediators. The present study aimed to investigate the effect of endotoxin on the heart rate dynamics of a denervated isolated heart in rats. Saline or endotoxin was injected into rats and their hearts were isolated and perfused. Atrial electrical activity was recorded and memory length in the time-series was assessed using inverse statistical analysis. Memory was defined as a statistical feature that lasts for a period of time and distinguishes the time-series from a random process. Endotoxaemic hearts exhibited a prolonged memory compared to the controls with respect to observing rare events. This indicates that a sudden decelerating event could potentially affect the cardiac rhythm of an endotoxaemic heart for a longer time than the controls. The prolongation of memory is indirectly linked to a reduced controllability in a complex system; therefore our data may provide evidence for a reduced controllability in cardiac rhythm following endotoxaemia.

  15. Use of graphical statistical process control tools to monitor and improve outcomes in cardiac surgery.

    Science.gov (United States)

    Smith, Ian R; Garlick, Bruce; Gardner, Michael A; Brighouse, Russell D; Foster, Kelley A; Rivers, John T

    2013-02-01

    Graphical Statistical Process Control (SPC) tools have been shown to promptly identify significant variations in clinical outcomes in a range of health care settings. We explored the application of these techniques to qualitatively inform the routine cardiac surgical morbidity and mortality (M&M) review process at a single site. Baseline clinical and procedural data relating to 4774 consecutive cardiac surgical procedures, performed between the 1st January 2003 and the 30th April 2011, were retrospectively evaluated. A range of appropriate performance measures and benchmarks were developed and evaluated using a combination of CUmulative SUM (CUSUM) charts, Exponentially Weighted Moving Average (EWMA) charts and Funnel Plots. Charts have been discussed at the unit's routine M&M meetings. Risk adjustment (RA) based on EuroSCORE has been incorporated into the charts to improve performance. Discrete and aggregated measures, including Blood Product/Reoperation, major acute post-procedural complications and Length of Stay/Readmissiontools facilitate near "real-time" performance monitoring allowing early detection and intervention in altered performance. Careful interpretation of charts for group and individual operators has proven helpful in detecting and differentiating systemic vs. individual variation. Copyright © 2012 Australian and New Zealand Society of Cardiac and Thoracic Surgeons (ANZSCTS) and the Cardiac Society of Australia and New Zealand (CSANZ). Published by Elsevier B.V. All rights reserved.

  16. Rough set-based hybrid fuzzy-neural controller design for industrial wastewater treatment.

    Science.gov (United States)

    Chen, W C; Chang, Ni-Bin; Chen, Jeng-Chung

    2003-01-01

    Recent advances in control engineering suggest that hybrid control strategies, integrating some ideas and paradigms existing in different soft computing techniques, such as fuzzy logic, genetic algorithms, rough set theory, and neural networks, may provide improved control performance in wastewater treatment processes. This paper presents an innovative hybrid control algorithm leading to integrate the distinct aspects of indiscernibility capability of rough set theory and search capability of genetic algorithms with conventional neural-fuzzy controller design. The methodology proposed in this study employs a three-stage analysis that is designed in series for generating a representative state function, searching for a set of multi-objective control strategies, and performing a rough set-based autotuning for the neural-fuzzy logic controller to make it applicable for controlling an industrial wastewater treatment process. Research findings in the case study clearly indicate that the use of rough set theory to aid in the neural-fuzzy logic controller design can produce relatively better plant performance in terms of operating cost, control stability, and response time simultaneously, which is effective at least in the selected industrial wastewater treatment plant. Such a methodology is anticipated to be capable of dealing with many other types of process control problems in waste treatment processes by making only minor modifications.

  17. 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.

  18. Impulsive control of stochastic systems with applications in chaos control, chaos synchronization, and neural networks.

    Science.gov (United States)

    Li, Chunguang; Chen, Luonan; Aihara, Kazuyuki

    2008-06-01

    Real systems are often subject to both noise perturbations and impulsive effects. In this paper, we study the stability and stabilization of systems with both noise perturbations and impulsive effects. In other words, we generalize the impulsive control theory from the deterministic case to the stochastic case. The method is based on extending the comparison method to the stochastic case. The method presented in this paper is general and easy to apply. Theoretical results on both stability in the pth mean and stability with disturbance attenuation are derived. To show the effectiveness of the basic theory, we apply it to the impulsive control and synchronization of chaotic systems with noise perturbations, and to the stability of impulsive stochastic neural networks. Several numerical examples are also presented to verify the theoretical results.

  19. Postnatal Cardiac Autonomic Nervous Control in Pediatric Congenital Heart Disease

    Directory of Open Access Journals (Sweden)

    Ineke Nederend

    2016-04-01

    Full Text Available Congenital heart disease is the most common congenital defect. During childhood, survival is generally good but, in adulthood, late complications are not uncommon. Abnormal autonomic control in children with congenital heart disease may contribute considerably to the pathophysiology of these long term sequelae. This narrative review of 34 studies aims to summarize current knowledge on function of the autonomic nervous system in children with a congenital heart defect. Large scale studies that measure both branches of the nervous system for prolonged periods of time in well-defined patient cohorts in various phases of childhood and adolescence are currently lacking. Pending such studies, there is not yet a good grasp on the extent and direction of sympathetic and parasympathetic autonomic function in pediatric congenital heart disease. Longitudinal studies in homogenous patient groups linking autonomic nervous system function and clinical outcome are warranted.

  20. 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.

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

    Science.gov (United States)

    Benyamini, Miri; Zacksenhouse, Miriam

    2015-01-01

    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.

  2. 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.

  3. Neural network controller development for a magnetically suspended flywheel energy storage system

    Science.gov (United States)

    Fittro, Roger L.; Pang, Da-Chen; Anand, Davinder K.

    1994-01-01

    A neural network controller has been developed to accommodate disturbances and nonlinearities and improve the robustness of a magnetically suspended flywheel energy storage system. The controller is trained using the back propagation-through-time technique incorporated with a time-averaging scheme. The resulting nonlinear neural network controller improves system performance by adapting flywheel stiffness and damping based on operating speed. In addition, a hybrid multi-layered neural network controller is developed off-line which is capable of improving system performance even further. All of the research presented in this paper was implemented via a magnetic bearing computer simulation. However, careful attention was paid to developing a practical methodology which will make future application to the actual bearing system fairly straightforward.

  4. Neural network solution for finite-horizon H-infinity constrained optimal control of nonlinear systems

    Institute of Scientific and Technical Information of China (English)

    Tao CHENG; Frank L.LEWIS

    2007-01-01

    In this paper,neural networks are used to approximately solve the finite-horizon constrained input H-infiniy state feedback control problem.The method is based on solving a related Hamilton-Jacobi-Isaacs equation of the corresponding finite-horizon zero-sum game.The game value function is approximated by a neural network wlth timevarying weights.It is shown that the neural network approximation converges uniformly to the game-value function and the resulting almost optimal constrained feedback controller provides closed-loop stability and bounded L2 gain.The result is an almost optimal H-infinity feedback controller with time-varying coefficients that is solved a priori off-line.The effectiveness of the method is shown on the Rotational/Translational Actuator benchmark nonlinear control problem.

  5. Elevator Group-Control Policy Based on Neural Network Optimized by Genetic Algorithm

    Institute of Scientific and Technical Information of China (English)

    SHEN Hong; WAN Jianru; ZHANG Zhichao; LIU Yingpei; LI Guangye

    2009-01-01

    Aiming at the diversity and nonlinearity of the elevator system control target, an effective group method based on a hybrid algorithm of genetic algorithm and neural network is presented in this paper. The genetic algo-rithm is used to search the weight of the neural network. At the same time, the multi-objective-based evaluation function is adopted, in which there are three main indicators including the passenger waiting time, car passengers number and the number of stops. Different weights are given to meet the actual needs. The optimal values of the evaluation function are obtained, and the optimal dispatch control of the elevator group control system based on neural network is realized. By analyzing the running of the elevator group control system, all the processes and steps are presented. The validity of the hybrid algorithm is verified by the dynamic imitation performance.

  6. Neural adaptive control for vibration suppression in composite fin-tip of aircraft.

    Science.gov (United States)

    Suresh, S; Kannan, N; Sundararajan, N; Saratchandran, P

    2008-06-01

    In this paper, we present a neural adaptive control scheme for active vibration suppression of a composite aircraft fin tip. The mathematical model of a composite aircraft fin tip is derived using the finite element approach. The finite element model is updated experimentally to reflect the natural frequencies and mode shapes very accurately. Piezo-electric actuators and sensors are placed at optimal locations such that the vibration suppression is a maximum. Model-reference direct adaptive neural network control scheme is proposed to force the vibration level within the minimum acceptable limit. In this scheme, Gaussian neural network with linear filters is used to approximate the inverse dynamics of the system and the parameters of the neural controller are estimated using Lyapunov based update law. In order to reduce the computational burden, which is critical for real-time applications, the number of hidden neurons is also estimated in the proposed scheme. The global asymptotic stability of the overall system is ensured using the principles of Lyapunov approach. Simulation studies are carried-out using sinusoidal force functions of varying frequency. Experimental results show that the proposed neural adaptive control scheme is capable of providing significant vibration suppression in the multiple bending modes of interest. The performance of the proposed scheme is better than the H(infinity) control scheme.

  7. Adaptive Neural Network Dynamic Inversion with Prescribed Performance for Aircraft Flight Control

    Directory of Open Access Journals (Sweden)

    Wendong Gai

    2013-01-01

    Full Text Available An adaptive neural network dynamic inversion with prescribed performance method is proposed for aircraft flight control. The aircraft nonlinear attitude angle model is analyzed. And we propose a new attitude angle controller design method based on prescribed performance which describes the convergence rate and overshoot of the tracking error. Then the model error is compensated by the adaptive neural network. Subsequently, the system stability is analyzed in detail. Finally, the proposed method is applied to the aircraft attitude tracking control system. The nonlinear simulation demonstrates that this method can guarantee the stability and tracking performance in the transient and steady behavior.

  8. 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....

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

    DEFF Research Database (Denmark)

    Bendtsen, Jan Dimon; Trangbæk, Klaus

    2000-01-01

    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...... the system description into a linear part and a nonlinear part. Linear parameter-varying control synthesis methods are then applied to design a nonlinear control law for this system. Since the model is assumed to have been identified from input-output measurement data only, it must be expected...

  10. Neural-network-based speed controller for induction motors using inverse dynamics model

    Science.gov (United States)

    Ahmed, Hassanein S.; Mohamed, Kamel

    2016-08-01

    Artificial Neural Networks (ANNs) are excellent tools for controller design. ANNs have many advantages compared to traditional control methods. These advantages include simple architecture, training and generalization and distortion insensitivity to nonlinear approximations and nonexact input data. Induction motors have many excellent features, such as simple and rugged construction, high reliability, high robustness, low cost, minimum maintenance, high efficiency, and good self-starting capabilities. In this paper, we propose a neural-network-based inverse model for speed controllers for induction motors. Simulation results show that the ANNs have a high tracing capability.

  11. Adaptive neural control for a class of nonlinearly parametric time-delay systems.

    Science.gov (United States)

    Ho, Daniel W C; Li, Junmin; Niu, Yugang

    2005-05-01

    In this paper, an adaptive neural controller for a class of time-delay nonlinear systems with unknown nonlinearities is proposed. Based on a wavelet neural network (WNN) online approximation model, a state feedback adaptive controller is obtained by constructing a novel integral-type Lyapunov-Krasovskii functional, which also efficiently overcomes the controller singularity problem. It is shown that the proposed method guarantees the semiglobal boundedness of all signals in the adaptive closed-loop systems. An example is provided to illustrate the application of the approach.

  12. Vive les differences! Individual variation in neural mechanisms of executive control

    Science.gov (United States)

    Braver, Todd S.; Cole, Michael W.; Yarkoni, Tal

    2010-01-01

    Summary Investigations of individual differences have become increasingly important in the cognitive neuroscience of executive control. For instance, individual variation in lateral prefrontal cortex function (and that of associated regions) has recently been used to identify contributions of executive control processes to a number of domains, including working memory capacity, anxiety, reward/motivation, and emotion regulation. However, the origins of such individual differences remain poorly understood. Recent progress toward identifying the genetic and environmental sources of variation in neural traits, in combination with progress identifying the causal relationships between neural and cognitive processes, will be essential for developing a mechanistic understanding of executive control. PMID:20381337

  13. Fuzzy neural network control of underwater vehicles based on desired state programming

    Institute of Scientific and Technical Information of China (English)

    LIANG Xiao; LI Ye; XU Yu-ru; WAN Lei; QIN Zai-bai

    2006-01-01

    Due to the nonlinearity and uncertainty, the precise control of underwater vehicles in some intelligent operations hasn't been solved very well yet. A novel method of control based on desired state programming was presented, which used the technique of fuzzy neural network. The structure of fuzzy neural network was constructed according to the moving characters and the back propagation algorithm was deduced. Simulation experiments were conducted on general detection remotely operated vehicle.The results show that there is a great improvement in response and precision over traditional control, and good robustness to the model's uncertainty and external disturbance, which has theoretical and practical value.

  14. Direct Adaptive Soft Computing Neural Control of a Continuous Bioprocess via Second Order Learning

    Science.gov (United States)

    Baruch, Ieroham; Mariaca-Gaspar, Carlos-Roman; Barrera-Cortes, Josefina

    This paper proposes a new Kalman Filter Recurrent Neural Network (KFRNN) topology and a recursive Levenberg-Marquardt (L-M) second order learning algorithm capable to estimate parameters and states of highly nonlinear bioprocess in a noisy environment. The proposed KFRNN identifier, learned by the Backpropagation and L-M learning algorithm, was incorporated in a direct adaptive neural control scheme. The proposed control scheme was applied for real-time soft computing identification and control of a continuous stirred tank bioreactor model, where fast convergence, noise filtering and low mean squared error of reference tracking were achieved.

  15. 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.

  16. Recurrent RBFN-based fuzzy neural network control for X-Y-theta motion control stage using linear ultrasonic motors.

    Science.gov (United States)

    Lin, Faa-Jeng; Shieh, Po-Huang

    2006-12-01

    A recurrent radial basis function network (RBFN) based fuzzy neural network (FNN) control system is proposed to control the position of an X-Y-theta motion control stage using linear ultrasonic motors (LUSMs) to track various contours in this study. The proposed recurrent RBFN-based FNN combines the merits of self-constructing fuzzy neural network (SCFNN), recurrent neural network (RNN), and RBFN. Moreover, the structure and the parameter learning phases of the recurrent RBFN-based FNN are performed concurrently and on line. The structure learning is based on the partition of input space, and the parameter learning is based on the supervised gradient decent method using a delta adaptation law. The experimental results due to various contours show that the dynamic behaviors of the proposed recurrent RBFN-based FNN control system are robust with regard to uncertainties.

  17. A BMP regulatory network controls ectodermal cell fate decisions at the neural plate border.

    Science.gov (United States)

    Reichert, Sabine; Randall, Rebecca A; Hill, Caroline S

    2013-11-01

    During ectodermal patterning the neural crest and preplacodal ectoderm are specified in adjacent domains at the neural plate border. BMP signalling is required for specification of both tissues, but how it is spatially and temporally regulated to achieve this is not understood. Here, using a transgenic zebrafish BMP reporter line in conjunction with double-fluorescent in situ hybridisation, we show that, at the beginning of neurulation, the ventral-to-dorsal gradient of BMP activity evolves into two distinct domains at the neural plate border: one coinciding with the neural crest and the other abutting the epidermis. In between is a region devoid of BMP activity, which is specified as the preplacodal ectoderm. We identify the ligands required for these domains of BMP activity. We show that the BMP-interacting protein Crossveinless 2 is expressed in the BMP activity domains and is under the control of BMP signalling. We establish that Crossveinless 2 functions at this time in a positive-feedback loop to locally enhance BMP activity, and show that it is required for neural crest fate. We further demonstrate that the Distal-less transcription factors Dlx3b and Dlx4b, which are expressed in the preplacodal ectoderm, are required for the expression of a cell-autonomous BMP inhibitor, Bambi-b, which can explain the specific absence of BMP activity in the preplacodal ectoderm. Taken together, our data define a BMP regulatory network that controls cell fate decisions at the neural plate border.

  18. Hypoxic regulation of hand1 controls the fetal-neonatal switch in cardiac metabolism.

    Directory of Open Access Journals (Sweden)

    Ross A Breckenridge

    2013-09-01

    Full Text Available Cardiomyocytes are vulnerable to hypoxia in the adult, but adapted to hypoxia in utero. Current understanding of endogenous cardiac oxygen sensing pathways is limited. Myocardial oxygen consumption is determined by regulation of energy metabolism, which shifts from glycolysis to lipid oxidation soon after birth, and is reversed in failing adult hearts, accompanying re-expression of several "fetal" genes whose role in disease phenotypes remains unknown. Here we show that hypoxia-controlled expression of the transcription factor Hand1 determines oxygen consumption by inhibition of lipid metabolism in the fetal and adult cardiomyocyte, leading to downregulation of mitochondrial energy generation. Hand1 is under direct transcriptional control by HIF1α. Transgenic mice prolonging cardiac Hand1 expression die immediately following birth, failing to activate the neonatal lipid metabolising gene expression programme. Deletion of Hand1 in embryonic cardiomyocytes results in premature expression of these genes. Using metabolic flux analysis, we show that Hand1 expression controls cardiomyocyte oxygen consumption by direct transcriptional repression of lipid metabolising genes. This leads, in turn, to increased production of lactate from glucose, decreased lipid oxidation, reduced inner mitochondrial membrane potential, and mitochondrial ATP generation. We found that this pathway is active in adult cardiomyocytes. Up-regulation of Hand1 is protective in a mouse model of myocardial ischaemia. We propose that Hand1 is part of a novel regulatory pathway linking cardiac oxygen levels with oxygen consumption. Understanding hypoxia adaptation in the fetal heart may allow development of strategies to protect cardiomyocytes vulnerable to ischaemia, for example during cardiac ischaemia or surgery.

  19. Some neural correlates of sensorial and cognitive control of behavior

    Science.gov (United States)

    Ogmen, Haluk; Prakash, R. V.; Moussa, M.

    1992-07-01

    Development and maintenance of unsupervised intelligent activity relies on an active interaction with the environment. Such active exploratory behavior plays an essential role in both the development and adult phases of higher biological systems including humans. Exploration initiates a self-organization process whereby a coherent fusion of different sensory and motor modalities can be achieved (sensory-motor development) and maintained (adult rearrangement). In addition, the development of intelligence depends critically on an active manipulation of the environment. These observations are in sharp contrast with current attempts of artificial intelligence and various neural network models. In this paper, we present a neural network model that combines internal drives and environmental cues to reach behavioral decisions for the exploratory activity. The vision system consists of an ambient and a focal system. The ambient vision system guides eye movements by using nonassociative learning. This sensory based attentional focusing is augmented by a `cognitive' system using models developed for various aspects of frontal lobe function. The combined system has nonassociative learning, reinforcement learning, selective attention, habit formation, and flexible criterion categorization properties.

  20. Design of Neural Network Variable Structure Reentry Control System for Reusable Launch Vehicle

    Institute of Scientific and Technical Information of China (English)

    HU Wei-jun; ZHOU Jun

    2008-01-01

    A flight control system is designed for a reusable launch vehicle with aerodynamic control surfaces and reaction control system based on a variable-structure control and neural network theory. The control problems of coupling among the channels and the uncertainty of model parameters are solved by using the method. High precise and robust tracking of required attitude angles can be achieved in complicated air space. A mathematical model of reusable launch vehicle is pre-sented first, and then a controller of flight system is presented. Base on the mathematical model, the controller is divided into two parts: variable-structure controller and neural network module which is used to modify the parameters of con-troller. This control system decouples the lateral/directional tunnels well with a neural network sliding mode controller and provides a robust and de-coupled tracking for mission angle profiles. After this a control allocation algorithm is employed to allocate the torque moments to aerodynamic control surfaces and thrusters. The final simulation shows that the control system has a good accurate, robust and de-coupled tracking performance. The stable state error is less than 1°, and the overshoot is less than 5%.

  1. Transient Stability Enhancement of Power Systems by Lyapunov-Based Recurrent Neural Networks UPFC Controllers

    Science.gov (United States)

    Chu, Chia-Chi; Tsai, Hung-Chi; Chang, Wei-Neng

    A Lyapunov-based recurrent neural networks unified power flow controller (UPFC) is developed for improving transient stability of power systems. First, a simple UPFC dynamical model, composed of a controllable shunt susceptance on the shunt side and an ideal complex transformer on the series side, is utilized to analyze UPFC dynamical characteristics. Secondly, we study the control configuration of the UPFC with two major blocks: the primary control, and the supplementary control. The primary control is implemented by standard PI techniques when the power system is operated in a normal condition. The supplementary control will be effective only when the power system is subjected by large disturbances. We propose a new Lyapunov-based UPFC controller of the classical single-machine-infinite-bus system for damping enhancement. In order to consider more complicated detailed generator models, we also propose a Lyapunov-based adaptive recurrent neural network controller to deal with such model uncertainties. This controller can be treated as neural network approximations of Lyapunov control actions. In addition, this controller also provides online learning ability to adjust the corresponding weights with the back propagation algorithm built in the hidden layer. The proposed control scheme has been tested on two simple power systems. Simulation results demonstrate that the proposed control strategy is very effective for suppressing power swing even under severe system conditions.

  2. Application of neural networks and fuzzy control to the welding robot

    Institute of Scientific and Technical Information of China (English)

    2000-01-01

    Intelligent control is applied in welding robot. The neural network was used for detecting the deviation of the torch from the center of the gap. The robot tracing the welding line with the fuzzy controller. The proposed method was successfully used to seam tracking in V-groove weld configuration .

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

    DEFF Research Database (Denmark)

    Manoonpong, Poramate; Wörgötter, Florentin; Laksanacharoen, Pudit

    2014-01-01

    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...

  4. 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.

  5. Backstepping Design of Adaptive Neural Fault-Tolerant Control for MIMO Nonlinear Systems.

    Science.gov (United States)

    Gao, Hui; Song, Yongduan; Wen, Changyun

    2016-08-24

    In this paper, an adaptive controller is developed for a class of multi-input and multioutput nonlinear systems with neural networks (NNs) used as a modeling tool. It is shown that all the signals in the closed-loop system with the proposed adaptive neural controller are globally uniformly bounded for any external input in L[₀,∞]. In our control design, the upper bound of the NN modeling error and the gains of external disturbance are characterized by unknown upper bounds, which is more rational to establish the stability in the adaptive NN control. Filter-based modification terms are used in the update laws of unknown parameters to improve the transient performance. Finally, fault-tolerant control is developed to accommodate actuator failure. An illustrative example applying the adaptive controller to control a rigid robot arm shows the validation of the proposed controller.

  6. 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.

  7. [Liver transplant with donated graft after controlled cardiac death. Current situation].

    Science.gov (United States)

    Abradelo De Usera, Manuel; Jiménez Romero, Carlos; Loinaz Segurola, Carmelo; Moreno González, Enrique

    2013-11-01

    An increasing pressure on the liver transplant waiting list, forces us to explore new sources, in order to expand the donor pool. One of the most interesting and with a promising potential, is donation after cardiac death (DCD). Initially, this activity has developed in Spain by means of the Maastricht type II donation in the uncontrolled setting. For different reasons, donation after controlled cardiac death has been reconsidered in our country. The most outstanding circumstance involved in DCD donation is a potential ischemic stress, that could cause severe liver graft cell damage, resulting in an adverse effect on liver transplant results, in terms of complications and outcomes. The complex and particular issues related to DCD Donation will be discussed in this review. Copyright © 2012 AEC. Published by Elsevier Espana. All rights reserved.

  8. An Optimal Control of Bone Marrow in Cancer Chemotherapy by Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    H. Hosseinipour

    2015-09-01

    Full Text Available Although neural network models for cancer chemotherapy have been analyzed since the early seventies, less research has been done in actually formulating them as optimal control problems. In this paper an artificial neural networks-based method for optimal control of bone marrow in cell-cycle-specific chemotherapy is proposed. In this method, we use artificial neural networks for approximating the optimal control problem which maximizes both bone marrow mass and drug`s dose at the same time. The corresponding model be transfer to Hamiltonian function and using Pontryagin principle we create the boundary conditions. After defining boundary conditions, we use the approximating property of artificial networks and put the boundary conditions in error functions to satisfy the limitations..

  9. 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.

  10. Full-state tracking control of a mobile robot using neural networks.

    Science.gov (United States)

    Chaitanya, V Sree Krishna

    2005-10-01

    In this paper a nonholonomic mobile robot with completely unknown dynamics is discussed. A mathematical model has been considered and an efficient neural network is developed, which ensures guaranteed tracking performance leading to stability of the system. The neural network assumes a single layer structure, by taking advantage of the robot regressor dynamics that expresses the highly nonlinear robot dynamics in a linear form in terms of the known and unknown robot dynamic parameters. No assumptions relating to the boundedness is placed on the unmodeled disturbances. It is capable of generating real-time smooth and continuous velocity control signals that drive the mobile robot to follow the desired trajectories. The proposed approach resolves speed jump problem existing in some previous tracking controllers. Further, this neural network does not require offline training procedures. Lyapunov theory has been used to prove system stability. The practicality and effectiveness of the proposed tracking controller are demonstrated by simulation and comparison results.

  11. Fuzzy neural network output maximization control for sensorless wind energy conversion system

    Energy Technology Data Exchange (ETDEWEB)

    Lin, Whei-Min; Hong, Chih-Ming [Department of Electrical Engineering, National Sun Yat-Sen University, Kaohsiung (China); Cheng, Fu-Sheng [Department of Electrical Engineering, Cheng-Shiu University, Kaohsiung (China)

    2010-02-15

    This paper presents the design of an online training fuzzy neural network (FNN) controller with a high-performance speed observer for the induction generator (IG). The proposed output maximization control is achieved without mechanical sensors such as the wind speed or position sensor, and the new control system will deliver maximum electric power with light weight, high efficiency, and high reliability. The estimation of the rotor speed is designed on the basis of the sliding mode control theory. (author)

  12. Non-Minimum Phase Nonlinear System Predictive Control Based on Local Recurrent Neural Networks

    Institute of Scientific and Technical Information of China (English)

    张燕; 陈增强; 袁著祉

    2003-01-01

    After a recursive multi-step-ahead predictor for nonlinear systems based on local recurrent neural networks is introduced, an intelligent PID controller is adopted to correct the errors including identified model errors and accumulated errors produced in the recursive process. Characterized by predictive control, this method can achieve a good control accuracy and has good robustness. A simulation study shows that this control algorithm is very effective.

  13. Toward Building Hybrid Biological/in silico Neural Networks for Motor Neuroprosthetic Control.

    Science.gov (United States)

    Kocaturk, Mehmet; Gulcur, Halil Ozcan; Canbeyli, Resit

    2015-01-01

    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 that 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.

  14. Nurses' perceptions of glycemic control in patients who have undergone cardiac surgery.

    Science.gov (United States)

    Henry, Linda; Dunning, Elizabeth; Halpin, Linda; Stanger, Debra; Martin, Lisa

    2008-01-01

    Previous work investigating the effect of glycemic control in patients who underwent cardiac surgery has demonstrated that obtaining and maintaining blood glucose values between 80 and 120 is imperative in achieving excellent clinical outcomes in a patient who have undergone cardiac surgery. However, the caregiver's workload associated with meeting this goal is only now beginning to be understood. This qualitative study used focus groups held on 3 consecutive days to interview nurses in the cardiovascular intensive care unit and cardiovascular step-down unit about their thoughts on glycemic control.Three research questions were developed to help guide the focus group discussions. Ten nurses, 3 from cardiovascular intensive care unit and 7 from cardiovascular step-down unit, participated in the focus groups and saturation was accomplished. The essence of the nurses' message was that they recognize glycemic control as a very important part of their patient care. However, to be able to perform this intervention, they need available equipment, a designated person to obtain all blood glucose values, periodic updates on patient outcomes related to glycemic control, and a less intrusive way to draw the patients' blood. The ability of the nurses to obtain glycemic control is hindered by the lack of time, lack of necessary resources/equipment, lack of knowledge about the long-term outcomes resulting from glycemic control, and the discomfort to patients caused by the frequent blood draws. Hospitals need to investigate alternative mechanisms that will assist the nurse in meeting this goal.

  15. 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...

  16. Acetylation control of cardiac fatty acid β-oxidation and energy metabolism in obesity, diabetes, and heart failure.

    Science.gov (United States)

    Fukushima, Arata; Lopaschuk, Gary D

    2016-12-01

    Alterations in cardiac energy metabolism are an important contributor to the cardiac pathology associated with obesity, diabetes, and heart failure. High rates of fatty acid β-oxidation with cardiac insulin resistance represent a cardiac metabolic hallmark of diabetes and obesity, while a marginal decrease in fatty acid oxidation and a prominent decrease in insulin-stimulated glucose oxidation are commonly seen in the early stages of heart failure. Alterations in post-translational control of energy metabolic processes have recently been identified as an important contributor to these metabolic changes. In particular, lysine acetylation of non-histone proteins, which controls a diverse family of mitochondrial metabolic pathways, contributes to the cardiac energy derangements seen in obesity, diabetes, and heart failure. Lysine acetylation is controlled both via acetyltransferases and deacetylases (sirtuins), as well as by non-enzymatic lysine acetylation due to increased acetyl CoA pool size or dysregulated nicotinamide adenine dinucleotide (NAD(+)) metabolism (which stimulates sirtuin activity). One of the important mitochondrial acetylation targets are the fatty acid β-oxidation enzymes, which contributes to alterations in cardiac substrate preference during the course of obesity, diabetes, and heart failure, and can ultimately lead to cardiac dysfunction in these disease states. This review will summarize the role of lysine acetylation and its regulatory control in the context of mitochondrial fatty acid β-oxidation. The functional contribution of cardiac protein lysine acetylation to the shift in cardiac energy substrate preference that occurs in obesity, diabetes, and especially in the early stages of heart failure will also be reviewed. This article is part of a Special Issue entitled: The role of post-translational protein modifications on heart and vascular metabolism edited by Jason R.B. Dyck & Jan F.C. Glatz.

  17. Speed control of SR motor by self-tuning fuzzy PI controller with artificial neural network

    Indian Academy of Sciences (India)

    Ercument Karakas; Soner Vardarbasi

    2007-10-01

    In this work, the dynamic model, flux-current-rotor position and torque-current-rotor position values of the switched reluctance motor (SRM) are obtained in MATLAB/Simulink. Motor control speed is achieved by self-tuning fuzzy PI (Proportional Integral) controller with artificial neural network tuning (NSTFPI). Performance of NSTFPI controller is compared with performance of fuzzy logic (FL) and fuzzy logic PI (FLPI) controllers in respect of rise time, settling time, overshoot and steady state error

  18. Speed Control of BLDC Motor Based on Recurrent Wavelet Neural Network

    Directory of Open Access Journals (Sweden)

    Adel A. Obed

    2014-12-01

    Full Text Available In recent years, artificial intelligence techniques such as wavelet neural network have been applied to control the speed of the BLDC motor drive. The BLDC motor is a multivariable and nonlinear system due to variations in stator resistance and moment of inertia. Therefore, it is not easy to obtain a good performance by applying conventional PID controller. The Recurrent Wavelet Neural Network (RWNN is proposed, in this paper, with PID controller in parallel to produce a modified controller called RWNN-PID controller, which combines the capability of the artificial neural networks for learning from the BLDC motor drive and the capability of wavelet decomposition for identification and control of dynamic system and also having the ability of self-learning and self-adapting. The proposed controller is applied for controlling the speed of BLDC motor which provides a better performance than using conventional controllers with a wide range of speed. The parameters of the proposed controller are optimized using Particle Swarm Optimization (PSO algorithm. The BLDC motor drive with RWNN-PID controller through simulation results proves a better in the performance and stability compared with using conventional PID and classical WNN-PID controllers.

  19. Partial state feedback control of chaotic neural network and its application

    Energy Technology Data Exchange (ETDEWEB)

    He Guoguang [Aihara Complexity Modelling Project, ERATO, JST, Tokyo 153-8505 (Japan); Department of Physics, College of Science, Zhejiang University, Hangzhou 310027 (China); Institute of Industrial Science, University of Tokyo, Tokyo 153-8505 (Japan)], E-mail: gghe@aihara.jst.go.jp; Shrimali, Manish Dev; Aihara, Kazuyuki [Aihara Complexity Modelling Project, ERATO, JST, Tokyo 153-8505 (Japan); Institute of Industrial Science, University of Tokyo, Tokyo 153-8505 (Japan)

    2007-11-12

    The chaos control in the chaotic neural network is studied using the partial state feedback with a control signal from a few control neurons. The controlled CNN converges to one of the stored patterns with a period which depends on the initial conditions, i.e., the set of control neurons and other control parameters. We show that the controlled CNN can distinguish between two initial patterns even if they have a small difference. This implies that such a controlled CNN can be feasibly applied to information processing such as pattern recognition.

  20. 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.

  1. Synchronization of neural networks with stochastic perturbation via aperiodically intermittent control.

    Science.gov (United States)

    Zhang, Wei; Li, Chuandong; Huang, Tingwen; Xiao, Mingqing

    2015-11-01

    In this paper, the synchronization problem for neural networks with stochastic perturbation is studied with intermittent control via adaptive aperiodicity. Under the framework of stochastic theory and Lyapunov stability method, we develop some techniques of intermittent control with adaptive aperiodicity to achieve the synchronization of a class of neural networks, modeled by stochastic systems. Some effective sufficient conditions are established for the realization of synchronization of the underlying network. Numerical simulations of two examples are provided to illustrate the theoretical results obtained in the paper.

  2. Application of neural networks for permanent magnet synchronous motor direct torque control

    Institute of Scientific and Technical Information of China (English)

    Zhang Chunmei; Liu Heping; Chen Shujin; Wang Fangjun

    2008-01-01

    Neural networks require a lot of training to understand the model of a plant or a process. Issues such as learning speed, stability, and weight convergence remain as areas of research and comparison of many training algorithms. The application of neural networks to control interior permanent magnet synchronous motor using direct torque control (DTC) is discussed. A neural network is used to emulate the state selector of the DTC. The neural networks used are the back-propagation and radial basis function. To reduce the training patterns and increase the execution speed of the training process, the inputs of switching table are converted to digital signals, i.e., one bit represent the flux error, one bit the torque error, and three bits the region of stator flux. Computer simulations of the motor and neural-network system using the two approaches are presented and compared. Discussions about the back-propagation and radial basis function as the most promising training techniques are presented, giving its advantages and disadvantages. The system using back-propagation and radial basis function networks controller has quick parallel speed and high torque response.

  3. 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.

  4. 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.

  5. Prevalence of cardiac sarcoidosis in white population: a case–control study

    Science.gov (United States)

    Martusewicz-Boros, Magdalena M.; Boros, Piotr W.; Wiatr, Elżbieta; Zych, Jacek; Piotrowska-Kownacka, Dorota; Roszkowski-Śliż, Kazimierz

    2016-01-01

    Abstract Cardiac sarcoidosis (CS) is a life-threatening and underdiagnosed manifestation of the disease, which requires a complicated and expensive diagnostic pathway. There is a need for simple tool for practitioners to determine the risk of CS without access to specialized equipment. The aim of study was to determine the prevalence of CS in a group of patients diagnosed with or followed up because of sarcoidosis. A secondary objective was the search for factors associated with heart involvement. We performed a prospective case–control study (screening analysis) in consecutive sarcoidosis patients collected from October 2012 to September 2015. Cardiac magnetic resonance (CMR) imaging was performed to confirm or exclude cardiac involvement in all patients. The study was conducted in a hospital-based referral center for patients with sarcoidosis and other interstitial lung diseases. Analysis was performed in a group of 201 patients (all white) with biopsy-proven sarcoidosis, mean age 41.4 ± 10.2, 121 of them (60.2%) males. Four patients with previously recognized cardiac diseases, which make CMR imaging for CS inconclusive, were not included. Cardiac involvement was detected by CMR in 49 patients (24.4%). Factors associated with an increased risk of CS (univariate analyses) included male sex (odds ratio [OR]: 2.5; 1.21–5.16, P = 0.01), cardiac-related symptoms (OR: 3.53; 1.81–6.89, P = 0.0002), extrathoracic sarcoidosis (OR: 3.48; 1.77–6.84, P = 0.0003), elevated serum NT-proBNP (OR: 3.82; 1.55–9.42, P = 0.004), any electrocardiography abnormality (OR: 5.38; 2.48–11.67, P = 0.0001), and contemporary radiological progression sarcoidosis in the lungs (OR: 2.98; 1.52–5.84, P = 0.001). Abnormalities in echocardiography and Holter ECG were also risk factors, but not significant in multivariate analyses. A CS Risk Index was developed using a multivariate model to predict CS, achieving an accuracy of 82%, sensitivity of 50

  6. Autonomous Neural Network Controller for Adaptive Material Handling

    Science.gov (United States)

    1992-02-28

    proportional-integral-differential (PID) control. Although simple, PID controllers have several shortcomings that prevent them from being useful for...controlling robots. These methods are much more complicated than the prevalent PID controllers . They involve one or more optimal criteria and need to

  7. 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

  8. Control of a Shunt Active Power Filter with Neural Networks—Theory and Practical Results

    Science.gov (United States)

    Villalva, Marcelo G.; Filho, Ernesto Ruppert

    This paper presents theoretical studies and practical results obtained with a four-wire shunt active power filter fully controlled with neural networks. The paper is focused on a current compensation method based on adaptive linear elements (adalines), which are powerful and easy-to-use neural networks. The reader will find here an introduction about these networks, an explanatory section about the achievement of Fourier series with adalines, and the full description of an adaline-based selective current compensator. The paper also brings a quick discussion about the use of a feedforward neural network in the current controller of the active filter, as well as simulation and experimental results obtained with the prototype of an active power filter.

  9. 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.

  10. 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.

  11. Preoperative education interventions to reduce anxiety and improve recovery among cardiac surgery patients: a review of randomised controlled trials.

    Science.gov (United States)

    Guo, Ping

    2015-01-01

    To update evidence of the effectiveness of preoperative education among cardiac surgery patients. Patients awaiting cardiac surgery may experience high levels of anxiety and depression, which can adversely affect their existing disease and surgery and result in prolonged recovery. There is evidence that preoperative education interventions can lead to improved patient experiences and positive postoperative outcomes among a mix of general surgical patients. However, a previous review suggested limited evidence to support the positive impact of preoperative education on patients' recovery from cardiac surgery. Comprehensive review of the literature. The Cochrane Central Register of Controlled Trials from the Cochrane Library, MEDLINE, CINAHL, PsycINFO, EMBASE and Web of Science were searched for English-language articles published between 2000-2011. Original articles were included reporting randomised controlled trials of cardiac preoperative education interventions. Six trials were identified and have produced conflicting findings. Some trials have demonstrated the effects of preoperative education on improving physical and psychosocial recovery of cardiac patients, while others found no evidence that patients' anxiety is reduced or of any effect on pain or hospital stay. Evidence of the effectiveness of preoperative education interventions among cardiac surgery patients remains inconclusive. Further research is needed to evaluate cardiac preoperative education interventions for sustained effect and in non-Western countries. A nurse-coordinated multidisciplinary preoperative education approach may offer a way forward to provide a more effective and efficient service. Staff training in developing and delivering such interventions is a priority. © 2014 John Wiley & Sons Ltd.

  12. Effect of Weight Gain on Cardiac Autonomic Control During Wakefulness and Sleep

    Science.gov (United States)

    Adachi, Taro; Sert-Kuniyoshi, Fatima H.; Calvin, Andrew D.; Singh, Prachi; Romero-Corral, Abel; van der Walt, Christelle; Davison, Diane E.; Bukartyk, Jan; Konecny, Tomas; Pusalavidyasagar, Snigdha; Sierra-Johnson, Justo; Somers, Virend K.

    2012-01-01

    Obesity has been associated with increased cardiac sympathetic activation during wakefulness, but the effect on sleep-related sympathetic modulation is not known. The aim of this study was to investigate the effect of fat gain on cardiac autonomic control during wakefulness and sleep in humans. We performed a randomized controlled study to assess the effects of fat gain on heart rate variability (HRV). We recruited 36 healthy volunteers, who were randomized to either a standardized diet to gain approximately 4 kg over 8 weeks followed by an 8 week weight loss period (n=20), or to serve as a weight-maintainer control (n=16). An overnight polysomnogram with power spectral analysis of HRV was performed at baseline, after weight gain, and after weight loss to determine the ratio of low frequency (LF) to high frequency (HF) power, and to examine the relationship between changes in HRV and changes in insulin, leptin and adiponectin levels. Mean weight gain was 3.9 kg in the fat gain group versus 0.1 kg in the maintainer group. LF/HF increased both during wakefulness and sleep after fat gain and returned to baseline after fat loss in the fat gain group, and did not change in the control group. Insulin, leptin and adiponectin also increased after fat gain and fell after fat loss, but no clear pattern of changes were seen that correlated consistently with changes in HRV. Short-term fat gain in healthy subjects is associated with increased cardiac sympathetic activation during wakefulness and sleep but the mechanisms remain unclear. PMID:21357280

  13. Dexmedetomidine versus Propofol Sedation Reduces Delirium after Cardiac Surgery: A Randomized Controlled Trial.

    Science.gov (United States)

    Djaiani, George; Silverton, Natalie; Fedorko, Ludwik; Carroll, Jo; Styra, Rima; Rao, Vivek; Katznelson, Rita

    2016-02-01

    Postoperative delirium (POD) is a serious complication after cardiac surgery. Use of dexmedetomidine to prevent delirium is controversial. The authors hypothesized that dexmedetomidine sedation after cardiac surgery would reduce the incidence of POD. After institutional ethics review board approval, and informed consent, a single-blinded, prospective, randomized controlled trial was conducted in patients 60 yr or older undergoing cardiac surgery. Patients with a history of serious mental illness, delirium, and severe dementia were excluded. Upon admission to intensive care unit (ICU), patients received either dexmedetomidine (0.4 μg/kg bolus followed by 0.2 to 0.7 μg kg h infusion) or propofol (25 to 50 μg kg min infusion) according to a computer-generated randomization code in blocks of four. Assessment of delirium was performed with confusion assessment method for ICU or confusion assessment method after discharge from ICU at 12-h intervals during the 5 postoperative days. Primary outcome was the incidence of POD. POD was present in 16 of 91 (17.5%) and 29 of 92 (31.5%) patients in dexmedetomidine and propofol groups, respectively (odds ratio, 0.46; 95% CI, 0.23 to 0.92; P = 0.028). Median onset of POD was on postoperative day 2 (1 to 4 days) versus 1 (1 to 4 days), P = 0.027, and duration of POD 2 days (1 to 4 days) versus 3 days (1 to 5 days), P = 0.04, in dexmedetomidine and propofol groups, respectively. When compared with propofol, dexmedetomidine sedation reduced incidence, delayed onset, and shortened duration of POD in elderly patients after cardiac surgery. The absolute risk reduction for POD was 14%, with a number needed to treat of 7.1.

  14. Neural network based control of Doubly Fed Induction Generator in wind power generation

    Science.gov (United States)

    Barbade, Swati A.; Kasliwal, Prabha

    2012-07-01

    To complement the other types of pollution-free generation wind energy is a viable option. Previously wind turbines were operated at constant speed. The evolution of technology related to wind systems industry leaded to the development of a generation of variable speed wind turbines that present many advantages compared to the fixed speed wind turbines. In this paper the phasor model of DFIG is used. This paper presents a study of a doubly fed induction generator driven by a wind turbine connected to the grid, and controlled by artificial neural network ANN controller. The behaviour of the system is shown with PI control, and then as controlled by ANN. The effectiveness of the artificial neural network controller is compared to that of a PI controller. The SIMULINK/MATLAB simulation for Doubly Fed Induction Generator and corresponding results and waveforms are displayed.

  15. Speed Control of Two-Mass System Using Neural Network Estimator

    Energy Technology Data Exchange (ETDEWEB)

    Lee, Kyo Beum [Korea University (Korea, Republic of); Song, Joong Ho; Choi, Ick; Kim, Kwang Bae [Korea Institute of Science and Technology (Korea, Republic of); Lee, Kwang Won [Aju Univeristy (Korea, Republic of)

    1999-03-01

    A new control scheme using a torsional torque estimator based on a neural network is proposed and investigated for improving control characteristics of the high-performance motion control system. This control method presents better performance in the corresponding speed vibration response, compared with the disturbance observer-based control method. This result comes from the fact that the proposed neural network estimator keeps the self-learning capability, whereas the disturbance observer-based torque estimator with low pass filter should adjust the time constant of the adopted filter according to the natural resonance frequency determined by considering the system parameters varied. The simulation results shows the validity of the proposed control scheme. (author). 13 refs., 13 figs., 1 tab.

  16. Electron beam energy stabilization using a neural network hybrid controller at the Australian Synchrotron Linac.

    Energy Technology Data Exchange (ETDEWEB)

    Meier, E.; Morgan, M. J.; Biedron, S. G.; LeBlanc, G.; Wu, J. (OTD-ESE); (Monash Univ.); (Australian Synchrotron Project); (SLAC National Accelerator Lab.)

    2009-01-01

    This paper describes the implementation of a neural network hybrid controller for energy stabilization at the Australian Synchrotron Linac. The structure of the controller consists of a neural network (NNET) feed forward control, augmented by a conventional Proportional-Integral (PI) feedback controller to ensure stability of the system. The system is provided with past states of the machine in order to predict its future state, and therefore apply appropriate feed forward control. The NNET is able to cancel multiple frequency jitter in real-time. When it is not performing optimally due to jitter changes, the system can successfully be augmented by the PI controller to attenuate the remaining perturbations. With a view to control the energy and bunch length at the FERMI{at}Elettra Free Electron Laser (FEL), the present study considers a neural network hybrid feed forward-feedback type of control to rectify limitations related to feedback systems, such as poor response for high jitter frequencies or limited bandwidth, while ensuring robustness of control. The Australian Synchrotron Linac is equipped with a beam position monitor (BPM), that was provided by Sincrotrone Trieste from a former transport line thus allowing energy measurements and energy control experiments. The present study will consequently focus on correcting energy jitter induced by variations in klystron phase and voltage.

  17. 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...

  18. Adaptive Neural Control Based on High Order Integral Chained Differentiator for Morphing Aircraft

    Directory of Open Access Journals (Sweden)

    Zhonghua Wu

    2015-01-01

    Full Text Available This paper presents an adaptive neural control for the longitudinal dynamics of a morphing aircraft. Based on the functional decomposition, it is reasonable to decompose the longitudinal dynamics into velocity and altitude subsystems. As for the velocity subsystem, the adaptive control is proposed via dynamic inversion method using neural network. To deal with input constraints, the additional compensation system is employed to help engine recover from input saturation rapidly. The highlight is that high order integral chained differentiator is used to estimate the newly defined variables and an adaptive neural controller is designed for the altitude subsystem where only one neural network is employed to approximate the lumped uncertain nonlinearity. The altitude subsystem controller is considerably simpler than the ones based on backstepping. It is proved using Lyapunov stability theory that the proposed control law can ensure that all the tracking error converges to an arbitrarily small neighborhood around zero. Numerical simulation study demonstrates the effectiveness of the proposed strategy, during the morphing process, in spite of some uncertain system nonlinearity.

  19. Integration of Online Parameter Identification and Neural Network for In-Flight Adaptive Control

    Science.gov (United States)

    Hageman, Jacob J.; Smith, Mark S.; Stachowiak, Susan

    2003-01-01

    An indirect adaptive system has been constructed for robust control of an aircraft with uncertain aerodynamic characteristics. This system consists of a multilayer perceptron pre-trained neural network, online stability and control derivative identification, a dynamic cell structure online learning neural network, and a model following control system based on the stochastic optimal feedforward and feedback technique. The pre-trained neural network and model following control system have been flight-tested, but the online parameter identification and online learning neural network are new additions used for in-flight adaptation of the control system model. A description of the modification and integration of these two stand-alone software packages into the complete system in preparation for initial flight tests is presented. Open-loop results using both simulation and flight data, as well as closed-loop performance of the complete system in a nonlinear, six-degree-of-freedom, flight validated simulation, are analyzed. Results show that this online learning system, in contrast to the nonlearning system, has the ability to adapt to changes in aerodynamic characteristics in a real-time, closed-loop, piloted simulation, resulting in improved flying qualities.

  20. Nonlinear Model Predictive Control Based on a Self-Organizing Recurrent Neural Network.

    Science.gov (United States)

    Han, Hong-Gui; Zhang, Lu; Hou, Ying; Qiao, Jun-Fei

    2016-02-01

    A nonlinear model predictive control (NMPC) scheme is developed in this paper based on a self-organizing recurrent radial basis function (SR-RBF) neural network, whose structure and parameters are adjusted concurrently in the training process. The proposed SR-RBF neural network is represented in a general nonlinear form for predicting the future dynamic behaviors of nonlinear systems. To improve the modeling accuracy, a spiking-based growing and pruning algorithm and an adaptive learning algorithm are developed to tune the structure and parameters of the SR-RBF neural network, respectively. Meanwhile, for the control problem, an improved gradient method is utilized for the solution of the optimization problem in NMPC. The stability of the resulting control system is proved based on the Lyapunov stability theory. Finally, the proposed SR-RBF neural network-based NMPC (SR-RBF-NMPC) is used to control the dissolved oxygen (DO) concentration in a wastewater treatment process (WWTP). Comparisons with other existing methods demonstrate that the SR-RBF-NMPC can achieve a considerably better model fitting for WWTP and a better control performance for DO concentration.

  1. Adaptive Sampling for WSAN Control Applications Using Artificial Neural Networks

    OpenAIRE

    2012-01-01

    Wireless sensor actuator networks are becoming a solution for control applications. Reliable data transmission and real time constraints are the most significant challenges. Control applications will have some Quality of Service (QoS) requirements from the sensor network, such as minimum delay and guaranteed delivery of packets. We investigate variable sampling method to mitigate the effects of time delays in wireless networked control systems using an observer based control system model. Our...

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

    DEFF Research Database (Denmark)

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

    1998-01-01

    qualities. The controller is a non-linear version of the well-known generalized predictive controller developed in linear control theory. It involves minimization of a cost function which in the present case has to be done numerically. Therefore, we develop the numerical algorithms necessary in substantial...

  3. Cardiac rehabilitation adapted to transient ischaemic attack and stroke (CRAFTS: a randomised controlled trial

    Directory of Open Access Journals (Sweden)

    Blake Catherine

    2009-02-01

    Full Text Available Abstract Background Coronary Heart Disease and Cerebrovascular Disease share many predisposing, modifiable risk factors (hypertension, abnormal blood lipids and lipoproteins, cigarette smoking, physical inactivity, obesity and diabetes mellitus. Lifestyle interventions and pharmacological therapy are recognised as the cornerstones of secondary prevention. Cochrane review has proven the benefits of programmes incorporating exercise and lifestyle counselling in the cardiac disease population. A Cochrane review highlighted as priority, the need to establish feasibility and efficacy of exercise based interventions for Cerebrovascular Disease. Methods A single blind randomised controlled trial is proposed to examine a primary care cardiac rehabilitation programme for adults post transient ischemic attack (TIA and stroke in effecting a positive change in the primary outcome measures of cardiac risk scores derived from Blood Pressure, lipid profile, smoking and diabetic status and lifestyle factors of habitual smoking, exercise and healthy eating participation. Secondary outcomes of interest include health related quality of life as measured by the Hospital Anxiety and Depression Scale, the Stroke Specific Quality of Life scale and WONCA COOP Functional Health Status charts and cardiovascular fitness as measured by a sub-maximal fitness test. A total of 144 patients, over 18 years of age with confirmed diagnosis of ischaemic stroke or TIA, will be recruited from Dublin community stroke services and two tertiary T.I.A clinics. Exclusion criteria will include oxygen dependence, unstable cardiac conditions, uncontrolled diabetes, major medical conditions, claudication, febrile illness, pregnancy or cognitive impairment. Participants will be block-statified, randomly allocated to one of two groups using a pre-prepared computer generated randomisation schedule. Both groups will receive a two hour education class on risk reduction post stroke. The

  4. Neural networks for modelling and control of a non-linear dynamic system

    OpenAIRE

    Murray-Smith, R.; Neumerkel, D.; Sbarbaro-Hofer, D.

    1992-01-01

    The authors describe the use of neural nets to model and control a nonlinear second-order electromechanical model of a drive system with varying time constants and saturation effects. A model predictive control structure is used. This is compared with a proportional-integral (PI) controller with regard to performance and robustness against disturbances. Two feedforward network types, the multilayer perceptron and radial-basis-function nets, are used to model the system. The problems involved ...

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

    Directory of Open Access Journals (Sweden)

    A. Selwin Mich Priyadharson

    2014-01-01

    Full Text Available 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 uncertainty of the process and frequent load changes. This study presents the design of neuro controllers to regulate level, cascaded with fuzzy controller to control flow in gate valve to the turbine. A prototype model is fabricated in the laboratory as experimental setup for flow and level control and real time simulation studies were carried out using PID and neural cascaded with fuzzy scheme. The designed prototype model is fabricated with 5 levels in the upper tank and 2 levels in the lower tank. Based on the outputs of the level sensors from the upper and lower tanks, the ladder logic is actuated. This project work uses PLC of Bernecker and Rainer (B and R Industrial Automation inbuilt with 20 digital inputs and provides 12 potential free outputs to control the miniaturized process depicted in this work. Finally, the performance of the proposed neural cascaded with fuzzy scheme is evaluated by simulation results by comparing with conventional controllers output using real time data obtained from the hydro power plant. The advantages of the proposed neural cascaded with fuzzy scheme over the existing controllers are highlighted.

  6. Optimal control of end-port glass tank furnace regenerator temperature based on artificial neural network

    Institute of Scientific and Technical Information of China (English)

    CHEN Xi; ZHAO Guo-zhu

    2005-01-01

    In the paper, an artificial neural network (ANN) method is put forward to optimize melting temperature control, which reveals the nonlinear relationships of tank melting temperature disturbances with secondary wind flow and fuel pressure, implements dynamic feed-forward complementation and dynamic correctional ratio between air and fuel in the main control system. The application to Anhui Fuyang Glass Factory improved the control character of the melting temperature greatly.

  7. Use of the Analog Neural Networks in the Adaptive Antenna Control Systems

    Directory of Open Access Journals (Sweden)

    Z. Raida

    2002-09-01

    Full Text Available In the paper, original control system of adaptive antennas, which isbased on Kalman filter, is presented and compared with earlierapproaches to this problem. The designed control circuit eliminatessome disadvantages of the control circuits based on the classicalKalman neural network and the Wang one, and enables a real timeprocessing of quickly changing signals processed by adaptive antennas.Especially, the dependence of the convergence rate on ratio ofeigenvalues and the risk of instability are significantly reduced.

  8. A Fast Adaptive Artificial Neural Network Controller for Flexible Link Manipulators

    OpenAIRE

    Amin Riad Maouche; Hosna Meddahi

    2016-01-01

    This paper describes a hybrid approach to the problem of controlling flexible link manipulators in the dynamic phase of the trajectory. A flexible beam/arm is an appealing option for civil and military applications, such as space-based robot manipulators. However, flexibility brings with it unwanted oscillations and severe chattering which may even lead to an unstable system. To tackle these challenges, a novel control architecture scheme is presented. First, a neural network controller based...

  9. Neural Network-Based Adaptive Backstepping Control for Hypersonic Flight Vehicles with Prescribed Tracking Performance

    OpenAIRE

    Zhu Guoqiang; Liu Jinkun

    2015-01-01

    An adaptive neural control scheme is proposed for a class of generic hypersonic flight vehicles. The main advantages of the proposed scheme include the following: (1) a new constraint variable is defined to generate the virtual control that forces the tracking error to fall within prescribed boundaries; (2) RBF NNs are employed to compensate for complex and uncertain terms to solve the problem of controller complexity; (3) only one parameter needs to be updated online at each design step, whi...

  10. The Role of the Suprachiasmatic Nucleus in Cardiac Autonomic Control during Sleep

    Science.gov (United States)

    Joustra, S. D.; Reijntjes, R. H.; Pereira, A. M.; Lammers, G. J.; Biermasz, N. R.; Thijs, R. D.

    2016-01-01

    Background The suprachiasmatic nucleus (SCN) may play an important role in central autonomic control, since its projections connect to (para)sympathetic relay stations in the brainstem and spinal cord. The cardiac autonomic modifications during nighttime may therefore not only result from direct effects of the sleep-related changes in the central autonomic network, but also from endogenous circadian factors as directed by the SCN. To explore the influence of the SCN on autonomic fluctuations during nighttime, we studied heart rate and its variability (HRV) in a clinical model of SCN damage. Methods Fifteen patients in follow-up after surgical treatment for nonfunctioning pituitary macroadenoma (NFMA) compressing the optic chiasm (8 females, 26–65 years old) and fifteen age-matched healthy controls (5 females, 30–63 years) underwent overnight ambulatory polysomnography. Eleven patients had hypopituitarism and received adequate replacement therapy. HRV was calculated for each 30-second epoch and corrected for sleep stage, arousals, and gender using mixed effect regression models. Results Compared to controls, patients spent more time awake after sleep onset and in NREM1-sleep, and less in REM-sleep. Heart rate, low (LF) and high frequency (HF) power components and the LF/HF ratio across sleep stages were not significantly different between groups. Conclusions These findings suggest that the SCN does not play a dominant role in cardiac autonomic control during sleep. PMID:27010631

  11. The Role of the Suprachiasmatic Nucleus in Cardiac Autonomic Control during Sleep.

    Directory of Open Access Journals (Sweden)

    S D Joustra

    Full Text Available The suprachiasmatic nucleus (SCN may play an important role in central autonomic control, since its projections connect to (parasympathetic relay stations in the brainstem and spinal cord. The cardiac autonomic modifications during nighttime may therefore not only result from direct effects of the sleep-related changes in the central autonomic network, but also from endogenous circadian factors as directed by the SCN. To explore the influence of the SCN on autonomic fluctuations during nighttime, we studied heart rate and its variability (HRV in a clinical model of SCN damage.Fifteen patients in follow-up after surgical treatment for nonfunctioning pituitary macroadenoma (NFMA compressing the optic chiasm (8 females, 26-65 years old and fifteen age-matched healthy controls (5 females, 30-63 years underwent overnight ambulatory polysomnography. Eleven patients had hypopituitarism and received adequate replacement therapy. HRV was calculated for each 30-second epoch and corrected for sleep stage, arousals, and gender using mixed effect regression models.Compared to controls, patients spent more time awake after sleep onset and in NREM1-sleep, and less in REM-sleep. Heart rate, low (LF and high frequency (HF power components and the LF/HF ratio across sleep stages were not significantly different between groups.These findings suggest that the SCN does not play a dominant role in cardiac autonomic control during sleep.

  12. Neural effects of cognitive control load on auditory selective attention.

    Science.gov (United States)

    Sabri, Merav; Humphries, Colin; Verber, Matthew; Liebenthal, Einat; Binder, Jeffrey R; Mangalathu, Jain; Desai, Anjali

    2014-08-01

    Whether and how working memory disrupts or alters auditory selective attention is unclear. We compared simultaneous event-related potentials (ERP) and functional magnetic resonance imaging (fMRI) responses associated with task-irrelevant sounds across high and low working memory load in a dichotic-listening paradigm. Participants performed n-back tasks (1-back, 2-back) in one ear (Attend ear) while ignoring task-irrelevant speech sounds in the other ear (Ignore ear). The effects of working memory load on selective attention were observed at 130-210ms, with higher load resulting in greater irrelevant syllable-related activation in localizer-defined regions in auditory cortex. The interaction between memory load and presence of irrelevant information revealed stronger activations primarily in frontal and parietal areas due to presence of irrelevant information in the higher memory load. Joint independent component analysis of ERP and fMRI data revealed that the ERP component in the N1 time-range is associated with activity in superior temporal gyrus and medial prefrontal cortex. These results demonstrate a dynamic relationship between working memory load and auditory selective attention, in agreement with the load model of attention and the idea of common neural resources for memory and attention.

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

    Science.gov (United States)

    Ramseyer, Craig A.; Mote, Thomas L.

    2016-10-01

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

  14. Manual hyperinflation partly prevents reductions of functional residual capacity in cardiac surgical patients - a randomized controlled trial

    OpenAIRE

    Paulus, Frederique; Veelo, Denise P; Selma B. de Nijs; Beenen, Ludo FM; Bresser, Paul; de Mol, Bas AJM; Binnekade, Jan M; Schultz, Marcus J

    2011-01-01

    Introduction Cardiac surgery is associated with post-operative reductions of functional residual capacity (FRC). Manual hyperinflation (MH) aims to prevent airway plugging, and as such could prevent the reduction of FRC after surgery. The main purpose of this study was to determine the effect of MH on post-operative FRC of cardiac surgical patients. Methods This was a randomized controlled trial of patients after elective coronary artery bypass graft and/or valve surgery admitted to the inten...

  15. Decentralized adaptive neural network sliding mode position/force control of constrained reconfigurable manipulators

    Institute of Scientific and Technical Information of China (English)

    李元春; 丁贵彬; 赵博

    2016-01-01

    A decentralized adaptive neural network sliding mode position/force control scheme is proposed for constrained reconfigurable manipulators. Different from the decentralized control strategy in multi-manipulator cooperation, the proposed decentralized position/force control scheme can be applied to series constrained reconfigurable manipulators. By multiplying each row of Jacobian matrix in the dynamics by contact force vector, the converted joint torque is obtained. Furthermore, using desired information of other joints instead of their actual values, the dynamics can be represented as a set of interconnected subsystems by model decomposition technique. An adaptive neural network controller is introduced to approximate the unknown dynamics of subsystem. The interconnection and the whole error term are removed by employing an adaptive sliding mode term. And then, the Lyapunov stability theory guarantees the stability of the closed-loop system. Finally, two reconfigurable manipulators with different configurations are employed to show the effectiveness of the proposed decentralized position/force control scheme.

  16. Decentralized direct adaptive neural network control for a class of interconnected systems

    Institute of Scientific and Technical Information of China (English)

    Zhang Tianping; Mei Jiandong

    2006-01-01

    The problem of direct adaptive neural network control for a class of large-scale systems with unknown function control gains and the high-order interconnections is studied in this paper. Based on the principle of sliding mode control and the approximation capability of multilayer neural networks, a design scheme of decentralized direct adaptive sliding mode controller is proposed. The plant dynamic uncertainty and modeling errors are adaptively compensated by adjusted the weights and sliding mode gains on-line for each subsystem using only local information. According to the Lyapunov method, the closed-loop adaptive control system is proven to be globally stable, with tracking errors converging to a neighborhood of zero. Simulation results demonstrate the effectiveness of the proposed approach.

  17. Synchronization for an array of neural networks with hybrid coupling by a novel pinning control strategy.

    Science.gov (United States)

    Gong, Dawei; Lewis, Frank L; Wang, Liping; Xu, Ke

    2016-05-01

    In this paper, a novel pinning synchronization (synchronization with pinning control) scheme for an array of neural networks with hybrid coupling is investigated. The main contributions are as follows: (1) A novel pinning control strategy is proposed for the first time. Pinning control schemes are introduced as an array of column vector. The controllers are designed as simple linear systems, which are easy to be analyzed or tested. (2) Augmented Lyapunov-Krasovskii functional (LKF) is applied to introduce more relax variables, which can alleviate the requirements of the positive definiteness of the matrix. (3) Based on the appropriate LKF, by introducing some free weighting matrices, some novel synchronization criteria are derived. Furthermore, the proposed pinning control scheme described by column vector can also be expanded to almost all the other array of neural networks. Finally, numerical examples are provided to show the effectiveness of the proposed results.

  18. Neural compensation, muscle load distribution and muscle function in control of biped models

    Science.gov (United States)

    Bavarian, B.

    Three aspects of the neuromuscular control of muscle actuators in biped movements were studied: neural compensation, muscle load distribution, and muscle function. A block diagram of a neural control circuit model of the control nervous system is presented. Based on this block diagram a circuit comprised of a dynamic compensator, an inverse plant, and pre-programmed reference trajectory generators is proposed for control of a general n-link biped model. This circuit is used to study the postural stability and point-to-point voluntary movement of a two-link planar biped with two pairs of muscle models. The muscle load distribution, relevant to functional electrical stimulation of paraplegic patients for restoration of limited motor function, is considered. A quantitative analysis of the local controllability of a two-link planar biped model incorporating six major muscles of the lower extremities is presented. A model of the muscle for the lower extremities is presented.

  19. 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.

  20. Active vibration control of flexible cantilever plates using piezoelectric materials and artificial neural networks

    Science.gov (United States)

    Abdeljaber, Osama; Avci, Onur; Inman, Daniel J.

    2016-02-01

    The study presented in this paper introduces a new intelligent methodology to mitigate the vibration response of flexible cantilever plates. The use of the piezoelectric sensor/actuator pairs for active control of plates is discussed. An intelligent neural network based controller is designed to control the optimal voltage applied on the piezoelectric patches. The control technique utilizes a neurocontroller along with a Kalman Filter to compute the appropriate actuator command. The neurocontroller is trained based on an algorithm that incorporates a set of emulator neural networks which are also trained to predict the future response of the cantilever plate. Then, the neurocontroller is evaluated by comparing the uncontrolled and controlled responses under several types of dynamic excitations. It is observed that the neurocontroller reduced the vibration response of the flexible cantilever plate significantly; the results demonstrated the success and robustness of the neurocontroller independent of the type and distribution of the excitation force.