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....

Cardiac outflow tract malformations in chick embryos exposed to homocysteine

NARCIS (Netherlands)

M.J. Boot (Marit); R.P.M. Steegers-Theunissen (Régine); R.E. Poelmann (Robert); L. van Iperen (Liesbeth); A.C. Gittenberger-De Groot (Adriana)

2004-01-01

textabstractIncreased homocysteine concentrations have been associated with cardiac outflow tract defects. It has been hypothesized that cardiac neural crest cells were the target cells in these malformations. Cardiac neural crest cells migrate from the neural tube and contribute to the condensed

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...

Neural control of magnetic suspension systems

Science.gov (United States)

Gray, W. Steven

1993-01-01

The purpose of this research program is to design, build and test (in cooperation with NASA personnel from the NASA Langley Research Center) neural controllers for two different small air-gap magnetic suspension systems. The general objective of the program is to study neural network architectures for the purpose of control in an experimental setting and to demonstrate the feasibility of the concept. The specific objectives of the research program are: (1) to demonstrate through simulation and experimentation the feasibility of using neural controllers to stabilize a nonlinear magnetic suspension system; (2) to investigate through simulation and experimentation the performance of neural controllers designs under various types of parametric and nonparametric uncertainty; (3) to investigate through simulation and experimentation various types of neural architectures for real-time control with respect to performance and complexity; and (4) to benchmark in an experimental setting the performance of neural controllers against other types of existing linear and nonlinear compensator designs. To date, the first one-dimensional, small air-gap magnetic suspension system has been built, tested and delivered to the NASA Langley Research Center. The device is currently being stabilized with a digital linear phase-lead controller. The neural controller hardware is under construction. Two different neural network paradigms are under consideration, one based on hidden layer feedforward networks trained via back propagation and one based on using Gaussian radial basis functions trained by analytical methods related to stability conditions. Some advanced nonlinear control algorithms using feedback linearization and sliding mode control are in simulation studies.

Computer control versus manual control of systemic hypertension during cardiac surgery

NARCIS (Netherlands)

Hoeksel, S.A.A.P.; Blom, J.A.; Jansen, J.R.C.; Maessen, J.G.; Schreuder, J.J.

2001-01-01

Keywords:Cardiac surgery;hypertension;closed-loop controlBackground: We recently demonstrated the feasibility of computer controlled infusion of vasoactive drugs for the control of systemic hypertension during cardiac surgery. The objective of the current study was to investigate the effects of

Learning from neural control.

Science.gov (United States)

Wang, Cong; Hill, David J

2006-01-01

One of the amazing successes of biological systems is their ability to "learn by doing" and so adapt to their environment. In this paper, first, a deterministic learning mechanism is presented, by which an appropriately designed adaptive neural controller is capable of learning closed-loop system dynamics during tracking control to a periodic reference orbit. Among various neural network (NN) architectures, the localized radial basis function (RBF) network is employed. A property of persistence of excitation (PE) for RBF networks is established, and a partial PE condition of closed-loop signals, i.e., the PE condition of a regression subvector constructed out of the RBFs along a periodic state trajectory, is proven to be satisfied. Accurate NN approximation for closed-loop system dynamics is achieved in a local region along the periodic state trajectory, and a learning ability is implemented during a closed-loop feedback control process. Second, based on the deterministic learning mechanism, a neural learning control scheme is proposed which can effectively recall and reuse the learned knowledge to achieve closed-loop stability and improved control performance. The significance of this paper is that the presented deterministic learning mechanism and the neural learning control scheme provide elementary components toward the development of a biologically-plausible learning and control methodology. Simulation studies are included to demonstrate the effectiveness of the approach.

A cardiac electrical activity model based on a cellular automata system in comparison with neural network model.

Science.gov (United States)

Khan, Muhammad Sadiq Ali; Yousuf, Sidrah

2016-03-01

Cardiac Electrical Activity is commonly distributed into three dimensions of Cardiac Tissue (Myocardium) and evolves with duration of time. The indicator of heart diseases can occur randomly at any time of a day. Heart rate, conduction and each electrical activity during cardiac cycle should be monitor non-invasively for the assessment of "Action Potential" (regular) and "Arrhythmia" (irregular) rhythms. Many heart diseases can easily be examined through Automata model like Cellular Automata concepts. This paper deals with the different states of cardiac rhythms using cellular automata with the comparison of neural network also provides fast and highly effective stimulation for the contraction of cardiac muscles on the Atria in the result of genesis of electrical spark or wave. The specific formulated model named as "States of automaton Proposed Model for CEA (Cardiac Electrical Activity)" by using Cellular Automata Methodology is commonly shows the three states of cardiac tissues conduction phenomena (i) Resting (Relax and Excitable state), (ii) ARP (Excited but Absolutely refractory Phase i.e. Excited but not able to excite neighboring cells) (iii) RRP (Excited but Relatively Refractory Phase i.e. Excited and able to excite neighboring cells). The result indicates most efficient modeling with few burden of computation and it is Action Potential during the pumping of blood in cardiac cycle.

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.

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...

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.

Mechanical perturbation control of cardiac alternans

Science.gov (United States)

Hazim, Azzam; Belhamadia, Youssef; Dubljevic, Stevan

2018-05-01

Cardiac alternans is a disturbance in heart rhythm that is linked to the onset of lethal cardiac arrhythmias. Mechanical perturbation control has been recently used to suppress alternans in cardiac tissue of relevant size. In this control strategy, cardiac tissue mechanics are perturbed via active tension generated by the heart's electrical activity, which alters the tissue's electric wave profile through mechanoelectric coupling. We analyze the effects of mechanical perturbation on the dynamics of a map model that couples the membrane voltage and active tension systems at the cellular level. Therefore, a two-dimensional iterative map of the heart beat-to-beat dynamics is introduced, and a stability analysis of the system of coupled maps is performed in the presence of a mechanical perturbation algorithm. To this end, a bidirectional coupling between the membrane voltage and active tension systems in a single cardiac cell is provided, and a discrete form of the proposed control algorithm, that can be incorporated in the coupled maps, is derived. In addition, a realistic electromechanical model of cardiac tissue is employed to explore the feasibility of suppressing alternans at cellular and tissue levels. Electrical activity is represented in two detailed ionic models, the Luo-Rudy 1 and the Fox models, while two active contractile tension models, namely a smooth variant of the Nash-Panfilov model and the Niederer-Hunter-Smith model, are used to represent mechanical activity in the heart. The Mooney-Rivlin passive elasticity model is employed to describe passive mechanical behavior of the myocardium.

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.

Direct adaptive control using feedforward neural networks

OpenAIRE

Cajueiro, Daniel Oliveira; Hemerly, Elder Moreira

2003-01-01

ABSTRACT: This paper proposes a new scheme for direct neural adaptive control that works efficiently employing only one neural network, used for simultaneously identifying and controlling the plant. The idea behind this structure of adaptive control is to compensate the control input obtained by a conventional feedback controller. The neural network training process is carried out by using two different techniques: backpropagation and extended Kalman filter algorithm. Additionally, the conver...

Nonlinear control strategy based on using a shape-tunable neural controller

Energy Technology Data Exchange (ETDEWEB)

Chen, C.; Peng, S. [Feng Chia Univ, Taichung (Taiwan, Province of China). Department of chemical Engineering; Chang, W. [Feng Chia Univ, Taichung (Taiwan, Province of China). Department of Automatic Control

1997-08-01

In this paper, a nonlinear control strategy based on using a shape-tunable neural network is developed for adaptive control of nonlinear processes. Based on the steepest descent method, a learning algorithm that enables the neural controller to possess the ability of automatic controller output range adjustment is derived. The novel feature of automatic output range adjustment provides the neural controller more flexibility and capability, and therefore the scaling procedure, which is usually unavoidable for the conventional fixed-shape neural controllers, becomes unnecessary. The advantages and effectiveness of the proposed nonlinear control strategy are demonstrated through the challenge problem of controlling an open-loop unstable nonlinear continuous stirred tank reactor (CSTR). 14 refs., 11 figs.

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...

Neural Network Based Load Frequency Control for Restructuring ...

African Journals Online (AJOL)

Neural Network Based Load Frequency Control for Restructuring Power Industry. ... an artificial neural network (ANN) application of load frequency control (LFC) of a Multi-Area power system by using a neural network controller is presented.

Stability of a neural predictive controller scheme on a neural model

DEFF Research Database (Denmark)

Luther, Jim Benjamin; Sørensen, Paul Haase

2009-01-01

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

The neural optimal control hierarchy for motor control

Science.gov (United States)

DeWolf, T.; Eliasmith, C.

2011-10-01

Our empirical, neuroscientific understanding of biological motor systems has been rapidly growing in recent years. However, this understanding has not been systematically mapped to a quantitative characterization of motor control based in control theory. Here, we attempt to bridge this gap by describing the neural optimal control hierarchy (NOCH), which can serve as a foundation for biologically plausible models of neural motor control. The NOCH has been constructed by taking recent control theoretic models of motor control, analyzing the required processes, generating neurally plausible equivalent calculations and mapping them on to the neural structures that have been empirically identified to form the anatomical basis of motor control. We demonstrate the utility of the NOCH by constructing a simple model based on the identified principles and testing it in two ways. First, we perturb specific anatomical elements of the model and compare the resulting motor behavior with clinical data in which the corresponding area of the brain has been damaged. We show that damaging the assigned functions of the basal ganglia and cerebellum can cause the movement deficiencies seen in patients with Huntington's disease and cerebellar lesions. Second, we demonstrate that single spiking neuron data from our model's motor cortical areas explain major features of single-cell responses recorded from the same primate areas. We suggest that together these results show how NOCH-based models can be used to unify a broad range of data relevant to biological motor control in a quantitative, control theoretic framework.

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.

Neural network-based model reference adaptive control system.

Science.gov (United States)

Patino, H D; Liu, D

2000-01-01

In this paper, an approach to model reference adaptive control based on neural networks is proposed and analyzed for a class of first-order continuous-time nonlinear dynamical systems. The controller structure can employ either a radial basis function network or a feedforward neural network to compensate adaptively the nonlinearities in the plant. A stable controller-parameter adjustment mechanism, which is determined using the Lyapunov theory, is constructed using a sigma-modification-type updating law. The evaluation of control error in terms of the neural network learning error is performed. That is, the control error converges asymptotically to a neighborhood of zero, whose size is evaluated and depends on the approximation error of the neural network. In the design and analysis of neural network-based control systems, it is important to take into account the neural network learning error and its influence on the control error of the plant. Simulation results showing the feasibility and performance of the proposed approach are given.

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...

Krox20 defines a subpopulation of cardiac neural crest cells contributing to arterial valves and bicuspid aortic valve.

Science.gov (United States)

Odelin, Gaëlle; Faure, Emilie; Coulpier, Fanny; Di Bonito, Maria; Bajolle, Fanny; Studer, Michèle; Avierinos, Jean-François; Charnay, Patrick; Topilko, Piotr; Zaffran, Stéphane

2018-01-03

Although cardiac neural crest cells are required at early stages of arterial valve development, their contribution during valvular leaflet maturation remains poorly understood. Here, we show in mouse that neural crest cells from pre-otic and post-otic regions make distinct contributions to the arterial valve leaflets. Genetic fate-mapping analysis of Krox20-expressing neural crest cells shows a large contribution to the borders and the interleaflet triangles of the arterial valves. Loss of Krox20 function results in hyperplastic aortic valve and partially penetrant bicuspid aortic valve formation. Similar defects are observed in neural crest Krox20 -deficient embryos. Genetic lineage tracing in Krox20 -/- mutant mice shows that endothelial-derived cells are normal, whereas neural crest-derived cells are abnormally increased in number and misplaced in the valve leaflets. In contrast, genetic ablation of Krox20 -expressing cells is not sufficient to cause an aortic valve defect, suggesting that adjacent cells can compensate this depletion. Our findings demonstrate a crucial role for Krox20 in arterial valve development and reveal that an excess of neural crest cells may be associated with bicuspid aortic valve. © 2018. Published by The Company of Biologists Ltd.

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.

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.

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....

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.

Generalized Predictive Control and Neural Generalized Predictive Control

Directory of Open Access Journals (Sweden)

Sadhana CHIDRAWAR

2008-12-01

Full Text Available As Model Predictive Control (MPC relies on the predictive Control using a multilayer feed forward network as the plants linear model is presented. In using Newton-Raphson as the optimization algorithm, the number of iterations needed for convergence is significantly reduced from other techniques. This paper presents a detailed derivation of the Generalized Predictive Control and Neural Generalized Predictive Control with Newton-Raphson as minimization algorithm. Taking three separate systems, performances of the system has been tested. Simulation results show the effect of neural network on Generalized Predictive Control. The performance comparison of this three system configurations has been given in terms of ISE and IAE.

Glutaredoxin-2 controls cardiac mitochondrial dynamics and energetics in mice, and protects against human cardiac pathologies

Directory of Open Access Journals (Sweden)

Georges N. Kanaan

2018-04-01

Full Text Available Glutaredoxin 2 (GRX2, a mitochondrial glutathione-dependent oxidoreductase, is central to glutathione homeostasis and mitochondrial redox, which is crucial in highly metabolic tissues like the heart. Previous research showed that absence of Grx2, leads to impaired mitochondrial complex I function, hypertension and cardiac hypertrophy in mice but the impact on mitochondrial structure and function in intact cardiomyocytes and in humans has not been explored. We hypothesized that Grx2 controls cardiac mitochondrial dynamics and function in cellular and mouse models, and that low expression is associated with human cardiac dysfunction. Here we show that Grx2 absence impairs mitochondrial fusion, ultrastructure and energetics in primary cardiomyocytes and cardiac tissue. Moreover, provision of the glutathione precursor, N-acetylcysteine (NAC to Grx2-/- mice did not restore glutathione redox or prevent impairments. Using genetic and histopathological data from the human Genotype-Tissue Expression consortium we demonstrate that low GRX2 is associated with fibrosis, hypertrophy, and infarct in the left ventricle. Altogether, GRX2 is important in the control of cardiac mitochondrial structure and function, and protects against human cardiac pathologies. Keywords: Human heart, Mitochondria, Oxidative stress, Redox, Cardiac metabolism, Cardiac hypertrophy

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

Directory of Open Access Journals (Sweden)

MARABA, V. A.

2011-11-01

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

Alterations in cardiac autonomic control in spinal cord injury.

Science.gov (United States)

Biering-Sørensen, Fin; Biering-Sørensen, Tor; Liu, Nan; Malmqvist, Lasse; Wecht, Jill Maria; Krassioukov, Andrei

2018-01-01

A spinal cord injury (SCI) interferes with the autonomic nervous system (ANS). The effect on the cardiovascular system will depend on the extent of damage to the spinal/central component of ANS. The cardiac changes are caused by loss of supraspinal sympathetic control and relatively increased parasympathetic cardiac control. Decreases in sympathetic activity result in heart rate and the arterial blood pressure changes, and may cause arrhythmias, in particular bradycardia, with the risk of cardiac arrest in those with cervical or high thoracic injuries. The objective of this review is to give an update of the current knowledge related to the alterations in cardiac autonomic control following SCI. With this purpose the review includes the following subheadings: 2. Neuro-anatomical plasticity and cardiac control 2.1 Autonomic nervous system and the heart 2.2 Alteration in autonomic control of the heart following spinal cord injury 3. Spinal shock and neurogenic shock 3.1 Pathophysiology of spinal shock 3.2 Pathophysiology of neurogenic shock 4. Autonomic dysreflexia 4.1 Pathophysiology of autonomic dysreflexia 4.2 Diagnosis of autonomic dysreflexia 5. Heart rate/electrocardiography following spinal cord injury 5.1 Acute phase 5.2 Chronic phase 6. Heart rate variability 6.1 Time domain analysis 6.2 Frequency domain analysis 6.3 QT-variability index 6.4 Nonlinear (fractal) indexes 7. Echocardiography 7.1 Changes in cardiac structure following spinal cord injury 7.2 Changes in cardiac function following spinal cord injury 8. International spinal cord injury cardiovascular basic data set and international standards to document the remaining autonomic function in spinal cord injury. Copyright © 2017 Elsevier B.V. All rights reserved.

Feedforward Nonlinear Control Using Neural Gas Network

Directory of Open Access Journals (Sweden)

Iván Machón-González

2017-01-01

Full Text Available Nonlinear systems control is a main issue in control theory. Many developed applications suffer from a mathematical foundation not as general as the theory of linear systems. This paper proposes a control strategy of nonlinear systems with unknown dynamics by means of a set of local linear models obtained by a supervised neural gas network. The proposed approach takes advantage of the neural gas feature by which the algorithm yields a very robust clustering procedure. The direct model of the plant constitutes a piece-wise linear approximation of the nonlinear system and each neuron represents a local linear model for which a linear controller is designed. The neural gas model works as an observer and a controller at the same time. A state feedback control is implemented by estimation of the state variables based on the local transfer function that was provided by the local linear model. The gradient vectors obtained by the supervised neural gas algorithm provide a robust procedure for feedforward nonlinear control, that is, supposing the inexistence of disturbances.

Adaptive nonlinear control using input normalized neural networks

International Nuclear Information System (INIS)

Leeghim, Henzeh; Seo, In Ho; Bang, Hyo Choong

2008-01-01

An adaptive feedback linearization technique combined with the neural network is addressed to control uncertain nonlinear systems. The neural network-based adaptive control theory has been widely studied. However, the stability analysis of the closed-loop system with the neural network is rather complicated and difficult to understand, and sometimes unnecessary assumptions are involved. As a result, unnecessary assumptions for stability analysis are avoided by using the neural network with input normalization technique. The ultimate boundedness of the tracking error is simply proved by the Lyapunov stability theory. A new simple update law as an adaptive nonlinear control is derived by the simplification of the input normalized neural network assuming the variation of the uncertain term is sufficiently small

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.

Neural and Fuzzy Adaptive Control of Induction Motor Drives

International Nuclear Information System (INIS)

Bensalem, Y.; Sbita, L.; Abdelkrim, M. N.

2008-01-01

This paper proposes an adaptive neural network speed control scheme for an induction motor (IM) drive. The proposed scheme consists of an adaptive neural network identifier (ANNI) and an adaptive neural network controller (ANNC). For learning the quoted neural networks, a back propagation algorithm was used to automatically adjust the weights of the ANNI and ANNC in order to minimize the performance functions. Here, the ANNI can quickly estimate the plant parameters and the ANNC is used to provide on-line identification of the command and to produce a control force, such that the motor speed can accurately track the reference command. By combining artificial neural network techniques with fuzzy logic concept, a neural and fuzzy adaptive control scheme is developed. Fuzzy logic was used for the adaptation of the neural controller to improve the robustness of the generated command. The developed method is robust to load torque disturbance and the speed target variations when it ensures precise trajectory tracking with the prescribed dynamics. The algorithm was verified by simulation and the results obtained demonstrate the effectiveness of the IM designed controller

Myocardial ischaemia and the cardiac nervous system.

Science.gov (United States)

Armour, J A

1999-01-01

tentative ideas concerning the importance of this nervous system in cardiac disease states with a view to stimulating further interest in neural control of the heart so that appropriate neurocardiological strategies can be devised for the management of heart disease.

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.

Bio-inspired spiking neural network for nonlinear systems control.

Science.gov (United States)

Pérez, Javier; Cabrera, Juan A; Castillo, Juan J; Velasco, Juan M

2018-08-01

Spiking neural networks (SNN) are the third generation of artificial neural networks. SNN are the closest approximation to biological neural networks. SNNs make use of temporal spike trains to command inputs and outputs, allowing a faster and more complex computation. As demonstrated by biological organisms, they are a potentially good approach to designing controllers for highly nonlinear dynamic systems in which the performance of controllers developed by conventional techniques is not satisfactory or difficult to implement. SNN-based controllers exploit their ability for online learning and self-adaptation to evolve when transferred from simulations to the real world. SNN's inherent binary and temporary way of information codification facilitates their hardware implementation compared to analog neurons. Biological neural networks often require a lower number of neurons compared to other controllers based on artificial neural networks. In this work, these neuronal systems are imitated to perform the control of non-linear dynamic systems. For this purpose, a control structure based on spiking neural networks has been designed. Particular attention has been paid to optimizing the structure and size of the neural network. The proposed structure is able to control dynamic systems with a reduced number of neurons and connections. A supervised learning process using evolutionary algorithms has been carried out to perform controller training. The efficiency of the proposed network has been verified in two examples of dynamic systems control. Simulations show that the proposed control based on SNN exhibits superior performance compared to other approaches based on Neural Networks and SNNs. Copyright © 2018 Elsevier Ltd. All rights reserved.

Exercise training restores cardiac protein quality control in heart failure.

Directory of Open Access Journals (Sweden)

Juliane C Campos

Full Text Available Exercise training is a well-known coadjuvant in heart failure treatment; however, the molecular mechanisms underlying its beneficial effects remain elusive. Despite the primary cause, heart failure is often preceded by two distinct phenomena: mitochondria dysfunction and cytosolic protein quality control disruption. The objective of the study was to determine the contribution of exercise training in regulating cardiac mitochondria metabolism and cytosolic protein quality control in a post-myocardial infarction-induced heart failure (MI-HF animal model. Our data demonstrated that isolated cardiac mitochondria from MI-HF rats displayed decreased oxygen consumption, reduced maximum calcium uptake and elevated H₂O₂ release. These changes were accompanied by exacerbated cardiac oxidative stress and proteasomal insufficiency. Declined proteasomal activity contributes to cardiac protein quality control disruption in our MI-HF model. Using cultured neonatal cardiomyocytes, we showed that either antimycin A or H₂O₂ resulted in inactivation of proteasomal peptidase activity, accumulation of oxidized proteins and cell death, recapitulating our in vivo model. Of interest, eight weeks of exercise training improved cardiac function, peak oxygen uptake and exercise tolerance in MI-HF rats. Moreover, exercise training restored mitochondrial oxygen consumption, increased Ca²⁺-induced permeability transition and reduced H₂O₂ release in MI-HF rats. These changes were followed by reduced oxidative stress and better cardiac protein quality control. Taken together, our findings uncover the potential contribution of mitochondrial dysfunction and cytosolic protein quality control disruption to heart failure and highlight the positive effects of exercise training in re-establishing cardiac mitochondrial physiology and protein quality control, reinforcing the importance of this intervention as a non-pharmacological tool for heart failure therapy.

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....

Neural Generalized Predictive Control of a non-linear Process

DEFF Research Database (Denmark)

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

1998-01-01

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

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.

Neural-fuzzy control of adept one SCARA

International Nuclear Information System (INIS)

Er, M.J.; Toh, B.H.; Toh, B.Y.

1998-01-01

This paper presents an Intelligent Control Strategy for the Adept One SCARA (Selective Compliance Assembly Robot Arm). It covers the design and simulation study of a Neural-Fuzzy Controller (NFC) for the SCARA with a view of tracking a predetermined trajectory of motion in the joint space. The SCARA was simulated as a three-axis manipulator with the dynamics of the tool (fourth link) neglected and the mass of the load incorporated into the mass of the third link. The overall performance of the control system under different conditions, namely variation in playload, variations in coefficients of static, dynamic and viscous friction and different trajectories were studied and comparison made with an existing Neural Network Controller and two Computed Torque Controllers. The NFC was shown to be robust and is able to overcome the drawback of the existing Neural Network Controller

Neural Networks for Modeling and Control of Particle Accelerators

Science.gov (United States)

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

2016-04-01

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

Distributed predictive control of spiral wave in cardiac excitable media

International Nuclear Information System (INIS)

Zheng-Ning, Gan; Xin-Ming, Cheng

2010-01-01

In this paper, we propose the distributed predictive control strategies of spiral wave in cardiac excitable media. The modified FitzHugh–Nagumo model was used to express the cardiac excitable media approximately. Based on the control-Lyapunov theory, we obtained the distributed control equation, which consists of a positive control-Lyapunov function and a positive cost function. Using the equation, we investigate two kinds of robust control strategies: the time-dependent distributed control strategy and the space-time dependent distributed control strategy. The feasibility of the strategies was demonstrated via an illustrative example, in which the spiral wave was prevented to occur, and the possibility for inducing ventricular fibrillation was eliminated. The strategies are helpful in designing various cardiac devices. Since the second strategy is more efficient and robust than the first one, and the response time in the second strategy is far less than that in the first one, the former is suitable for the quick-response control systems. In addition, our spatiotemporal control strategies, especially the second strategy, can be applied to other cardiac models, even to other reaction-diffusion systems. (general)

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.

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

Indian Academy of Sciences (India)

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

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.

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.

Neural controller for adaptive movements with unforeseen payloads.

Science.gov (United States)

Kuperstein, M; Wang, J

1990-01-01

A theory and computer simulation of a neural controller that learns to move and position a link carrying an unforeseen payload accurately are presented. The neural controller learns adaptive dynamic control from its own experience. It does not use information about link mass, link length, or direction of gravity, and it uses only indirect uncalibrated information about payload and actuator limits. Its average positioning accuracy across a large range of payloads after learning is 3% of the positioning range. This neural controller can be used as a basis for coordinating any number of sensory inputs with limbs of any number of joints. The feedforward nature of control allows parallel implementation in real time across multiple joints.

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.

Wavelet neural network load frequency controller

International Nuclear Information System (INIS)

Hemeida, Ashraf Mohamed

2005-01-01

This paper presents the feasibility of applying a wavelet neural network (WNN) approach for the load frequency controller (LFC) to damp the frequency oscillations of two area power systems due to load disturbances. The present intelligent control system trained the wavelet neural network (WNN) controller on line with adaptive learning rates, which are derived in the sense of a discrete type Lyapunov stability theorem. The present WNN controller is designed individually for each area. The proposed technique is applied successfully for a wide range of operating conditions. The time simulation results indicate its superiority and effectiveness over the conventional approach. The effects of consideration of the governor dead zone on the system performance are studied using the proposed controller and the conventional one

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

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....

Modeling and Speed Control of Induction Motor Drives Using Neural Networks

Directory of Open Access Journals (Sweden)

V. Jamuna

2010-08-01

Full Text Available Speed control of induction motor drives using neural networks is presented. The mathematical model of single phase induction motor is developed. A new simulink model for a neural network-controlled bidirectional chopper fed single phase induction motor is proposed. Under normal operation, the true drive parameters are real-time identified and they are converted into the controller parameters through multilayer forward computation by neural networks. Comparative study has been made between the conventional and neural network controllers. It is observed that the neural network controlled drive system has better dynamic performance, reduced overshoot and faster transient response than the conventional controlled system.

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

DEFF Research Database (Denmark)

Manoonpong, Poramate; Wörgötter, Florentin; Laksanacharoen, Pudit

2014-01-01

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

Feedforward Nonlinear Control Using Neural Gas Network

OpenAIRE

Machón-González, Iván; López-García, Hilario

2017-01-01

Nonlinear systems control is a main issue in control theory. Many developed applications suffer from a mathematical foundation not as general as the theory of linear systems. This paper proposes a control strategy of nonlinear systems with unknown dynamics by means of a set of local linear models obtained by a supervised neural gas network. The proposed approach takes advantage of the neural gas feature by which the algorithm yields a very robust clustering procedure. The direct model of the ...

Cardiac-vessels system violations of children moved from radiation polluted zones

International Nuclear Information System (INIS)

Bobovich, A.A.; Kokorina, N.V.

2008-01-01

In the work are presented results of circulation system status and heard pre-pathology processes of 60 children moved from radiation polluted zones. Some connections between cardiac-vessels system violation and neural system violations are ascertained. Role of neural system functions fallen down and non-adaptive cardiac-vessels system changes in pathogenesis are discussed. (authors)

Application of neural networks to seismic active control

International Nuclear Information System (INIS)

Tang, Yu.

1995-01-01

An exploratory study on seismic active control using an artificial neural network (ANN) is presented in which a singledegree-of-freedom (SDF) structural system is controlled by a trained neural network. A feed-forward neural network and the backpropagation training method are used in the study. In backpropagation training, the learning rate is determined by ensuring the decrease of the error function at each training cycle. The training patterns for the neural net are generated randomly. Then, the trained ANN is used to compute the control force according to the control algorithm. The control strategy proposed herein is to apply the control force at every time step to destroy the build-up of the system response. The ground motions considered in the simulations are the N21E and N69W components of the Lake Hughes No. 12 record that occurred in the San Fernando Valley in California on February 9, 1971. Significant reduction of the structural response by one order of magnitude is observed. Also, it is shown that the proposed control strategy has the ability to reduce the peak that occurs during the first few cycles of the time history. These promising results assert the potential of applying ANNs to active structural control under seismic loads

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

Science.gov (United States)

Wan, Ying; Cao, Jinde; Wen, Guanghui

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

Alterations in cardiac autonomic control in spinal cord injury

DEFF Research Database (Denmark)

Biering-Sørensen, Fin; Biering-Sørensen, Tor; Liu, Nan

2018-01-01

parasympathetic cardiac control. Decreases in sympathetic activity result in heart rate and the arterial blood pressure changes, and may cause arrhythmias, in particular bradycardia, with the risk of cardiac arrest in those with cervical or high thoracic injuries. The objective of this review is to give an update...

Prediction based chaos control via a new neural network

International Nuclear Information System (INIS)

Shen Liqun; Wang Mao; Liu Wanyu; Sun Guanghui

2008-01-01

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

Control of beam halo-chaos using neural network self-adaptation method

International Nuclear Information System (INIS)

Fang Jinqing; Huang Guoxian; Luo Xiaoshu

2004-11-01

Taking the advantages of neural network control method for nonlinear complex systems, control of beam halo-chaos in the periodic focusing channels (network) of high intensity accelerators is studied by feed-forward back-propagating neural network self-adaptation method. The envelope radius of high-intensity proton beam is reached to the matching beam radius by suitably selecting the control structure of neural network and the linear feedback coefficient, adjusted the right-coefficient of neural network. The beam halo-chaos is obviously suppressed and shaking size is much largely reduced after the neural network self-adaptation control is applied. (authors)

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.

Neurally Adjusted Ventilatory Assist After Pediatric Cardiac Surgery: Clinical Experience and Impact on Ventilation Pressures.

Science.gov (United States)

Crulli, Benjamin; Khebir, Mariam; Toledano, Baruch; Vobecky, Suzanne; Poirier, Nancy; Emeriaud, Guillaume

2018-02-01

After pediatric cardiac surgery, ventilation with high airway pressures can be detrimental to right ventricular function and pulmonary blood flow. Neurally adjusted ventilatory assist (NAVA) improves patient-ventilator interactions, helping maintain spontaneous ventilation. This study reports our experience with the use of NAVA in children after a cardiac surgery. We hypothesize that using NAVA in this population is feasible and allows for lower ventilation pressures. We retrospectively studied all children ventilated with NAVA (invasively or noninvasively) after undergoing cardiac surgery between January 2013 and May 2015 in our pediatric intensive care unit. The number and duration of NAVA episodes were described. For the first period of invasive NAVA in each subject, detailed clinical and ventilator data in the 4 h before and after the start of NAVA were extracted. 33 postoperative courses were included in 28 subjects with a median age of 3 [interquartile range (IQR) 1-12] months. NAVA was used invasively in 27 courses for a total duration of 87 (IQR 15-334) h per course. Peak inspiratory pressures and mean airway pressures decreased significantly after the start of NAVA (mean differences of 5.8 cm H 2 O (95% CI 4.1-7.5) and 2.0 cm H 2 O (95% CI 1.2-2.8), respectively, P < .001 for both). There was no significant difference in vital signs or blood gas values. NAVA was used noninvasively in 14 subjects, over 79 (IQR 25-137) h. NAVA could be used in pediatric subjects after cardiac surgery. The significant decrease in airway pressures observed after transition to NAVA could have a beneficial impact in this specific population, which should be investigated in future interventional studies. Copyright © 2018 by Daedalus Enterprises.

Automatic segmentation and disease classification using cardiac cine MR images

NARCIS (Netherlands)

Wolterink, Jelmer M.; Leiner, Tim; Viergever, Max A.; Išgum, Ivana

2018-01-01

Segmentation of the heart in cardiac cine MR is clinically used to quantify cardiac function. We propose a fully automatic method for segmentation and disease classification using cardiac cine MR images. A convolutional neural network (CNN) was designed to simultaneously segment the left ventricle

Controlling the dynamics of multi-state neural networks

International Nuclear Information System (INIS)

Jin, Tao; Zhao, Hong

2008-01-01

In this paper, we first analyze the distribution of local fields (DLF) which is induced by the memory patterns in the Q-Ising model. It is found that the structure of the DLF is closely correlated with the network dynamics and the system performance. However, the design rule adopted in the Q-Ising model, like the other rules adopted for multi-state neural networks with associative memories, cannot be applied to directly control the DLF for a given set of memory patterns, and thus cannot be applied to further study the relationships between the structure of the DLF and the dynamics of the network. We then extend a design rule, which was presented recently for designing binary-state neural networks, to make it suitable for designing general multi-state neural networks. This rule is able to control the structure of the DLF as expected. We show that controlling the DLF not only can affect the dynamic behaviors of the multi-state neural networks for a given set of memory patterns, but also can improve the storage capacity. With the change of the DLF, the network shows very rich dynamic behaviors, such as the 'chaos phase', the 'memory phase', and the 'mixture phase'. These dynamic behaviors are also observed in the binary-state neural networks; therefore, our results imply that they may be the universal behaviors of feedback neural networks

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.

Neural PID Control of Robot Manipulators With Application to an Upper Limb Exoskeleton.

Science.gov (United States)

Yu, Wen; Rosen, Jacob

2013-04-01

In order to minimize steady-state error with respect to uncertainties in robot control, proportional-integral-derivative (PID) control needs a big integral gain, or a neural compensator is added to the classical proportional-derivative (PD) control with a large derivative gain. Both of them deteriorate transient performances of the robot control. In this paper, we extend the popular neural PD control into neural PID control. This novel control is a natural combination of industrial linear PID control and neural compensation. The main contributions of this paper are semiglobal asymptotic stability of the neural PID control and local asymptotic stability of the neural PID control with a velocity observer which are proved with standard weight training algorithms. These conditions give explicit selection methods for the gains of the linear PID control. An experimental study on an upper limb exoskeleton with this neural PID control is addressed.

Neck muscle biomechanics and neural control.

Science.gov (United States)

Fice, Jason Bradley; Siegmund, Gunter P; Blouin, Jean-Sebastien

2018-04-18

The mechanics, morphometry, and geometry of our joints, segments and muscles are fundamental biomechanical properties intrinsic to human neural control. The goal of our study was to investigate if the biomechanical actions of individual neck muscles predicts their neural control. Specifically, we compared the moment direction & variability produced by electrical stimulation of a neck muscle (biomechanics) to their preferred activation direction & variability (neural control). Subjects sat upright with their head fixed to a 6-axis load cell and their torso restrained. Indwelling wire electrodes were placed into the sternocleidomastoid (SCM), splenius capitis (SPL), and semispinalis capitis (SSC) muscles. The electrically stimulated direction was defined as the moment direction produced when a current (2-19mA) was passed through each muscle's electrodes. Preferred activation direction was defined as the vector sum of the spatial tuning curve built from RMS EMG when subjects produced isometric moments at 7.5% and 15% of their maximum voluntary contraction (MVC) in 26 3D directions. The spatial tuning curves at 15% MVC were well-defined (unimodal, pbiomechanics but, as activation increases, biomechanical constraints in part dictate the activation of synergistic neck muscles.

Adrenergic innervation of the developing chick heart: neural crest ablations to produce sympathetically aneural hearts

International Nuclear Information System (INIS)

Kirby, M.; Stewart, D.

1984-01-01

Ablation of various regions of premigratory trunk neural crest which gives rise to the sympathetic trunks was used to remove sympathetic cardiac innervation. Neuronal uptake of [ 3 H]-norepinephrine was used as an index of neuronal development in the chick atrium. Following ablation of neural crest over somites 10-15 or 15-20, uptake was significantly decreased in the atrium at 16 and 17 days of development. Ablation of neural crest over somites 5-10 and 20-25 caused no decrease in [ 3 H]-norepinephrine uptake. Removal of neural crest over somites 5-25 or 10-20 caused approximately equal depletions of [ 3 H]-norepinephrine uptake in the atrium. Cardiac norepinephrine concentration was significantly depressed following ablation of neural crest over somites 5-25 but not over somites 10-20. Light-microscopic and histofluorescent preparations confirmed the absence of sympathetic trunks in the region of the normal origin of the sympathetic cardiac nerves following neural crest ablation over somites 10-20. The neural tube and dorsal root ganglia were damaged in the area of the neural-crest ablation; however, all of these structures were normal cranial and caudal to the lesioned area. Development of most of the embryos as well as the morphology of all of the hearts was normal following the lesion. These results indicate that it is possible to produce sympathetically aneural hearts by neural-crest ablation; however, sympathetic cardiac nerves account for an insignificant amount of cardiac norepinephrine

Adaptive control using neural networks and approximate models.

Science.gov (United States)

Narendra, K S; Mukhopadhyay, S

1997-01-01

The NARMA model is an exact representation of the input-output behavior of finite-dimensional nonlinear discrete-time dynamical systems in a neighborhood of the equilibrium state. However, it is not convenient for purposes of adaptive control using neural networks due to its nonlinear dependence on the control input. Hence, quite often, approximate methods are used for realizing the neural controllers to overcome computational complexity. In this paper, we introduce two classes of models which are approximations to the NARMA model, and which are linear in the control input. The latter fact substantially simplifies both the theoretical analysis as well as the practical implementation of the controller. Extensive simulation studies have shown that the neural controllers designed using the proposed approximate models perform very well, and in many cases even better than an approximate controller designed using the exact NARMA model. In view of their mathematical tractability as well as their success in simulation studies, a case is made in this paper that such approximate input-output models warrant a detailed study in their own right.

Bidirectional neural interface: Closed-loop feedback control for hybrid neural systems.

Science.gov (United States)

Chou, Zane; Lim, Jeffrey; Brown, Sophie; Keller, Melissa; Bugbee, Joseph; Broccard, Frédéric D; Khraiche, Massoud L; Silva, Gabriel A; Cauwenberghs, Gert

2015-01-01

Closed-loop neural prostheses enable bidirectional communication between the biological and artificial components of a hybrid system. However, a major challenge in this field is the limited understanding of how these components, the two separate neural networks, interact with each other. In this paper, we propose an in vitro model of a closed-loop system that allows for easy experimental testing and modification of both biological and artificial network parameters. The interface closes the system loop in real time by stimulating each network based on recorded activity of the other network, within preset parameters. As a proof of concept we demonstrate that the bidirectional interface is able to establish and control network properties, such as synchrony, in a hybrid system of two neural networks more significantly more effectively than the same system without the interface or with unidirectional alternatives. This success holds promise for the application of closed-loop systems in neural prostheses, brain-machine interfaces, and drug testing.

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

Institute of Scientific and Technical Information of China (English)

无

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.

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.

System Identification, Prediction, Simulation and Control with Neural Networks

DEFF Research Database (Denmark)

Sørensen, O.

1997-01-01

a Gauss-Newton search direction is applied. 3) Amongst numerous model types, often met in control applications, only the Non-linear ARMAX (NARMAX) model, representing input/output description, is examined. A simulated example confirms that a neural network has the potential to perform excellent System......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...

Motion control of servo cylinder using neural network

International Nuclear Information System (INIS)

Hwang, Un Kyoo; Cho, Seung Ho

2004-01-01

In this paper, a neural network controller that can be implemented in parallel with a PD controller is suggested for motion control of a hydraulic servo cylinder. By applying a self-excited oscillation method, the system design parameters of open loop transfer function of servo cylinder system are identified. Based on system design parameters, the PD gains are determined for the desired closed loop characteristics. The neural network is incorporated with PD control in order to compensate the inherent nonlinearities of hydraulic servo system. As an application example, a motion control using PD-NN has been performed and proved its superior performance by comparing with that of a PD control

An Artificial Neural Network Controller for Intelligent Transportation Systems Applications

Science.gov (United States)

1996-01-01

An Autonomous Intelligent Cruise Control (AICC) has been designed using a feedforward artificial neural network, as an example for utilizing artificial neural networks for nonlinear control problems arising in intelligent transportation systems appli...

Adaptive Neural Network Sliding Mode Control for Quad Tilt Rotor Aircraft

Directory of Open Access Journals (Sweden)

Yanchao Yin

2017-01-01

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

Neural network application to aircraft control system design

Science.gov (United States)

Troudet, Terry; Garg, Sanjay; Merrill, Walter C.

1991-01-01

The feasibility of using artificial neural network as control systems for modern, complex aerospace vehicles is investigated via an example aircraft control design study. The problem considered is that of designing a controller for an integrated airframe/propulsion longitudinal dynamics model of a modern fighter aircraft to provide independent control of pitch rate and airspeed responses to pilot command inputs. An explicit model following controller using H infinity control design techniques is first designed to gain insight into the control problem as well as to provide a baseline for evaluation of the neurocontroller. Using the model of the desired dynamics as a command generator, a multilayer feedforward neural network is trained to control the vehicle model within the physical limitations of the actuator dynamics. This is achieved by minimizing an objective function which is a weighted sum of tracking errors and control input commands and rates. To gain insight in the neurocontrol, linearized representations of the nonlinear neurocontroller are analyzed along a commanded trajectory. Linear robustness analysis tools are then applied to the linearized neurocontroller models and to the baseline H infinity based controller. Future areas of research identified to enhance the practical applicability of neural networks to flight control design.

Neural network application to aircraft control system design

Science.gov (United States)

Troudet, Terry; Garg, Sanjay; Merrill, Walter C.

1991-01-01

The feasibility of using artificial neural networks as control systems for modern, complex aerospace vehicles is investigated via an example aircraft control design study. The problem considered is that of designing a controller for an integrated airframe/propulsion longitudinal dynamics model of a modern fighter aircraft to provide independent control of pitch rate and airspeed responses to pilot command inputs. An explicit model following controller using H infinity control design techniques is first designed to gain insight into the control problem as well as to provide a baseline for evaluation of the neurocontroller. Using the model of the desired dynamics as a command generator, a multilayer feedforward neural network is trained to control the vehicle model within the physical limitations of the actuator dynamics. This is achieved by minimizing an objective function which is a weighted sum of tracking errors and control input commands and rates. To gain insight in the neurocontrol, linearized representations of the nonlinear neurocontroller are analyzed along a commanded trajectory. Linear robustness analysis tools are then applied to the linearized neurocontroller models and to the baseline H infinity based controller. Future areas of research are identified to enhance the practical applicability of neural networks to flight control design.

Nonlinear adaptive inverse control via the unified model neural network

Science.gov (United States)

Jeng, Jin-Tsong; Lee, Tsu-Tian

1999-03-01

In this paper, we propose a new nonlinear adaptive inverse control via a unified model neural network. In order to overcome nonsystematic design and long training time in nonlinear adaptive inverse control, we propose the approximate transformable technique to obtain a Chebyshev Polynomials Based Unified Model (CPBUM) neural network for the feedforward/recurrent neural networks. It turns out that the proposed method can use less training time to get an inverse model. Finally, we apply this proposed method to control magnetic bearing system. The experimental results show that the proposed nonlinear adaptive inverse control architecture provides a greater flexibility and better performance in controlling magnetic bearing systems.

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...

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

DEFF Research Database (Denmark)

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

2001-01-01

a number of nonlinear model structures based on neural networks, effective training algorithms and tools for model validation and model structure selection. The NNCTRL toolkit is an add-on to NNSYSID and provides tools for design and simulation of control systems based on neural networks. The user can...... 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...

Methodology for neural networks prototyping. Application to traffic control

Energy Technology Data Exchange (ETDEWEB)

Belegan, I.C.

1998-07-01

The work described in this report was carried out in the context of the European project ASTORIA (Advanced Simulation Toolbox for Real-World Industrial Application in Passenger Management and Adaptive Control), and concerns the development of an advanced toolbox for complex transportation systems. Our work was focused on the methodology for prototyping a set of neural networks corresponding to specific strategies for traffic control and congestion management. The tool used for prototyping is SNNS (Stuggart Neural Network Simulator), developed at the University of Stuggart, Institute for Parallel and Distributed High Performance Systems, and the real data from the field were provided by ZELT. This report is structured into six parts. The introduction gives some insights about traffic control and its approaches. The second chapter discusses the various control strategies existing. The third chapter is an introduction to the field of neural networks. The data analysis and pre-processing is described in the fourth chapter. In the fifth chapter, the methodology for prototyping the neural networks is presented. Finally, conclusions and further work are presented. (author) 14 refs.

Computationally efficient model predictive control algorithms a neural network approach

CERN Document Server

Ławryńczuk, Maciej

2014-01-01

This book thoroughly discusses computationally efficient (suboptimal) Model Predictive Control (MPC) techniques based on neural models. The subjects treated include: ·         A few types of suboptimal MPC algorithms in which a linear approximation of the model or of the predicted trajectory is successively calculated on-line and used for prediction. ·         Implementation details of the MPC algorithms for feedforward perceptron neural models, neural Hammerstein models, neural Wiener models and state-space neural models. ·         The MPC algorithms based on neural multi-models (inspired by the idea of predictive control). ·         The MPC algorithms with neural approximation with no on-line linearization. ·         The MPC algorithms with guaranteed stability and robustness. ·         Cooperation between the MPC algorithms and set-point optimization. Thanks to linearization (or neural approximation), the presented suboptimal algorithms do not require d...

Implementation of a fuzzy logic/neural network multivariable controller

International Nuclear Information System (INIS)

Cordes, G.A.; Clark, D.E.; Johnson, J.A.; Smartt, H.B.; Wickham, K.L.; Larson, T.K.

1992-01-01

This paper describes a multivariable controller developed at the Idaho National Engineering Laboratory (INEL) that incorporates both fuzzy logic rules and a neural network. The controller was implemented in a laboratory demonstration and was robust, producing smooth temperature and water level response curves with short time constants. In the future, intelligent control systems will be a necessity for optimal operation of autonomous reactor systems located on earth or in space. Even today, there is a need for control systems that adapt to the changing environment and process. Hybrid intelligent control systems promise to provide this adaptive capability. Fuzzy logic implements our imprecise, qualitative human reasoning. The values of system variables (controller inputs) and control variables (controller outputs) are described in linguistic terms and subdivided into fully overlapping value ranges. The fuzzy rule base describes how combinations of input parameter ranges determine the output control values. Neural networks implement our human learning. In this controller, neural networks were embedded in the software to explore their potential for adding adaptability

Neural network based approach for tuning of SNS feedback and feedforward controllers

International Nuclear Information System (INIS)

Kwon, Sung-Il; Prokop, Mark S.; Regan, Amy H.

2002-01-01

The primary controllers in the SNS low level RF system are proportional-integral (PI) feedback controllers. To obtain the best performance of the linac control systems, approximately 91 individual PI controller gains should be optimally tuned. Tuning is time consuming and requires automation. In this paper, a neural network is used for the controller gain tuning. A neural network can approximate any continuous mapping through learning. In a sense, the cavity loop PI controller is a continuous mapping of the tracking error and its one-sample-delay inputs to the controller output. Also, monotonic cavity output with respect to its input makes knowing the detailed parameters of the cavity unnecessary. Hence the PI controller is a prime candidate for approximation through a neural network. Using mean square error minimization to train the neural network along with a continuous mapping of appropriate weights, optimally tuned PI controller gains can be determined. The same neural network approximation property is also applied to enhance the adaptive feedforward controller performance. This is done by adjusting the feedforward controller gains, forgetting factor, and learning ratio. Lastly, the automation of the tuning procedure data measurement, neural network training, tuning and loading the controller gain to the DSP is addressed.

Neural Control of the Lower Urinary Tract

Science.gov (United States)

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

2015-01-01

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

Neural systems for preparatory control of imitation.

Science.gov (United States)

Cross, Katy A; Iacoboni, Marco

2014-01-01

Humans have an automatic tendency to imitate others. Previous studies on how we control these tendencies have focused on reactive mechanisms, where inhibition of imitation is implemented after seeing an action. This work suggests that reactive control of imitation draws on at least partially specialized mechanisms. Here, we examine preparatory imitation control, where advance information allows control processes to be employed before an action is observed. Drawing on dual route models from the spatial compatibility literature, we compare control processes using biological and non-biological stimuli to determine whether preparatory imitation control recruits specialized neural systems that are similar to those observed in reactive imitation control. Results indicate that preparatory control involves anterior prefrontal, dorsolateral prefrontal, posterior parietal and early visual cortices regardless of whether automatic responses are evoked by biological (imitative) or non-biological stimuli. These results indicate both that preparatory control of imitation uses general mechanisms, and that preparatory control of imitation draws on different neural systems from reactive imitation control. Based on the regions involved, we hypothesize that preparatory control is implemented through top-down attentional biasing of visual processing.

Chaos Control and Synchronization of Cellular Neural Network with Delays Based on OPNCL Control

International Nuclear Information System (INIS)

Qian, Tang; Xing-Yuan, Wang

2010-01-01

The problem of chaos control and complete synchronization of cellular neural network with delays is studied. Based on the open plus nonlinear closed loop (OPNCL) method, the control scheme and synchronization scheme are designed. Both the schemes can achieve the chaos control and complete synchronization of chaotic neural network respectively, and their validity is further verified by numerical simulation experiments. (general)

Identification-based chaos control via backstepping design using self-organizing fuzzy neural networks

International Nuclear Information System (INIS)

Peng Yafu; Hsu, C.-F.

2009-01-01

This paper proposes an identification-based adaptive backstepping control (IABC) for the chaotic systems. The IABC system is comprised of a neural backstepping controller and a robust compensation controller. The neural backstepping controller containing a self-organizing fuzzy neural network (SOFNN) identifier is the principal controller, and the robust compensation controller is designed to dispel the effect of minimum approximation error introduced by the SOFNN identifier. The SOFNN identifier is used to online estimate the chaotic dynamic function with structure and parameter learning phases of fuzzy neural network. The structure learning phase consists of the growing and pruning of fuzzy rules; thus the SOFNN identifier can avoid the time-consuming trial-and-error tuning procedure for determining the neural structure of fuzzy neural network. The parameter learning phase adjusts the interconnection weights of neural network to achieve favorable approximation performance. Finally, simulation results verify that the proposed IABC can achieve favorable tracking performance.

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.

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.

Modeling and control of magnetorheological fluid dampers using neural networks

Science.gov (United States)

Wang, D. H.; Liao, W. H.

2005-02-01

Due to the inherent nonlinear nature of magnetorheological (MR) fluid dampers, one of the challenging aspects for utilizing these devices to achieve high system performance is the development of accurate models and control algorithms that can take advantage of their unique characteristics. In this paper, the direct identification and inverse dynamic modeling for MR fluid dampers using feedforward and recurrent neural networks are studied. The trained direct identification neural network model can be used to predict the damping force of the MR fluid damper on line, on the basis of the dynamic responses across the MR fluid damper and the command voltage, and the inverse dynamic neural network model can be used to generate the command voltage according to the desired damping force through supervised learning. The architectures and the learning methods of the dynamic neural network models and inverse neural network models for MR fluid dampers are presented, and some simulation results are discussed. Finally, the trained neural network models are applied to predict and control the damping force of the MR fluid damper. Moreover, validation methods for the neural network models developed are proposed and used to evaluate their performance. Validation results with different data sets indicate that the proposed direct identification dynamic model using the recurrent neural network can be used to predict the damping force accurately and the inverse identification dynamic model using the recurrent neural network can act as a damper controller to generate the command voltage when the MR fluid damper is used in a semi-active mode.

Neural networks for feedback feedforward nonlinear control systems.

Science.gov (United States)

Parisini, T; Zoppoli, R

1994-01-01

This paper deals with the problem of designing feedback feedforward control strategies to drive the state of a dynamic system (in general, nonlinear) so as to track any desired trajectory joining the points of given compact sets, while minimizing a certain cost function (in general, nonquadratic). Due to the generality of the problem, conventional methods are difficult to apply. Thus, an approximate solution is sought by constraining control strategies to take on the structure of multilayer feedforward neural networks. After discussing the approximation properties of neural control strategies, a particular neural architecture is presented, which is based on what has been called the "linear-structure preserving principle". The original functional problem is then reduced to a nonlinear programming one, and backpropagation is applied to derive the optimal values of the synaptic weights. Recursive equations to compute the gradient components are presented, which generalize the classical adjoint system equations of N-stage optimal control theory. Simulation results related to nonlinear nonquadratic problems show the effectiveness of the proposed method.

Integrated evolutionary computation neural network quality controller for automated systems

Energy Technology Data Exchange (ETDEWEB)

Patro, S.; Kolarik, W.J. [Texas Tech Univ., Lubbock, TX (United States). Dept. of Industrial Engineering

1999-06-01

With increasing competition in the global market, more and more stringent quality standards and specifications are being demands at lower costs. Manufacturing applications of computing power are becoming more common. The application of neural networks to identification and control of dynamic processes has been discussed. The limitations of using neural networks for control purposes has been pointed out and a different technique, evolutionary computation, has been discussed. The results of identifying and controlling an unstable, dynamic process using evolutionary computation methods has been presented. A framework for an integrated system, using both neural networks and evolutionary computation, has been proposed to identify the process and then control the product quality, in a dynamic, multivariable system, in real-time.

Implementation of neural network based non-linear predictive control

DEFF Research Database (Denmark)

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

1999-01-01

This paper describes a control method for non-linear systems based on generalized predictive control. Generalized predictive control (GPC) was developed to control linear systems, including open-loop unstable and non-minimum phase systems, but has also been proposed to be extended for the control...... 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...... on an efficient quasi-Newton algorithm. The performance is demonstrated on a pneumatic servo system....

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

Science.gov (United States)

Wilson, Edward

1995-01-01

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

A New Controller to Enhance PV System Performance Based on Neural Network

Directory of Open Access Journals (Sweden)

Roshdy A AbdelRassoul

2017-06-01

Full Text Available In recent years, a radical increase of photovoltaic (PV power generators installation took place because of increased efficiency of solar cells, as well as the growth of manufacturing technology of solar panels. This paper shows the operation and modeling of photovoltaic systems, particularly designing neural controller to control the system. Neural controller is optimized using particle swarm optimization (PSO   leads to getting the best performance of the designed PV system. Using neural network the maximum overshoot and rise time obtained become 0.00001% and 0.1798 seconds, respectively also this paper introduce a comparison between some kind of controller for PV system.In recent years, a radical increase of photovoltaic (PV power generators installation took place because of increased efficiency of solar cells, as well as the growth of manufacturing technology of solar panels. This paper shows the operation and modeling of photovoltaic systems, particularly designing neural controller to control the system. Neural controller is optimized using particle swarm optimization (PSO   leads to getting the best performance of the designed PV system. Using neural network the maximum overshoot and rise time obtained become 0.00001% and 0.1798 seconds, respectively also this paper introduce a comparison between some kind of controller for PV system.

Segmentation of left ventricle myocardium in porcine cardiac cine MR images using a hybrid of fully convolutional neural networks and convolutional LSTM

Science.gov (United States)

Zhang, Dongqing; Icke, Ilknur; Dogdas, Belma; Parimal, Sarayu; Sampath, Smita; Forbes, Joseph; Bagchi, Ansuman; Chin, Chih-Liang; Chen, Antong

2018-03-01

In the development of treatments for cardiovascular diseases, short axis cardiac cine MRI is important for the assessment of various structural and functional properties of the heart. In short axis cardiac cine MRI, Cardiac properties including the ventricle dimensions, stroke volume, and ejection fraction can be extracted based on accurate segmentation of the left ventricle (LV) myocardium. One of the most advanced segmentation methods is based on fully convolutional neural networks (FCN) and can be successfully used to do segmentation in cardiac cine MRI slices. However, the temporal dependency between slices acquired at neighboring time points is not used. Here, based on our previously proposed FCN structure, we proposed a new algorithm to segment LV myocardium in porcine short axis cardiac cine MRI by incorporating convolutional long short-term memory (Conv-LSTM) to leverage the temporal dependency. In this approach, instead of processing each slice independently in a conventional CNN-based approach, the Conv-LSTM architecture captures the dynamics of cardiac motion over time. In a leave-one-out experiment on 8 porcine specimens (3,600 slices), the proposed approach was shown to be promising by achieving average mean Dice similarity coefficient (DSC) of 0.84, Hausdorff distance (HD) of 6.35 mm, and average perpendicular distance (APD) of 1.09 mm when compared with manual segmentations, which improved the performance of our previous FCN-based approach (average mean DSC=0.84, HD=6.78 mm, and APD=1.11 mm). Qualitatively, our model showed robustness against low image quality and complications in the surrounding anatomy due to its ability to capture the dynamics of cardiac motion.

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.

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.

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.

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.

Modeling and Control of CSTR using Model based Neural Network Predictive Control

OpenAIRE

Shrivastava, Piyush

2012-01-01

This paper presents a predictive control strategy based on neural network model of the plant is applied to Continuous Stirred Tank Reactor (CSTR). This system is a highly nonlinear process; therefore, a nonlinear predictive method, e.g., neural network predictive control, can be a better match to govern the system dynamics. In the paper, the NN model and the way in which it can be used to predict the behavior of the CSTR process over a certain prediction horizon are described, and some commen...

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.

Adaptive control of a PWR core power using neural networks

International Nuclear Information System (INIS)

Arab-Alibeik, H.; Setayeshi, S.

2005-01-01

Reactor power control is important because of safety concerns and the call for regular and appropriate operation of nuclear power plants. It seems that the load-follow operation of these plants will be unavoidable in the future. Discrepancies between the real plant and the model used in controller design for load-follow operation encourage one to use auto-tuning and (or) adaptive techniques. Neural network technology shows great promise for addressing many problems in non-model-based adaptive control methods. Also, there has been a great attention to inverse control especially in the neural and fuzzy control context. Fortunately, online adaptation eliminates some limitations of inverse control and its shortcomings for real world applications. We use a neural adaptive inverse controller to control the power of a PWR reactor. The stability of the system and convergence of the controller parameters are guaranteed during online adaptation phase provided the controller is near the plant's real inverse after offline training period. The performance of the controller is verified using nonlinear simulations in diverse operating conditions

Adaptive model predictive process control using neural networks

Science.gov (United States)

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

1997-08-19

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

A Gain-Scheduling PI Control Based on Neural Networks

Directory of Open Access Journals (Sweden)

Stefania Tronci

2017-01-01

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

Intelligent neural network and fuzzy logic control of industrial and power systems

Science.gov (United States)

Kuljaca, Ognjen

The main role played by neural network and fuzzy logic intelligent control algorithms today is to identify and compensate unknown nonlinear system dynamics. There are a number of methods developed, but often the stability analysis of neural network and fuzzy control systems was not provided. This work will meet those problems for the several algorithms. Some more complicated control algorithms included backstepping and adaptive critics will be designed. Nonlinear fuzzy control with nonadaptive fuzzy controllers is also analyzed. An experimental method for determining describing function of SISO fuzzy controller is given. The adaptive neural network tracking controller for an autonomous underwater vehicle is analyzed. A novel stability proof is provided. The implementation of the backstepping neural network controller for the coupled motor drives is described. Analysis and synthesis of adaptive critic neural network control is also provided in the work. Novel tuning laws for the system with action generating neural network and adaptive fuzzy critic are given. Stability proofs are derived for all those control methods. It is shown how these control algorithms and approaches can be used in practical engineering control. Stability proofs are given. Adaptive fuzzy logic control is analyzed. Simulation study is conducted to analyze the behavior of the adaptive fuzzy system on the different environment changes. A novel stability proof for adaptive fuzzy logic systems is given. Also, adaptive elastic fuzzy logic control architecture is described and analyzed. A novel membership function is used for elastic fuzzy logic system. The stability proof is proffered. Adaptive elastic fuzzy logic control is compared with the adaptive nonelastic fuzzy logic control. The work described in this dissertation serves as foundation on which analysis of particular representative industrial systems will be conducted. Also, it gives a good starting point for analysis of learning abilities of

Premature Ventricular Contraction Coupling Interval Variability Destabilizes Cardiac Neuronal and Electrophysiological Control: Insights From Simultaneous Cardioneural Mapping.

Science.gov (United States)

Hamon, David; Rajendran, Pradeep S; Chui, Ray W; Ajijola, Olujimi A; Irie, Tadanobu; Talebi, Ramin; Salavatian, Siamak; Vaseghi, Marmar; Bradfield, Jason S; Armour, J Andrew; Ardell, Jeffrey L; Shivkumar, Kalyanam

2017-04-01

Variability in premature ventricular contraction (PVC) coupling interval (CI) increases the risk of cardiomyopathy and sudden death. The autonomic nervous system regulates cardiac electrical and mechanical indices, and its dysregulation plays an important role in cardiac disease pathogenesis. The impact of PVCs on the intrinsic cardiac nervous system, a neural network on the heart, remains unknown. The objective was to determine the effect of PVCs and CI on intrinsic cardiac nervous system function in generating cardiac neuronal and electric instability using a novel cardioneural mapping approach. In a porcine model (n=8), neuronal activity was recorded from a ventricular ganglion using a microelectrode array, and cardiac electrophysiological mapping was performed. Neurons were functionally classified based on their response to afferent and efferent cardiovascular stimuli, with neurons that responded to both defined as convergent (local reflex processors). Dynamic changes in neuronal activity were then evaluated in response to right ventricular outflow tract PVCs with fixed short, fixed long, and variable CI. PVC delivery elicited a greater neuronal response than all other stimuli ( P <0.001). Compared with fixed short and long CI, PVCs with variable CI had a greater impact on neuronal response ( P <0.05 versus short CI), particularly on convergent neurons ( P <0.05), as well as neurons receiving sympathetic ( P <0.05) and parasympathetic input ( P <0.05). The greatest cardiac electric instability was also observed after variable (short) CI PVCs. Variable CI PVCs affect critical populations of intrinsic cardiac nervous system neurons and alter cardiac repolarization. These changes may be critical for arrhythmogenesis and remodeling, leading to cardiomyopathy. © 2017 American Heart Association, Inc.

Bioimpedance harmonic analysis as a tool to simultaneously assess circulation and nervous control.

Science.gov (United States)

Mudraya, I S; Revenko, S V; Nesterov, A V; Gavrilov, I Yu; Kirpatovsky, V I

2011-07-01

Multicycle harmonic (Fourier) analysis of bioimpedance was employed to simultaneously assess circulation and neural activity in visceral (rat urinary bladder) and somatic (human finger) organs. The informative value of the first cardiac harmonic of the bladder impedance as an index of bladder circulation is demonstrated. The individual reactions of normal and obstructive bladders in response to infusion cystometry were recorded. The potency of multicycle harmonic analysis of bioimpedance to assess sympathetic and parasympathetic neural control in urinary bladder is discussed. In the human finger, bioimpedance harmonic analysis revealed three periodic components at the rate of the heart beat, respiration and Mayer wave (0.1 Hz), which were observed under normal conditions and during blood flow arrest in the hand. The revealed spectrum peaks were explained by the changes in systemic blood pressure and in regional vascular tone resulting from neural vasomotor control. During normal respiration and circulation, two side cardiac peaks were revealed in a bioimpedance amplitude spectrum, whose amplitude reflected the depth of amplitude respiratory modulation of the cardiac output. During normal breathing, the peaks corresponding to the second and third cardiac harmonics were split, reflecting frequency respiratory modulation of the heart rate. Multicycle harmonic analysis of bioimpedance is a novel potent tool to examine the interaction between the respiratory and cardiovascular system and to simultaneously assess regional circulation and neural influences in visceral and somatic organs.

Three Pillars for the Neural Control of Appetite.

Science.gov (United States)

Sternson, Scott M; Eiselt, Anne-Kathrin

2017-02-10

The neural control of appetite is important for understanding motivated behavior as well as the present rising prevalence of obesity. Over the past several years, new tools for cell type-specific neuron activity monitoring and perturbation have enabled increasingly detailed analyses of the mechanisms underlying appetite-control systems. Three major neural circuits strongly and acutely influence appetite but with notably different characteristics. Although these circuits interact, they have distinct properties and thus appear to contribute to separate but interlinked processes influencing appetite, thereby forming three pillars of appetite control. Here, we summarize some of the key characteristics of appetite circuits that are emerging from recent work and synthesize the findings into a provisional framework that can guide future studies.

Neural networks and orbit control in accelerators

International Nuclear Information System (INIS)

Bozoki, E.; Friedman, A.

1994-01-01

An overview of the architecture, workings and training of Neural Networks is given. We stress the aspects which are important for the use of Neural Networks for orbit control in accelerators and storage rings, especially its ability to cope with the nonlinear behavior of the orbit response to 'kicks' and the slow drift in the orbit response during long-term operation. Results obtained for the two NSLS storage rings with several network architectures and various training methods for each architecture are given

Adaptive training of neural networks for control of autonomous mobile robots

NARCIS (Netherlands)

Steur, E.; Vromen, T.; Nijmeijer, H.; Fossen, T.I.; Nijmeijer, H.; Pettersen, K.Y.

2017-01-01

We present an adaptive training procedure for a spiking neural network, which is used for control of a mobile robot. Because of manufacturing tolerances, any hardware implementation of a spiking neural network has non-identical nodes, which limit the performance of the controller. The adaptive

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.

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...

Application of neural network technology to setpoint control of a simulated reactor experiment loop

International Nuclear Information System (INIS)

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

1991-01-01

This paper 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 the best neural network design 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. 5 refs., 8 figs., 3 tabs

Sliding Intermittent Control for BAM Neural Networks with Delays

Directory of Open Access Journals (Sweden)

Jianqiang Hu

2013-01-01

Full Text Available This paper addresses the exponential stability problem for a class of delayed bidirectional associative memory (BAM neural networks with delays. A sliding intermittent controller which takes the advantages of the periodically intermittent control idea and the impulsive control scheme is proposed and employed to the delayed BAM system. With the adjustable parameter taking different particular values, such a sliding intermittent control method can comprise several kinds of control schemes as special cases, such as the continuous feedback control, the impulsive control, the periodically intermittent control, and the semi-impulsive control. By using analysis techniques and the Lyapunov function methods, some sufficient criteria are derived for the closed-loop delayed BAM neural networks to be globally exponentially stable. Finally, two illustrative examples are given to show the effectiveness of the proposed control scheme and the obtained theoretical results.

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.

Cardiac effects of electrically induced intrathoracic autonomic reflexes.

Science.gov (United States)

Armour, J A

1988-06-01

Electrical stimulation of the afferent components in one cardiopulmonary nerve (the left vagosympathetic complex at a level immediately caudal to the origin of the left recurrent laryngeal nerve) in acutely decentralized thoracic autonomic ganglionic preparations altered cardiac chronotropism and inotropism in 17 of 44 dogs. Since these neural preparations were acutely decentralized, the effects were mediated presumably via intrathoracic autonomic reflexes. The lack of consistency of these reflexly generated cardiac responses presumably were due in part to anatomical variation of afferent axons in the afferent nerve stimulated. As stimulation of the afferent components in the same neural structure caudal to the heart (where cardiopulmonary afferent axons are not present) failed to elicit cardiac responses in any dog, it is presumed that when cardiac responses were elicited by the more cranially located stimulations, these were due to activation of afferent axons arising from the heart and (or) lungs. When cardiac responses were elicited, intramyocardial pressures in the right ventricular conus as well as the ventral and lateral walls of the left ventricle were augmented. Either bradycardia or tachycardia was elicited. Following hexamethonium administration no responses were produced, demonstrating that nicotonic cholinergic synaptic mechanisms were involved in these intrathoracic cardiopulmonary-cardiac reflexes. In six of the animals, when atropine was administered before hexamethonium, reflexly generated responses were attenuated. The same thing occurred when morphine was administered in four animals. In contrast, in four animals following administration of phentolamine, the reflexly generated changes were enhanced.(ABSTRACT TRUNCATED AT 250 WORDS)

Neural Network with Local Memory for Nuclear Reactor Power Level Control

International Nuclear Information System (INIS)

Uluyol, Oender; Ragheb, Magdi; Tsoukalas, Lefteri

2001-01-01

A methodology is introduced for a neural network with local memory called a multilayered local output gamma feedback (LOGF) neural network within the paradigm of locally-recurrent globally-feedforward neural networks. It appears to be well-suited for the identification, prediction, and control tasks in highly dynamic systems; it allows for the presentation of different timescales through incorporation of a gamma memory. A learning algorithm based on the backpropagation-through-time approach is derived. The spatial and temporal weights of the network are iteratively optimized for a given problem using the derived learning algorithm. As a demonstration of the methodology, it is applied to the task of power level control of a nuclear reactor at different fuel cycle conditions. The results demonstrate that the LOGF neural network controller outperforms the classical as well as the state feedback-assisted classical controllers for reactor power level control by showing a better tracking of the demand power, improving the fuel and exit temperature responses, and by performing robustly in different fuel cycle and power level conditions

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.

Applying neural networks to control the TFTR neutral beam ion sources

International Nuclear Information System (INIS)

Lagin, L.

1992-01-01

This paper describes the application of neural networks to the control of the neutral beam long-pulse positive ion source accelerators on the Tokamak Fusion Test Reactor (TFTR) at Princeton University. Neural networks were used to learn how the operators adjust the control setpoints when running these sources. The data sets used to train these networks were derived from a large database containing actual setpoints and power supply waveform calculations for the 1990 run period. The networks learned what the optimum control setpoints should initially be set based uon desired accel voltage and perveance levels. Neural networks were also used to predict the divergence of the ion beam

Application and Simulation of Fuzzy Neural Network PID Controller in the Aircraft Cabin Temperature

Directory of Open Access Journals (Sweden)

Ding Fang

2013-06-01

Full Text Available Considering complex factors of affecting ambient temperature in Aircraft cabin, and some shortages of traditional PID control like the parameters difficult to be tuned and control ineffective, this paper puts forward the intelligent PID algorithm that makes fuzzy logic method and neural network together, scheming out the fuzzy neural net PID controller. After the correction of the fuzzy inference and dynamic learning of neural network, PID parameters of the controller get the optimal parameters. MATLAB simulation results of the cabin temperature control model show that the performance of the fuzzy neural network PID controller has been greatly improved, with faster response, smaller overshoot and better adaptability.

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

Science.gov (United States)

Jimenez-Romero, Cristian; Johnson, Jeffrey

2017-01-01

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

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.

Intraoperative tight glucose control using hyperinsulinemic normoglycemia increases delirium after cardiac surgery.

Science.gov (United States)

Saager, Leif; Duncan, Andra E; Yared, Jean-Pierre; Hesler, Brian D; You, Jing; Deogaonkar, Anupa; Sessler, Daniel I; Kurz, Andrea

2015-06-01

Postoperative delirium is common in patients recovering from cardiac surgery. Tight glucose control has been shown to reduce mortality and morbidity. Therefore, the authors sought to determine the effect of tight intraoperative glucose control using a hyperinsulinemic-normoglycemic clamp approach on postoperative delirium in patients undergoing cardiac surgery. The authors enrolled 198 adult patients having cardiac surgery in this randomized, double-blind, single-center trial. Patients were randomly assigned to either tight intraoperative glucose control with a hyperinsulinemic-normoglycemic clamp (target blood glucose, 80 to 110 mg/dl) or standard therapy (conventional insulin administration with blood glucose target, battery. The authors considered patients to have experienced postoperative delirium when Confusion Assessment Method testing was positive at any assessment. A positive Confusion Assessment Method was defined by the presence of features 1 (acute onset and fluctuating course) and 2 (inattention) and either 3 (disorganized thinking) or 4 (altered consciousness). Patients randomized to tight glucose control were more likely to be diagnosed as being delirious than those assigned to routine glucose control (26 of 93 vs. 15 of 105; relative risk, 1.89; 95% CI, 1.06 to 3.37; P = 0.03), after adjusting for preoperative usage of calcium channel blocker and American Society of Anesthesiologist physical status. Delirium severity, among patients with delirium, was comparable with each glucose management strategy. Intraoperative hyperinsulinemic-normoglycemia augments the risk of delirium after cardiac surgery, but not its severity.

Robust Adaptive Neural Control of Morphing Aircraft with Prescribed Performance

OpenAIRE

Wu, Zhonghua; Lu, Jingchao; Shi, Jingping; Liu, Yang; Zhou, Qing

2017-01-01

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) tech...

Neural networks for combined control of capacitor banks and voltage regulators in distribution systems

Energy Technology Data Exchange (ETDEWEB)

Gu, Z.; Rizy, D.T.

1996-02-01

A neural network for controlling shunt capacitor banks and feeder voltage regulators in electric distribution systems is presented. The objective of the neural controller is to minimize total I{sup 2}R losses and maintain all bus voltages within standard limits. The performance of the neural network for different input selections and training data is discussed and compared. Two different input selections are tried, one using the previous control states of the capacitors and regulator along with measured line flows and voltage which is equivalent to having feedback and the other with measured line flows and voltage without previous control settings. The results indicate that the neural net controller with feedback can outperform the one without. Also, proper selection of a training data set that adequately covers the operating space of the distribution system is important for achieving satisfactory performance with the neural controller. The neural controller is tested on a radially configured distribution system with 30 buses, 5 switchable capacitor banks an d one nine tap line regulator to demonstrate the performance characteristics associated with these principles. Monte Carlo simulations show that a carefully designed and relatively compact neural network with a small but carefully developed training set can perform quite well under slight and extreme variation of loading conditions.

Four Degree Freedom Robot Arm with Fuzzy Neural Network Control

Directory of Open Access Journals (Sweden)

Şinasi Arslan

2013-01-01

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

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

Science.gov (United States)

Wang, Leimin; Shen, Yi; Zhang, Guodong

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

Automatic initialization and quality control of large-scale cardiac MRI segmentations.

Science.gov (United States)

Albà, Xènia; Lekadir, Karim; Pereañez, Marco; Medrano-Gracia, Pau; Young, Alistair A; Frangi, Alejandro F

2018-01-01

Continuous advances in imaging technologies enable ever more comprehensive phenotyping of human anatomy and physiology. Concomitant reduction of imaging costs has resulted in widespread use of imaging in large clinical trials and population imaging studies. Magnetic Resonance Imaging (MRI), in particular, offers one-stop-shop multidimensional biomarkers of cardiovascular physiology and pathology. A wide range of analysis methods offer sophisticated cardiac image assessment and quantification for clinical and research studies. However, most methods have only been evaluated on relatively small databases often not accessible for open and fair benchmarking. Consequently, published performance indices are not directly comparable across studies and their translation and scalability to large clinical trials or population imaging cohorts is uncertain. Most existing techniques still rely on considerable manual intervention for the initialization and quality control of the segmentation process, becoming prohibitive when dealing with thousands of images. The contributions of this paper are three-fold. First, we propose a fully automatic method for initializing cardiac MRI segmentation, by using image features and random forests regression to predict an initial position of the heart and key anatomical landmarks in an MRI volume. In processing a full imaging database, the technique predicts the optimal corrective displacements and positions in relation to the initial rough intersections of the long and short axis images. Second, we introduce for the first time a quality control measure capable of identifying incorrect cardiac segmentations with no visual assessment. The method uses statistical, pattern and fractal descriptors in a random forest classifier to detect failures to be corrected or removed from subsequent statistical analysis. Finally, we validate these new techniques within a full pipeline for cardiac segmentation applicable to large-scale cardiac MRI databases. The

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.

DNA methyltransferase 3b is dispensable for mouse neural crest development.

Directory of Open Access Journals (Sweden)

Bridget T Jacques-Fricke

Full Text Available The neural crest is a population of multipotent cells that migrates extensively throughout vertebrate embryos to form diverse structures. Mice mutant for the de novo DNA methyltransferase DNMT3b exhibit defects in two neural crest derivatives, the craniofacial skeleton and cardiac ventricular septum, suggesting that DNMT3b activity is necessary for neural crest development. Nevertheless, the requirement for DNMT3b specifically in neural crest cells, as opposed to interacting cell types, has not been determined. Using a conditional DNMT3b allele crossed to the neural crest cre drivers Wnt1-cre and Sox10-cre, neural crest DNMT3b mutants were generated. In both neural crest-specific and fully DNMT3b-mutant embryos, cranial neural crest cells exhibited only subtle migration defects, with increased numbers of dispersed cells trailing organized streams in the head. In spite of this, the resulting cranial ganglia, craniofacial skeleton, and heart developed normally when neural crest cells lacked DNMT3b. This indicates that DNTM3b is not necessary in cranial neural crest cells for their development. We conclude that defects in neural crest derivatives in DNMT3b mutant mice reflect a requirement for DNMT3b in lineages such as the branchial arch mesendoderm or the cardiac mesoderm that interact with neural crest cells during formation of these structures.

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

DEFF Research Database (Denmark)

Dasgupta, Sakyasingha; Goldschmidt, Dennis; Wörgötter, Florentin

2015-01-01

here, an artificial bio-inspired walking system which effectively combines biomechanics (in terms of the body and leg structures) with the underlying neural mechanisms. The neural mechanisms consist of (1) central pattern generator based control for generating basic rhythmic patterns and coordinated......Walking animals, like stick insects, cockroaches or ants, demonstrate a fascinating range of locomotive abilities and complex behaviors. The locomotive behaviors can consist of a variety of walking patterns along with adaptation that allow the animals to deal with changes in environmental...... conditions, like uneven terrains, gaps, obstacles etc. Biological study has revealed that such complex behaviors are a result of a combination of biomechanics and neural mechanism thus representing the true nature of embodied interactions. While the biomechanics helps maintain flexibility and sustain...

Pathological links between stroke and cardiac arrest

Institute of Scientific and Technical Information of China (English)

Shaila Ghanekar; Sydney Corey; Trenton Lippert; Cesar V.Borlongan

2017-01-01

There may be a pathological connection between cardiac failure and ischemic stroke.In this article we describe pertinent research that demonstrates subsequent death of cardiac and neural myocytes in the post ischemic stroke brain.Current stroke therapy overlooks the connection between cardiac and cerebrovascular events and fails to address the shared risk factors.Current pre-clinical stroke investigations have provided evidence that suggests the presence of an indirect cell death pathway in which toxic molecules emanate from the stroke brain and trigger cardiac cell death.On the other hand,other studies highlight the presence of a reverse cell death cascade in which toxic molecules from the heart,following cardiac arrest,travel to the brain and induce ischemic cell death.Further examination of these putative cell death pathways between ischemic stroke and cardiac arrest will prompt the advancement of innovative treatments specifically targeting both diseases,leading to ameliorated clinical results of patients diagnosed with heart failure and ischemic stroke.

Neural network feedforward control of a closed-circuit wind tunnel

Science.gov (United States)

Sutcliffe, Peter

Accurate control of wind-tunnel test conditions can be dramatically enhanced using feedforward control architectures which allow operating conditions to be maintained at a desired setpoint through the use of mathematical models as the primary source of prediction. However, as the desired accuracy of the feedforward prediction increases, the model complexity also increases, so that an ever increasing computational load is incurred. This drawback can be avoided by employing a neural network that is trained offline using the output of a high fidelity wind-tunnel mathematical model, so that the neural network can rapidly reproduce the predictions of the model with a greatly reduced computational overhead. A novel neural network database generation method, developed through the use of fractional factorial arrays, was employed such that a neural network can accurately predict wind-tunnel parameters across a wide range of operating conditions whilst trained upon a highly efficient database. The subsequent network was incorporated into a Neural Network Model Predictive Control (NNMPC) framework to allow an optimised output schedule capable of providing accurate control of the wind-tunnel operating parameters. Facilitation of an optimised path through the solution space is achieved through the use of a chaos optimisation algorithm such that a more globally optimum solution is likely to be found with less computational expense than the gradient descent method. The parameters associated with the NNMPC such as the control horizon are determined through the use of a Taguchi methodology enabling the minimum number of experiments to be carried out to determine the optimal combination. The resultant NNMPC scheme was employed upon the Hessert Low Speed Wind Tunnel at the University of Notre Dame to control the test-section temperature such that it follows a pre-determined reference trajectory during changes in the test-section velocity. Experimental testing revealed that the

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

Science.gov (United States)

Williams-Hayes, Peggy S.

2004-01-01

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

Cardiac output measurement instruments controlled by microprocessors

International Nuclear Information System (INIS)

Spector, M.; Barritault, L.; Boeri, C.; Fauchet, M.; Gambini, D.; Vernejoul, P. de

The nuclear medicine and biophysics laboratory of the Necker-Enfants malades University Hospital Centre has built a microprocessor controlled Cardiac flowmetre. The principle of the cardiac output measurement from a radiocardiogram is well established. After injection of a radioactive indicator upstream from the heart cavities the dilution curve is obtained by the use of a gamma-ray precordial detector. This curve normally displays two peaks due to passage of the indicator into the right and left sides of the heart respectively. The output is then obtained from the stewart Hamilton principle once recirculation is eliminated. The graphic method used for the calculation however is long and tedious. The decreasing fraction of the dilution curve is projected in logarithmic space in order to eliminate recirculation by determining the mean straight line from which the decreasing exponential is obtained. The principle of the use of microprocessors is explained (electronics, logics) [fr

Controlled neural network application in track-match problem

International Nuclear Information System (INIS)

Baginyan, S.A.; Ososkov, G.A.

1993-01-01

Track-match problem of high energy physics (HEP) data handling is formulated in terms of incidence matrices. The corresponding Hopfield neural network is developed to solve this type of constraint satisfaction problems (CSP). A special concept of the controlled neural network is proposed as a basis of an algorithm for the effective CSP solution. Results of comparable calculations show the very high performance of this algorithm against conventional search procedures. 8 refs.; 1 fig.; 1 tab

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…

neural control system

International Nuclear Information System (INIS)

Elshazly, A.A.E.

2002-01-01

Automatic power stabilization control is the desired objective for any reactor operation , especially, nuclear power plants. A major problem in this area is inevitable gap between a real plant ant the theory of conventional analysis and the synthesis of linear time invariant systems. in particular, the trajectory tracking control of a nonlinear plant is a class of problems in which the classical linear transfer function methods break down because no transfer function can represent the system over the entire operating region . there is a considerable amount of research on the model-inverse approach using feedback linearization technique. however, this method requires a prices plant model to implement the exact linearizing feedback, for nuclear reactor systems, this approach is not an easy task because of the uncertainty in the plant parameters and un-measurable state variables . therefore, artificial neural network (ANN) is used either in self-tuning control or in improving the conventional rule-based exper system.the main objective of this thesis is to suggest an ANN, based self-learning controller structure . this method is capable of on-line reinforcement learning and control for a nuclear reactor with a totally unknown dynamics model. previously, researches are based on back- propagation algorithm . back -propagation (BP), fast back -propagation (FBP), and levenberg-marquardt (LM), algorithms are discussed and compared for reinforcement learning. it is found that, LM algorithm is quite superior

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.

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

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

Science.gov (United States)

Bao, Gang; Zeng, Zhigang; Shen, Yanjun

2018-02-01

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

DCS-Neural-Network Program for Aircraft Control and Testing

Science.gov (United States)

Jorgensen, Charles C.

2006-01-01

A computer program implements a dynamic-cell-structure (DCS) artificial neural network that can perform such tasks as learning selected aerodynamic characteristics of an airplane from wind-tunnel test data and computing real-time stability and control derivatives of the airplane for use in feedback linearized control. A DCS neural network is one of several types of neural networks that can incorporate additional nodes in order to rapidly learn increasingly complex relationships between inputs and outputs. In the DCS neural network implemented by the present program, the insertion of nodes is based on accumulated error. A competitive Hebbian learning rule (a supervised-learning rule in which connection weights are adjusted to minimize differences between actual and desired outputs for training examples) is used. A Kohonen-style learning rule (derived from a relatively simple training algorithm, implements a Delaunay triangulation layout of neurons) is used to adjust node positions during training. Neighborhood topology determines which nodes are used to estimate new values. The network learns, starting with two nodes, and adds new nodes sequentially in locations chosen to maximize reductions in global error. At any given time during learning, the error becomes homogeneously distributed over all nodes.

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.

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.

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.

Self-esteem fluctuations and cardiac vagal control in everyday life.

Science.gov (United States)

Schwerdtfeger, Andreas R; Scheel, Sophie-Marie

2012-03-01

It has been proposed that self-esteem buffers threat-responding. The same effect is ascribed to the vagus nerve, which is a primary nerve of the parasympathetic nervous system. Consequently, it has been suggested that self-esteem and cardiac vagal tone are interconnected on a trait, as well as on a state, level. In this study, we examined the relationship of vagal cardiac control and self-esteem fluctuations across a single day using ecological momentary assessment. Eighty-four participants were recruited, and self-esteem, negative affect, and vagal tone were recorded throughout a 22-hour period. Men provided higher self-esteem ratings than women, but the negative relationship between self-esteem and negative affect was stronger in women. Moreover, controlling for potential confounds (e.g., age, BMI, depressive symptoms, smoking status, regular physical activity), we observed that for men, self-esteem was significantly positively associated with cardiac vagal tone, whereas for women it was not. These findings suggest that the relationship between self-esteem and vagal innervation of the heart during daily life is sex-specific and might involve different central-autonomic pathways for men and women, respectively. Copyright © 2011 Elsevier B.V. All rights reserved.

Application of a neural network to control a pressurized water reactor

International Nuclear Information System (INIS)

Lin, C.; Ku, C.C.; Lee, C.S.

1993-01-01

A neural network has been trained to control a pressurized water reactor. The inputs of the training pattern are the plant signals, and the outputs are the control rod actions. The training patterns are some kind of lookup table of control action. The table is designed by the heuristic method, which is based on the designer's knowledge of the controlled system and the operation experience. This method has two advantages: The controller's performance does not depend on the mathematical model of the plant, and the controller could be a nonlinear one. The advantages of using neural networks to implement the controller are to save computing time and overcome partial hardware failure

Adaptive Neural Network Algorithm for Power Control in Nuclear Power Plants

International Nuclear Information System (INIS)

Husam Fayiz, Al Masri

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. (paper)

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.

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

Science.gov (United States)

Peng, Jinzhu; Dubay, Rickey

2011-10-01

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

The role of cardiac vagal tone and inhibitory control in pre-schoolers' listening comprehension.

Science.gov (United States)

Scrimin, Sara; Patron, Elisabetta; Florit, Elena; Palomba, Daniela; Mason, Lucia

2017-12-01

This study investigated the role of basal cardiac activity and inhibitory control at the beginning of the school year in predicting oral comprehension at the end of the year in pre-schoolers. Forty-three, 4-year-olds participated in the study. At the beginning of the school year children's electrocardiogram at rest was registered followed by the assessment of inhibitory control as well as verbal working memory and verbal ability. At the end of the year all children were administered a listening comprehension ability measure. A stepwise regression showed a significant effect of basal cardiac vagal tone in predicting listening comprehension together with inhibitory control and verbal ability. These results are among the first to show the predictive role of basal cardiac vagal tone and inhibitory control in pre-schoolers' oral text comprehension, and offer new insight into the association between autonomic regulation of the heart, inhibitory control, and cognitive activity at a young age. © 2017 Wiley Periodicals, Inc.

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.

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.

A Tensor-Product-Kernel Framework for Multiscale Neural Activity Decoding and Control

Science.gov (United States)

Li, Lin; Brockmeier, Austin J.; Choi, John S.; Francis, Joseph T.; Sanchez, Justin C.; Príncipe, José C.

2014-01-01

Brain machine interfaces (BMIs) have attracted intense attention as a promising technology for directly interfacing computers or prostheses with the brain's motor and sensory areas, thereby bypassing the body. The availability of multiscale neural recordings including spike trains and local field potentials (LFPs) brings potential opportunities to enhance computational modeling by enriching the characterization of the neural system state. However, heterogeneity on data type (spike timing versus continuous amplitude signals) and spatiotemporal scale complicates the model integration of multiscale neural activity. In this paper, we propose a tensor-product-kernel-based framework to integrate the multiscale activity and exploit the complementary information available in multiscale neural activity. This provides a common mathematical framework for incorporating signals from different domains. The approach is applied to the problem of neural decoding and control. For neural decoding, the framework is able to identify the nonlinear functional relationship between the multiscale neural responses and the stimuli using general purpose kernel adaptive filtering. In a sensory stimulation experiment, the tensor-product-kernel decoder outperforms decoders that use only a single neural data type. In addition, an adaptive inverse controller for delivering electrical microstimulation patterns that utilizes the tensor-product kernel achieves promising results in emulating the responses to natural stimulation. PMID:24829569

The association between depressive disorder and cardiac autonomic control in adults 60 years and older.

Science.gov (United States)

Licht, Carmilla M M; Naarding, Paul; Penninx, Brenda W J H; van der Mast, Roos C; de Geus, Eco J C; Comijs, Hannie

2015-04-01

Altered cardiac autonomic control has often been reported in depressed persons and might play an important role in the increased risk for cardiovascular disease (CVD). A negative association between cardiac autonomic control and depression might become specifically clinically relevant in persons 60 years or older as CVD risk increases with age. This study included data of 321 persons with a depressive disorder and 115 controls participating in the Netherlands Study of Depression in Older Persons (mean age = 70.3 years, 65.7% female). Respiratory sinus arrhythmia (RSA), heart rate (HR), and preejection period (PEP) were measured and compared between depressed persons and controls. In addition, the role of antidepressants and clinical characteristics (e.g., age of depression onset and comorbid anxiety) was examined. Compared with controls, depressed persons had lower RSA (mean [standard error of the mean] = 23.5 [1.2] milliseconds versus 18.6 [0.7] milliseconds, p = .001, d = 0.373) and marginally higher HR (73.1 [1.1] beats/min versus 75.6 [0.6] beats/min, p = .065, d = 0.212), but comparable PEP (113.9 [2.1] milliseconds versus 112.0 [1.2] milliseconds, p = .45, d = 0.087), fully adjusted. Antidepressants strongly attenuated the associations between depression and HR and RSA. Antidepressant-naïve depressed persons had similar HR and RSA to controls, whereas users of antidepressants showed significantly lower RSA. In addition, tricyclic antidepressant users had higher HR (p 768) and shorter PEP (p = .014, d = 0.395) than did controls. Depression was not associated with cardiac autonomic control, but antidepressants were in this sample. All antidepressants were associated with low cardiac parasympathetic control and specifically tricyclic antidepressants with high cardiac sympathetic control.

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. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Neural Networks in Control Applications

DEFF Research Database (Denmark)

Sørensen, O.

are examined. The models are separated into three groups representing input/output descriptions as well as state space descriptions: - Models, where all in- and outputs are measurable (static networks). - Models, where some inputs are non-measurable (recurrent networks). - Models, where some in- and some...... outputs are non-measurable (recurrent networks with incomplete state information). The three groups are ordered in increasing complexity, and for each group it is shown how to solve the problems concerning training and application of the specific model type. Of particular interest are the model types...... Kalmann filter) representing state space description. The potentials of neural networks for control of non-linear processes are also examined, focusing on three different groups of control concepts, all considered as generalizations of known linear control concepts to handle also non-linear processes...

Neural control of vascular reactions: impact of emotion and attention.

Science.gov (United States)

Okon-Singer, Hadas; Mehnert, Jan; Hoyer, Jana; Hellrung, Lydia; Schaare, Herma Lina; Dukart, Juergen; Villringer, Arno

2014-03-19

This study investigated the neural regions involved in blood pressure reactions to negative stimuli and their possible modulation by attention. Twenty-four healthy human subjects (11 females; age = 24.75 ± 2.49 years) participated in an affective perceptual load task that manipulated attention to negative/neutral distractor pictures. fMRI data were collected simultaneously with continuous recording of peripheral arterial blood pressure. A parametric modulation analysis examined the impact of attention and emotion on the relation between neural activation and blood pressure reactivity during the task. When attention was available for processing the distractor pictures, negative pictures resulted in behavioral interference, neural activation in brain regions previously related to emotion, a transient decrease of blood pressure, and a positive correlation between blood pressure response and activation in a network including prefrontal and parietal regions, the amygdala, caudate, and mid-brain. These effects were modulated by attention; behavioral and neural responses to highly negative distractor pictures (compared with neutral pictures) were smaller or diminished, as was the negative blood pressure response when the central task involved high perceptual load. Furthermore, comparing high and low load revealed enhanced activation in frontoparietal regions implicated in attention control. Our results fit theories emphasizing the role of attention in the control of behavioral and neural reactions to irrelevant emotional distracting information. Our findings furthermore extend the function of attention to the control of autonomous reactions associated with negative emotions by showing altered blood pressure reactions to emotional stimuli, the latter being of potential clinical relevance.

Neural network-based control of an intelligent solar Stirling pump

International Nuclear Information System (INIS)

Tavakolpour-Saleh, A.R.; Jokar, H.

2016-01-01

In this paper, an ANN (artificial neural network) control system is applied to a novel solar-powered active LTD (low temperature differential) Stirling pump. First, a mathematical description of the proposed Stirling pump is presented. Then, optimum operating frequencies of the converter corresponding to different operating conditions (i.e. different sink and source temperatures and water heads) are investigated using the proposed mathematical framework. It is found that the proposed complex mathematical scheme has a very slow convergence and thus, is not appropriate for real-time implementation of the model-based controller. Consequently, a NN (neural network) model with a lower complexity is proposed to learn the simulation data obtained from the mathematical model. The designed neural network controller is thus applied to a digital processor to effectively tune the converter frequency so that a maximum output power is acquired. Finally, the performance of the proposed mechatronic system is evaluated experimentally. The experimental results clearly demonstrate the feasibility of pumping water at low temperature difference under variable operating conditions using the proposed intelligent Stirling converter. - Highlights: • A novel intelligent solar-powered active LTD Stirling pump was introduced. • A neural network controller was used to tune the converter speed. • The intelligent converter was able to adapt itself to different operating conditions. • It was possible to excite the water column with its resonance mode. • Experimental results showed the effectiveness of the proposed converter.

A randomized controlled trial of cell salvage in routine cardiac surgery.

Science.gov (United States)

Klein, Andrew A; Nashef, Samer A M; Sharples, Linda; Bottrill, Fiona; Dyer, Matthew; Armstrong, Johanna; Vuylsteke, Alain

2008-11-01

Previous trials have indicated that cell salvage may reduce allogeneic blood transfusion during cardiac surgery, but these studies have limitations, including inconsistent use of other blood transfusion-sparing strategies. We designed a randomized controlled trial to determine whether routine cell salvage for elective uncomplicated cardiac surgery reduces blood transfusion and is cost effective in the setting of a rigorous transfusion protocol and routine administration of antifibrinolytics. Two-hundred-thirteen patients presenting for first-time coronary artery bypass grafting and/or cardiac valve surgery were prospectively randomized to control or cell salvage groups. The latter group had blood aspirate during surgery and mediastinal drainage the first 6 h after surgery processed in a cell saver device and autotransfused. All patients received tranexamic acid and were subjected to an algorithm for red blood cell and hemostatic blood factor transfusion. There was no difference between the two groups in the proportion of patients exposed to allogeneic blood (32% in both groups, relative risk 1.0 P = 0.89). At current blood products and cell saver prices, the use of cell salvage increased the costs per patient by a minimum of $103. When patients who had mediastinal re-exploration for bleeding were excluded (as planned in the protocol), significantly fewer units of allogeneic red blood cells were transfused in the cell salvage compared with the control group (65 vs 100 U, relative risk 0.71 P = 0.04). In patients undergoing routine first-time cardiac surgery in an institution with a rigorous blood conservation program, the routine use of cell salvage does not further reduce the proportion of patients exposed to allogeneic blood transfusion. However, patients who do not have excessive bleeding after surgery receive significantly fewer units of blood with cell salvage. Although the use of cell savage may reduce the demand for blood products during cardiac surgery, this

A biologically inspired neural network controller for ballistic arm movements

Directory of Open Access Journals (Sweden)

Schmid Maurizio

2007-09-01

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

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.

Computer simulation system of neural PID control on nuclear reactor

International Nuclear Information System (INIS)

Chen Yuzhong; Yang Kaijun; Shen Yongping

2001-01-01

Neural network proportional integral differential (PID) controller on nuclear reactor is designed, and the control process is simulated by computer. The simulation result show that neutral network PID controller can automatically adjust its parameter to ideal state, and good control result can be gotten in reactor control process

Obesity-related differences in neural correlates of force control.

Science.gov (United States)

Mehta, Ranjana K; Shortz, Ashley E

2014-01-01

Greater body segment mass due to obesity has shown to impair gross and fine motor functions and reduce balance control. While recent studies suggest that obesity may be linked with altered brain functions involved in fine motor tasks, this association is not well investigated. The purpose of this study was to examine the neural correlates of motor performance in non-obese and obese adults during force control of two upper extremity muscles. Nine non-obese and eight obese young adults performed intermittent handgrip and elbow flexion exertions at 30% of their respective muscle strengths for 4 min. Functional near infrared spectroscopy was employed to measure neural activity in the prefrontal cortex bilaterally, joint steadiness was computed using force fluctuations, and ratings of perceived exertions (RPEs) were obtained to assess perceived effort. Obesity was associated with higher force fluctuations and lower prefrontal cortex activation during handgrip exertions, while RPE scores remained similar across both groups. No obesity-related differences in neural activity, force fluctuation, or RPE scores were observed during elbow flexion exertions. The study is one of the first to examine obesity-related differences on prefrontal cortex activation during force control of the upper extremity musculature. The study findings indicate that the neural correlates of motor activity in the obese may be muscle-specific. Future work is warranted to extend the investigation to monitoring multiple motor-function related cortical regions and examining obesity differences with different task parameters (e.g., longer duration, increased precision demands, larger muscles, etc.).

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

Directory of Open Access Journals (Sweden)

Mehmet eKocaturk

2015-08-01

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

Gas Turbine Engine Control Design Using Fuzzy Logic and Neural Networks

Directory of Open Access Journals (Sweden)

M. Bazazzadeh

2011-01-01

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

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

Science.gov (United States)

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

2015-11-01

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

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

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

Science.gov (United States)

Chen, Weisheng; Hua, Shaoyong; Zhang, Huaguang

2015-02-01

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

RBF Neural Network Approach for Identification and Control of DC Motors

Directory of Open Access Journals (Sweden)

EA Feilat

2012-12-01

Full Text Available In this paper, a neural network approach for the identification and control of a separately excited direct (DC motor (SEDCM driving a centrifugal pump load is applied. In this application, two radial basis function neural networks (RBFNN are used: The first is a RBFNN identifier trained offline to emulate the dynamic performance of the DC motor-load system. The second is a RBFNN controller, which is trained to make the motor speed follow a selected reference signal. Two RBFNN control schemes are proposed using direct inverse and internal model control schemes. The performance of the RBFNN identifier and controller is investigated in terms of step response, sharp changes in speed trajectory, and sudden load change, as well as changes in motor parameters. The performance of RBFNN in system identification and control has been compared with the performance of the well-known back-propagation neural network (BPNN. The simulation results show that both of the BPNN and RBFNN controllers exhibit excellent dynamic response, adapt well to changes in speed trajectory and load connected to the motor, and adapt to the variations of motor parameters. Furthermore, the simulation results show that the step response of RBFNN internal model and direct inverse controllers are identical.

Spatially divergent cardiac responses to nicotinic stimulation of ganglionated plexus neurons in the canine heart.

Science.gov (United States)

Cardinal, René; Pagé, Pierre; Vermeulen, Michel; Ardell, Jeffrey L; Armour, J Andrew

2009-01-28

Ganglionated plexuses (GPs) are major constituents of the intrinsic cardiac nervous system, the final common integrator of regional cardiac control. We hypothesized that nicotinic stimulation of individual GPs exerts divergent regional influences, affecting atrial as well as ventricular functions. In 22 anesthetized canines, unipolar electrograms were recorded from 127 atrial and 127 ventricular epicardial loci during nicotine injection (100 mcg in 0.1 ml) into either the 1) right atrial (RA), 2) dorsal atrial, 3) left atrial, 4) inferior vena cava-inferior left atrial, 5) right ventricular, 6) ventral septal ventricular or 7) cranial medial ventricular (CMV) GP. In addition to sinus and AV nodal function, neural effects on atrial and ventricular repolarization were identified as changes in the area subtended by unipolar recordings under basal conditions and at maximum neurally-induced effects. Animals were studied with intact AV node or following ablation to achieve ventricular rate control. Atrial rate was affected in response to stimulation of all 7 GPs with an incidence of 50-95% of the animals among the different GPs. AV conduction was affected following stimulation of 6/7 GP with an incidence of 22-75% among GPs. Atrial and ventricular repolarization properties were affected by atrial as well as ventricular GP stimulation. Distinct regional patterns of repolarization changes were identified in response to stimulation of individual GPs. RAGP predominantly affected the RA and posterior right ventricular walls whereas CMVGP elicited biatrial and biventricular repolarization changes. Spatially divergent and overlapping cardiac regions are affected in response to nicotinic stimulation of neurons in individual GPs.

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

Directory of Open Access Journals (Sweden)

Rong Mei

2017-01-01

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

Parasympathetic neural activity accounts for the lowering of exercise heart rate at high altitude

DEFF Research Database (Denmark)

Boushel, Robert Christopher; Calbet, J A; Rådegran, G

2001-01-01

In chronic hypoxia, both heart rate (HR) and cardiac output (Q) are reduced during exercise. The role of parasympathetic neural activity in lowering HR is unresolved, and its influence on Q and oxygen transport at high altitude has never been studied.......In chronic hypoxia, both heart rate (HR) and cardiac output (Q) are reduced during exercise. The role of parasympathetic neural activity in lowering HR is unresolved, and its influence on Q and oxygen transport at high altitude has never been studied....

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.

Cardiovascular Control in Men and Women

Science.gov (United States)

Fu, Qi

Women, primarily young women, have a greater incidence of orthostatic intolerance than agematched men. This difference is especially dramatic in the Postural Orthostatic Tachycardia Syndrome (POTS, also called Chronic Orthostatic Intolerance, in which patients are unable to stand or remain upright for prolonged periods of time due to intolerable light headedness, weakness, and near-syncope). However, the mechanisms underlying this gender difference are still not completely understood. It is likely that certain gender-specific factors such as the normal menstrual cycle, differences in some hormonal levels which may affect the neurohumoral regulation of blood pressure, or physical characteristics such as a smaller and less "distensible" heart may influence orthostatic blood pressure control. The authors review what has been done on the effects of gender and the menstrual cycle on sympathetic neural control of hemodynamics during shortand long-term orthostasis in healthy young individuals and in female patients with POTS. In addition, the role of cardiac size and function, a non-neural mechanism, in gender differences in orthostatic tolerance is also reviewed. It is suggested that sympathetic neural control and vasoconstrictor responses during orthostasis are comparable between healthy men and women, and are enhanced but not impaired in POTS patients. There is a gender-specific difference in cardiac size even in the healthy population, while this difference is exaggerated in female patients with POTS.

Autonomic cardiac innervation

Science.gov (United States)

Hasan, Wohaib

2013-01-01

Autonomic cardiac neurons have a common origin in the neural crest but undergo distinct developmental differentiation as they mature toward their adult phenotype. Progenitor cells respond to repulsive cues during migration, followed by differentiation cues from paracrine sources that promote neurochemistry and differentiation. When autonomic axons start to innervate cardiac tissue, neurotrophic factors from vascular tissue are essential for maintenance of neurons before they reach their targets, upon which target-derived trophic factors take over final maturation, synaptic strength and postnatal survival. Although target-derived neurotrophins have a central role to play in development, alternative sources of neurotrophins may also modulate innervation. Both developing and adult sympathetic neurons express proNGF, and adult parasympathetic cardiac ganglion neurons also synthesize and release NGF. The physiological function of these “non-classical” cardiac sources of neurotrophins remains to be determined, especially in relation to autocrine/paracrine sustenance during development.   Cardiac autonomic nerves are closely spatially associated in cardiac plexuses, ganglia and pacemaker regions and so are sensitive to release of neurotransmitter, neuropeptides and trophic factors from adjacent nerves. As such, in many cardiac pathologies, it is an imbalance within the two arms of the autonomic system that is critical for disease progression. Although this crosstalk between sympathetic and parasympathetic nerves has been well established for adult nerves, it is unclear whether a degree of paracrine regulation occurs across the autonomic limbs during development. Aberrant nerve remodeling is a common occurrence in many adult cardiovascular pathologies, and the mechanisms regulating outgrowth or denervation are disparate. However, autonomic neurons display considerable plasticity in this regard with neurotrophins and inflammatory cytokines having a central regulatory

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.

Two stage neural network modelling for robust model predictive control.

Science.gov (United States)

Patan, Krzysztof

2018-01-01

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

Self-Organizing Neural Circuits for Sensory-Guided Motor Control

National Research Council Canada - National Science Library

Grossberg, Stephen

1999-01-01

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

Cardiac arrest during gamete release in chum salmon regulated by the parasympathetic nerve system.

Directory of Open Access Journals (Sweden)

Yuya Makiguchi

Full Text Available Cardiac arrest caused by startling stimuli, such as visual and vibration stimuli, has been reported in some animals and could be considered as an extraordinary case of bradycardia and defined as reversible missed heart beats. Variability of the heart rate is established as a balance between an autonomic system, namely cholinergic vagus inhibition, and excitatory adrenergic stimulation of neural and hormonal action in teleost. However, the cardiac arrest and its regulating nervous mechanism remain poorly understood. We show, by using electrocardiogram (ECG data loggers, that cardiac arrest occurs in chum salmon (Oncorhynchus keta at the moment of gamete release for 7.39+/-1.61 s in females and for 5.20+/-0.97 s in males. The increase in heart rate during spawning behavior relative to the background rate during the resting period suggests that cardiac arrest is a characteristic physiological phenomenon of the extraordinarily high heart rate during spawning behavior. The ECG morphological analysis showed a peaked and tall T-wave adjacent to the cardiac arrest, indicating an increase in potassium permeability in cardiac muscle cells, which would function to retard the cardiac action potential. Pharmacological studies showed that the cardiac arrest was abolished by injection of atropine, a muscarinic receptor antagonist, revealing that the cardiac arrest is a reflex response of the parasympathetic nerve system, although injection of sotalol, a beta-adrenergic antagonist, did not affect the cardiac arrest. We conclude that cardiac arrest during gamete release in spawning release in spawning chum salmon is a physiological reflex response controlled by the parasympathetic nervous system. This cardiac arrest represents a response to the gaping behavior that occurs at the moment of gamete release.

Prediction of tonic parasympathetic cardiac control using respiratory sinus arrhythmia: the need for respiratory control

NARCIS (Netherlands)

GROSSMAN, P.; Karemaker, J.; Wieling, W.

1991-01-01

Respiratory sinus arrhythmia (RSA) has received much attention in recent years due to the large body of evidence indicating that variations in this phenomenon represent alterations in parasympathetic cardiac control. Although it appears that respiratory sinus arrhythmia is mediated by vagal

Apoptosis in neural crest cells by functional loss of APC tumor suppressor gene

Science.gov (United States)

Hasegawa, Sumitaka; Sato, Tomoyuki; Akazawa, Hiroshi; Okada, Hitoshi; Maeno, Akiteru; Ito, Masaki; Sugitani, Yoshinobu; Shibata, Hiroyuki; Miyazaki, Jun-ichi; Katsuki, Motoya; Yamauchi, Yasutaka; Yamamura, Ken-ichi; Katamine, Shigeru; Noda, Tetsuo

2002-01-01

Apc is a gene associated with familial adenomatous polyposis coli (FAP) and its inactivation is a critical step in colorectal tumor formation. The protein product, adenomatous polyposis coli (APC), acts to down-regulate intracellular levels of β-catenin, a key signal transducer in the Wnt signaling. Conditional targeting of Apc in the neural crest of mice caused massive apoptosis of cephalic and cardiac neural crest cells at about 11.5 days post coitum, resulting in craniofacial and cardiac anomalies at birth. Notably, the apoptotic cells localized in the regions where β-catenin had accumulated. In contrast to its role in colorectal epithelial cells, inactivation of APC leads to dysregulation of β-catenin/Wnt signaling with resultant apoptosis in certain tissues including neural crest cells. PMID:11756652

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

Directory of Open Access Journals (Sweden)

Xiangwei Bu

2017-01-01

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

Self-tuning control of a nuclear reactor using a Gaussian function neural network

International Nuclear Information System (INIS)

Park, M.G.; Cho, N.Z.

1995-01-01

A self-tuning control method is described for a nuclear reactor system that requires only a set of input-output measurements. The use of an artificial neural network in nonlinear model-based adaptive control, both as a plant model and a controller, is investigated. A neural network called a Gaussian function network is used for one-step-ahead predictive control to track the desired plant output. The effectiveness of the controller is demonstrated by the application of the method to the power tracking control of the Korea Multipurpose Research Reactor

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...... we developed a discriminative training algorithm for SHC models, where each SHC model for a specific speech pattern is trained with utterances of the pattern to be recognized and with other utterances. The discriminative training of SHC neural models has been tested on the TIDIGITS database...

Cognitive-affective neural plasticity following active-controlled mindfulness intervention

DEFF Research Database (Denmark)

Allen, Micah Galen

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

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.

[Third phase of cardiac rehabilitation: a nurse-based "home-control" model].

Science.gov (United States)

Albertini, Sara; Ciocca, Antonella; Opasich, Cristina; Pinna, Gian Domenico; Cobelli, Franco

2011-12-01

Phase 3 is a critical point for cardiac rehabilitation: many problems don't allow achieving a correct secondary prevention, in particular regarding the relationship between patient and cardiologist. Aiming at ensuring continuity of care of phase 3 cardiac rehabilitation patients, we have developed a telemetric educational program to stimulate in them the will and capacity to become active comanagers of their disease. Nurses specialized in cardiac rehabilitation, with the collaboration of the general practitioners, contact the patients by scheduled phone calls to collect questionnaires about their health status and the result of biochemistry. All the results are analyzed by the nurses and discussed with each patient (educational reinforcement). The effects of this program of comanagement of cardiac disease and secondary prevention are analyzed comparing each patient data at the discharge with data after one year and those coming from our archive (retrospective analysis). The patients enrolled in this study pay much more attention to the amount of food they eat; they tend not to gain weight, and they restart smoking in a reduced proportion compared to patients not enrolled in the study. However, despite having received better information on their cardiac disease, their compliance to physical training, consumption of healthy food, and pharmacological therapy is not improved. This study focuses on the role of a continuous educational program of a cardiac rehabilitation unit after the patient's discharge. This home control program conducted by nurses specialized in cardiac rehabilitation, with the assistance of cardiologists, psychologists and physiotherapists, and in collaboration with the general practitioner, was quite cheap, and helped maximizing the knowledge of the disease and reinforcing correct life style in the patients. The results are not as good as expected, probably because one year does not represent a sufficient time, or because the educational

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

Science.gov (United States)

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

2018-01-01

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

A novel neural-net-based nonlinear adaptive control and application to the cross-direction deviations control of a polymer film spread line

International Nuclear Information System (INIS)

Chen Zengqiang; Li Xiang; Liu Zhongxin; Yuan Zhuzhi

2008-01-01

A novel neural adaptive controller is presented to effectively control multivariable nonlinear systems. The proposed neural controller has been successfully applied to the cross-direction deviation control system of a polymer film spread line, whose good performance has been verified with real-time running results

Towards an Irritable Bowel Syndrome Control System Based on Artificial Neural Networks

Science.gov (United States)

Podolski, Ina; Rettberg, Achim

To solve health problems with medical applications that use complex algorithms is a trend nowadays. It could also be a chance to help patients with critical problems caused from nerve irritations to overcome them and provide a better living situation. In this paper a system for monitoring and controlling the nerves from the intestine is described on a theoretical basis. The presented system could be applied to the irritable bowel syndrome. For control a neural network is used. The advantages for using a neural network for the control of irritable bowel syndrome are the adaptation and learning. These two aspects are important because the syndrome behavior varies from patient to patient and have also concerning the time a lot of variations with respect to each patient. The developed neural network is implemented and can be simulated. Therefore, it can be shown how the network monitor and control the nerves for individual input parameters.

Using the artificial neural network to control the steam turbine heating process

International Nuclear Information System (INIS)

Nowak, Grzegorz; Rusin, Andrzej

2016-01-01

Highlights: • Inverse Artificial Neural Network has a potential to control the start-up process of a steam turbine. • Two serial neural networks made it possible to model the rotor stress based of steam parameters. • An ANN with feedback enables transient stress modelling with good accuracy. - Abstract: Due to the significant share of renewable energy sources (RES) – wind farms in particular – in the power sector of many countries, power generation systems become sensitive to variable weather conditions. Under unfavourable changes in weather, ensuring required energy supplies involves hasty start-ups of conventional steam power units whose operation should be characterized by higher and higher flexibility. Controlling the process of power engineering machinery operation requires fast predictive models that will make it possible to analyse many parallel scenarios and select the most favourable one. This approach is employed by the algorithm for the inverse neural network control presented in this paper. Based on the current thermal state of the turbine casing, the algorithm controls the steam temperature at the turbine inlet to keep both the start-up rate and the safety of the machine at the allowable level. The method used herein is based on two artificial neural networks (ANN) working in series.

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.

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.

Automatic gain control of neural coupling during cooperative hand movements.

Science.gov (United States)

Thomas, F A; Dietz, V; Schrafl-Altermatt, M

2018-04-13

Cooperative hand movements (e.g. opening a bottle) are controlled by a task-specific neural coupling, reflected in EMG reflex responses contralateral to the stimulation site. In this study the contralateral reflex responses in forearm extensor muscles to ipsilateral ulnar nerve stimulation was analyzed at various resistance and velocities of cooperative hand movements. The size of contralateral reflex responses was closely related to the level of forearm muscle activation required to accomplish the various cooperative hand movement tasks. This indicates an automatic gain control of neural coupling that allows a rapid matching of corrective forces exerted at both sides of an object with the goal 'two hands one action'.

Multimodal neural correlates of cognitive control in the Human Connectome Project.

Science.gov (United States)

Lerman-Sinkoff, Dov B; Sui, Jing; Rachakonda, Srinivas; Kandala, Sridhar; Calhoun, Vince D; Barch, Deanna M

2017-12-01

Cognitive control is a construct that refers to the set of functions that enable decision-making and task performance through the representation of task states, goals, and rules. The neural correlates of cognitive control have been studied in humans using a wide variety of neuroimaging modalities, including structural MRI, resting-state fMRI, and task-based fMRI. The results from each of these modalities independently have implicated the involvement of a number of brain regions in cognitive control, including dorsal prefrontal cortex, and frontal parietal and cingulo-opercular brain networks. However, it is not clear how the results from a single modality relate to results in other modalities. Recent developments in multimodal image analysis methods provide an avenue for answering such questions and could yield more integrated models of the neural correlates of cognitive control. In this study, we used multiset canonical correlation analysis with joint independent component analysis (mCCA + jICA) to identify multimodal patterns of variation related to cognitive control. We used two independent cohorts of participants from the Human Connectome Project, each of which had data from four imaging modalities. We replicated the findings from the first cohort in the second cohort using both independent and predictive analyses. The independent analyses identified a component in each cohort that was highly similar to the other and significantly correlated with cognitive control performance. The replication by prediction analyses identified two independent components that were significantly correlated with cognitive control performance in the first cohort and significantly predictive of performance in the second cohort. These components identified positive relationships across the modalities in neural regions related to both dynamic and stable aspects of task control, including regions in both the frontal-parietal and cingulo-opercular networks, as well as regions

Randomized controlled trial of internal and external targeted temperature management methods in post- cardiac arrest patients.

Science.gov (United States)

Look, Xinqi; Li, Huihua; Ng, Mingwei; Lim, Eric Tien Siang; Pothiawala, Sohil; Tan, Kenneth Boon Kiat; Sewa, Duu Wen; Shahidah, Nur; Pek, Pin Pin; Ong, Marcus Eng Hock

2018-01-01

Targeted temperature management post-cardiac arrest is currently implemented using various methods, broadly categorized as internal and external. This study aimed to evaluate survival-to-hospital discharge and neurological outcomes (Glasgow-Pittsburgh Score) of post-cardiac arrest patients undergoing internal cooling verses external cooling. A randomized controlled trial of post-resuscitation cardiac arrest patients was conducted from October 2008-September 2014. Patients were randomized to either internal or external cooling methods. Historical controls were selected matched by age and gender. Analysis using SPSS version 21.0 presented descriptive statistics and frequencies while univariate logistic regression was done using R 3.1.3. 23 patients were randomized to internal cooling and 22 patients to external cooling and 42 matched controls were selected. No significant difference was seen between internal and external cooling in terms of survival, neurological outcomes and complications. However in the internal cooling arm, there was lower risk of developing overcooling (p=0.01) and rebound hyperthermia (p=0.02). Compared to normothermia, internal cooling had higher survival (OR=3.36, 95% CI=(1.130, 10.412), and lower risk of developing cardiac arrhythmias (OR=0.18, 95% CI=(0.04, 0.63)). Subgroup analysis showed those with cardiac cause of arrest (OR=4.29, 95% CI=(1.26, 15.80)) and sustained ROSC (OR=5.50, 95% CI=(1.64, 20.39)) had better survival with internal cooling compared to normothermia. Cooling curves showed tighter temperature control for internal compared to external cooling. Internal cooling showed tighter temperature control compared to external cooling. Internal cooling can potentially provide better survival-to-hospital discharge outcomes and reduce cardiac arrhythmia complications in carefully selected patients as compared to normothermia. Copyright © 2017. Published by Elsevier Inc.

Competition model for aperiodic stochastic resonance in a Fitzhugh-Nagumo model of cardiac sensory neurons.

Science.gov (United States)

Kember, G C; Fenton, G A; Armour, J A; Kalyaniwalla, N

2001-04-01

Regional cardiac control depends upon feedback of the status of the heart from afferent neurons responding to chemical and mechanical stimuli as transduced by an array of sensory neurites. Emerging experimental evidence shows that neural control in the heart may be partially exerted using subthreshold inputs that are amplified by noisy mechanical fluctuations. This amplification is known as aperiodic stochastic resonance (ASR). Neural control in the noisy, subthreshold regime is difficult to see since there is a near absence of any correlation between input and the output, the latter being the average firing (spiking) rate of the neuron. This lack of correlation is unresolved by traditional energy models of ASR since these models are unsuitable for identifying "cause and effect" between such inputs and outputs. In this paper, the "competition between averages" model is used to determine what portion of a noisy, subthreshold input is responsible, on average, for the output of sensory neurons as represented by the Fitzhugh-Nagumo equations. A physiologically relevant conclusion of this analysis is that a nearly constant amount of input is responsible for a spike, on average, and this amount is approximately independent of the firing rate. Hence, correlation measures are generally reduced as the firing rate is lowered even though neural control under this model is actually unaffected.

Neural control and transient analysis of the LCL-type resonant converter

Science.gov (United States)

Zouggar, S.; Nait Charif, H.; Azizi, M.

2000-07-01

This paper proposes a generalised inverse learning structure to control the LCL converter. A feedforward neural network is trained to act as an inverse model of the LCL converter then both are cascaded such that the composed system results in an identity mapping between desired response and the LCL output voltage. Using the large signal model, we analyse the transient output response of the controlled LCL converter in the case of large variation of the load. The simulation results show the efficiency of using neural networks to regulate the LCL converter.

Synchronization of Hierarchical Time-Varying Neural Networks Based on Asynchronous and Intermittent Sampled-Data Control.

Science.gov (United States)

Xiong, Wenjun; Patel, Ragini; Cao, Jinde; Zheng, Wei Xing

In this brief, our purpose is to apply asynchronous and intermittent sampled-data control methods to achieve the synchronization of hierarchical time-varying neural networks. The asynchronous and intermittent sampled-data controllers are proposed for two reasons: 1) the controllers may not transmit the control information simultaneously and 2) the controllers cannot always exist at any time . The synchronization is then discussed for a kind of hierarchical time-varying neural networks based on the asynchronous and intermittent sampled-data controllers. Finally, the simulation results are given to illustrate the usefulness of the developed criteria.In this brief, our purpose is to apply asynchronous and intermittent sampled-data control methods to achieve the synchronization of hierarchical time-varying neural networks. The asynchronous and intermittent sampled-data controllers are proposed for two reasons: 1) the controllers may not transmit the control information simultaneously and 2) the controllers cannot always exist at any time . The synchronization is then discussed for a kind of hierarchical time-varying neural networks based on the asynchronous and intermittent sampled-data controllers. Finally, the simulation results are given to illustrate the usefulness of the developed criteria.

Adaptive online state-of-charge determination based on neuro-controller and neural network

Energy Technology Data Exchange (ETDEWEB)

Shen Yanqing, E-mail: network_hawk@126.co [Department of Automation, Chongqing Industry Polytechnic College, Jiulongpo District, Chongqing 400050 (China)

2010-05-15

This paper presents a novel approach using adaptive artificial neural network based model and neuro-controller for online cell State of Charge (SOC) determination. Taking cell SOC as model's predictive control input unit, radial basis function neural network, which can adjust its structure to prediction error with recursive least square algorithm, is used to simulate battery system. Besides that, neuro-controller based on Back-Propagation Neural Network (BPNN) and modified PID controller is used to decide the control input of battery system, i.e., cell SOC. Finally this algorithm is applied for the SOC determination of lead-acid batteries, and results of lab tests on physical cells, compared with model prediction, are presented. Results show that the ANN based battery system model adaptively simulates battery system with great accuracy, and the predicted SOC simultaneously converges to the real value quickly within the error of +-1 as time goes on.

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.

Role of cardiac volume receptors in the control of ADH release during acute simulated weightlessness in man

Science.gov (United States)

Convertino, V. A.; Benjamin, B. A.; Keil, L. C.; Sandler, H.

1984-01-01

Hemodynamic responses and antidiuretic hormone (ADH) were measured during body position changes, designed to induce central blood volume shifts in ten cardiac and one heart-lung transplant recipients, to assess the contribution of cardiac volume receptors in the control of ADH release during the initial acute phase of exposure to weightlessness. Each subject underwent 15 min of a sitting-control period (C) followed by 30 min of 6 deg headdown tilt (T) and 30 min of resumed sitting (S). Venous blood samples and cardiac dimensions were taken at 0 and 15 min of C; 5, 15, and 30 min of T; and 5, 15, and 30 min of S. Blood samples were analyzed for hematocrit, plasma osmolality, plasma renin activity (PRA), and ADH. Heart rate and blood pressure were recorded every two min. Plasma osmolality was not altered by posture changes. Mean left ventricular end-diastolic volume increased (P less than 0.05) from 90 ml in C to 106 ml in T and returned to 87 ml in S. Plasma ADH was reduced by 20 percent (P less than 0.05) with T, and returned to control levels with S. These responses were similar in six normal cardiac-innervated control subjects. These data may suggest that cardiac volume receptors are not the primary mechanism for the control of ADH release during acute central volume shifts in man.

Exercise capacity in diabetes mellitus is predicted by activity status and cardiac size rather than cardiac function: a case control study.

Science.gov (United States)

Roberts, Timothy J; Burns, Andrew T; MacIsaac, Richard J; MacIsaac, Andrew I; Prior, David L; La Gerche, André

2018-03-23

The reasons for reduced exercise capacity in diabetes mellitus (DM) remains incompletely understood, although diastolic dysfunction and diabetic cardiomyopathy are often favored explanations. However, there is a paucity of literature detailing cardiac function and reserve during incremental exercise to evaluate its significance and contribution. We sought to determine associations between comprehensive measures of cardiac function during exercise and maximal oxygen consumption ([Formula: see text]peak), with the hypothesis that the reduction in exercise capacity and cardiac function would be associated with co-morbidities and sedentary behavior rather than diabetes itself. This case-control study involved 60 subjects [20 with type 1 DM (T1DM), 20 T2DM, and 10 healthy controls age/sex-matched to each diabetes subtype] performing cardiopulmonary exercise testing and bicycle ergometer echocardiography studies. Measures of biventricular function were assessed during incremental exercise to maximal intensity. T2DM subjects were middle-aged (52 ± 11 years) with a mean T2DM diagnosis of 12 ± 7 years and modest glycemic control (HbA 1c 57 ± 12 mmol/mol). T1DM participants were younger (35 ± 8 years), with a 19 ± 10 year history of T1DM and suboptimal glycemic control (HbA 1c 65 ± 16 mmol/mol). Participants with T2DM were heavier than their controls (body mass index 29.3 ± 3.4 kg/m 2 vs. 24.7 ± 2.9, P = 0.001), performed less exercise (10 ± 12 vs. 28 ± 30 MET hours/week, P = 0.031) and had lower exercise capacity ([Formula: see text]peak = 26 ± 6 vs. 38 ± 8 ml/min/kg, P accounting for age, sex and body surface area in a multivariate analysis, significant positive predictors of [Formula: see text]peak were cardiac size (LV end-diastolic volume, LVEDV) and estimated MET-hours, while T2DM was a negative predictor. These combined factors accounted for 80% of the variance in [Formula: see text

HAND2 Target Gene Regulatory Networks Control Atrioventricular Canal and Cardiac Valve Development.

Science.gov (United States)

Laurent, Frédéric; Girdziusaite, Ausra; Gamart, Julie; Barozzi, Iros; Osterwalder, Marco; Akiyama, Jennifer A; Lincoln, Joy; Lopez-Rios, Javier; Visel, Axel; Zuniga, Aimée; Zeller, Rolf

2017-05-23

The HAND2 transcriptional regulator controls cardiac development, and we uncover additional essential functions in the endothelial to mesenchymal transition (EMT) underlying cardiac cushion development in the atrioventricular canal (AVC). In Hand2-deficient mouse embryos, the EMT underlying AVC cardiac cushion formation is disrupted, and we combined ChIP-seq of embryonic hearts with transcriptome analysis of wild-type and mutants AVCs to identify the functionally relevant HAND2 target genes. The HAND2 target gene regulatory network (GRN) includes most genes with known functions in EMT processes and AVC cardiac cushion formation. One of these is Snai1, an EMT master regulator whose expression is lost from Hand2-deficient AVCs. Re-expression of Snai1 in mutant AVC explants partially restores this EMT and mesenchymal cell migration. Furthermore, the HAND2-interacting enhancers in the Snai1 genomic landscape are active in embryonic hearts and other Snai1-expressing tissues. These results show that HAND2 directly regulates the molecular cascades initiating AVC cardiac valve development. Copyright © 2017 The Author(s). Published by Elsevier Inc. All rights reserved.

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

Energy Technology Data Exchange (ETDEWEB)

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

2009-07-01

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

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

Directory of Open Access Journals (Sweden)

M. Sridevi

2010-10-01

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

Semi-empirical neural network models of controlled dynamical systems

Directory of Open Access Journals (Sweden)

Mihail V. Egorchev

2017-12-01

Full Text Available A simulation approach is discussed for maneuverable aircraft motion as nonlinear controlled dynamical system under multiple and diverse uncertainties including knowledge imperfection concerning simulated plant and its environment exposure. The suggested approach is based on a merging of theoretical knowledge for the plant with training tools of artificial neural network field. The efficiency of this approach is demonstrated using the example of motion modeling and the identification of the aerodynamic characteristics of a maneuverable aircraft. A semi-empirical recurrent neural network based model learning algorithm is proposed for multi-step ahead prediction problem. This algorithm sequentially states and solves numerical optimization subproblems of increasing complexity, using each solution as initial guess for subsequent subproblem. We also consider a procedure for representative training set acquisition that utilizes multisine control signals.

Prediction and characterisation of a highly conserved, remote and cAMP responsive enhancer that regulates Msx1 gene expression in cardiac neural crest and outflow tract.

Science.gov (United States)

Miller, Kerry Ann; Davidson, Scott; Liaros, Angela; Barrow, John; Lear, Marissa; Heine, Danielle; Hoppler, Stefan; MacKenzie, Alasdair

2008-05-15

Double knockouts of the Msx1 and Msx2 genes in the mouse result in severe cardiac outflow tract malformations similar to those frequently found in newborn infants. Despite the known role of the Msx genes in cardiac formation little is known of the regulatory systems (ligand receptor, signal transduction and protein-DNA interactions) that regulate the tissue-specific expression of the Msx genes in mammals during the formation of the outflow tract. In the present study we have used a combination of multi-species comparative genomics, mouse transgenic analysis and in-situ hybridisation to predict and validate the existence of a remote ultra-conserved enhancer that supports the expression of the Msx1 gene in migrating mouse cardiac neural crest and the outflow tract primordia. Furthermore, culturing of embryonic explants derived from transgenic lines with agonists of the PKC and PKA signal transduction systems demonstrates that this remote enhancer is influenced by PKA but not PKC dependent gene regulatory systems. These studies demonstrate the efficacy of combining comparative genomics and transgenic analyses and provide a platform for the study of the possible roles of Msx gene mis-regulation in the aetiology of congenital heart malformation.

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

Science.gov (United States)

Long, Lijun; Zhao, Jun

2017-07-01

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

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

Indian Academy of Sciences (India)

In this paper, an adaptive fuzzy neural controller (AFNC) for a class of unknown chaotic systems is ... The robust controller is used to guarantee the stability and to control the per- ..... From the above analysis we have the following theorem:.

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

Directory of Open Access Journals (Sweden)

Jie Zhao

2015-01-01

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

Neural Network Control for the Probe Landing Based on Proportional Integral Observer

Directory of Open Access Journals (Sweden)

Yuanchun Li

2015-01-01

Full Text Available For the probe descending and landing safely, a neural network control method based on proportional integral observer (PIO is proposed. First, the dynamics equation of the probe under the landing site coordinate system is deduced and the nominal trajectory meeting the constraints in advance on three axes is preplanned. Then the PIO designed by using LMI technique is employed in the control law to compensate the effect of the disturbance. At last, the neural network control algorithm is used to guarantee the double zero control of the probe and ensure the probe can land safely. An illustrative design example is employed to demonstrate the effectiveness of the proposed control approach.

Adaptive neural networks control for camera stabilization with active suspension system

Directory of Open Access Journals (Sweden)

Feng Zhao

2015-08-01

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

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

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

DEFF Research Database (Denmark)

Manoonpong, Poramate; Parlitz, Ulrich; Wörgötter, Florentin

2013-01-01

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...... 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...

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

Science.gov (United States)

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

2015-03-01

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

Neural manufacturing: a novel concept for processing modeling, monitoring, and control

Science.gov (United States)

Fu, Chi Y.; Petrich, Loren; Law, Benjamin

1995-09-01

Semiconductor fabrication lines have become extremely costly, and achieving a good return from such a high capital investment requires efficient utilization of these expensive facilities. It is highly desirable to shorten processing development time, increase fabrication yield, enhance flexibility, improve quality, and minimize downtime. We propose that these ends can be achieved by applying recent advances in the areas of artificial neural networks, fuzzy logic, machine learning, and genetic algorithms. We use the term neural manufacturing to describe such applications. This paper describes our use of artificial neural networks to improve the monitoring and control of semiconductor process.

Algebraic and adaptive learning in neural control systems

Science.gov (United States)

Ferrari, Silvia

A systematic approach is developed for designing adaptive and reconfigurable nonlinear control systems that are applicable to plants modeled by ordinary differential equations. The nonlinear controller comprising a network of neural networks is taught using a two-phase learning procedure realized through novel techniques for initialization, on-line training, and adaptive critic design. A critical observation is that the gradients of the functions defined by the neural networks must equal corresponding linear gain matrices at chosen operating points. On-line training is based on a dual heuristic adaptive critic architecture that improves control for large, coupled motions by accounting for actual plant dynamics and nonlinear effects. An action network computes the optimal control law; a critic network predicts the derivative of the cost-to-go with respect to the state. Both networks are algebraically initialized based on prior knowledge of satisfactory pointwise linear controllers and continue to adapt on line during full-scale simulations of the plant. On-line training takes place sequentially over discrete periods of time and involves several numerical procedures. A backpropagating algorithm called Resilient Backpropagation is modified and successfully implemented to meet these objectives, without excessive computational expense. This adaptive controller is as conservative as the linear designs and as effective as a global nonlinear controller. The method is successfully implemented for the full-envelope control of a six-degree-of-freedom aircraft simulation. The results show that the on-line adaptation brings about improved performance with respect to the initialization phase during aircraft maneuvers that involve large-angle and coupled dynamics, and parameter variations.

Self-control with spiking and non-spiking neural networks playing games.

Science.gov (United States)

Christodoulou, Chris; Banfield, Gaye; Cleanthous, Aristodemos

2010-01-01

Self-control can be defined as choosing a large delayed reward over a small immediate reward, while precommitment is the making of a choice with the specific aim of denying oneself future choices. Humans recognise that they have self-control problems and attempt to overcome them by applying precommitment. Problems in exercising self-control, suggest a conflict between cognition and motivation, which has been linked to competition between higher and lower brain functions (representing the frontal lobes and the limbic system respectively). This premise of an internal process conflict, lead to a behavioural model being proposed, based on which, we implemented a computational model for studying and explaining self-control through precommitment behaviour. Our model consists of two neural networks, initially non-spiking and then spiking ones, representing the higher and lower brain systems viewed as cooperating for the benefit of the organism. The non-spiking neural networks are of simple feed forward multilayer type with reinforcement learning, one with selective bootstrap weight update rule, which is seen as myopic, representing the lower brain and the other with the temporal difference weight update rule, which is seen as far-sighted, representing the higher brain. The spiking neural networks are implemented with leaky integrate-and-fire neurons with learning based on stochastic synaptic transmission. The differentiating element between the two brain centres in this implementation is based on the memory of past actions determined by an eligibility trace time constant. As the structure of the self-control problem can be likened to the Iterated Prisoner's Dilemma (IPD) game in that cooperation is to defection what self-control is to impulsiveness or what compromising is to insisting, we implemented the neural networks as two players, learning simultaneously but independently, competing in the IPD game. With a technique resembling the precommitment effect, whereby the

Oxygen-controlled automated neural differentiation of mouse embryonic stem cells.

Science.gov (United States)

Mondragon-Teran, Paul; Tostoes, Rui; Mason, Chris; Lye, Gary J; Veraitch, Farlan S

2013-03-01

Automation and oxygen tension control are two tools that provide significant improvements to the reproducibility and efficiency of stem cell production processes. the aim of this study was to establish a novel automation platform capable of controlling oxygen tension during both the cell-culture and liquid-handling steps of neural differentiation processes. We built a bespoke automation platform, which enclosed a liquid-handling platform in a sterile, oxygen-controlled environment. An airtight connection was used to transfer cell culture plates to and from an automated oxygen-controlled incubator. Our results demonstrate that our system yielded comparable cell numbers, viabilities, metabolism profiles and differentiation efficiencies when compared with traditional manual processes. Interestingly, eliminating exposure to ambient conditions during the liquid-handling stage resulted in significant improvements in the yield of MAP2-positive neural cells, indicating that this level of control can improve differentiation processes. This article describes, for the first time, an automation platform capable of maintaining oxygen tension control during both the cell-culture and liquid-handling stages of a 2D embryonic stem cell differentiation process.

Decoupling control of vehicle chassis system based on neural network inverse system

Science.gov (United States)

Wang, Chunyan; Zhao, Wanzhong; Luan, Zhongkai; Gao, Qi; Deng, Ke

2018-06-01

Steering and suspension are two important subsystems affecting the handling stability and riding comfort of the chassis system. In order to avoid the interference and coupling of the control channels between active front steering (AFS) and active suspension subsystems (ASS), this paper presents a composite decoupling control method, which consists of a neural network inverse system and a robust controller. The neural network inverse system is composed of a static neural network with several integrators and state feedback of the original chassis system to approach the inverse system of the nonlinear systems. The existence of the inverse system for the chassis system is proved by the reversibility derivation of Interactor algorithm. The robust controller is based on the internal model control (IMC), which is designed to improve the robustness and anti-interference of the decoupled system by adding a pre-compensation controller to the pseudo linear system. The results of the simulation and vehicle test show that the proposed decoupling controller has excellent decoupling performance, which can transform the multivariable system into a number of single input and single output systems, and eliminate the mutual influence and interference. Furthermore, it has satisfactory tracking capability and robust performance, which can improve the comprehensive performance of the chassis system.

Neural network for adapting nuclear power plant control for wide-range operation

International Nuclear Information System (INIS)

Ku, C.C.; Lee, K.Y.; Edwards, R.M.

1991-01-01

A new concept of using neural networks has been evaluated for optimal control of a nuclear reactor. The neural network uses the architecture of a standard backpropagation network; however, a new dynamic learning algorithm has been developed to capture the underlying system dynamics. The learning algorithm is based on parameter estimation for dynamic systems. The approach is demonstrated on an optimal reactor temperature controller by adjusting the feedback gains for wide-range operation. Application of optimal control to a reactor has been considered for improving temperature response using a robust fifth-order reactor power controller. Conventional gain scheduling can be employed to extend the range of good performance to accommodate large changes in power where nonlinear characteristics significantly modify the dynamics of the power plant. Gain scheduling is developed based on expected parameter variations, and it may be advantageous to further adapt feedback gains on-line to better match actual plant performance. A neural network approach is used here to adapt the gains to better accommodate plant uncertainties and thereby achieve improved robustness characteristics

Accelerator and feedback control simulation using neural networks

International Nuclear Information System (INIS)

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

Practical Application of Neural Networks in State Space Control

DEFF Research Database (Denmark)

Bendtsen, Jan Dimon

the networks, although some modifications are needed for the method to apply to the multilayer perceptron network. In connection with the multilayer perceptron networks it is also pointed out how instantaneous, sample-by-sample linearized state space models can be extracted from a trained network, thus opening......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...... 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...

TRIGA control rod position and reactivity transient Monitoring by Neural Networks

International Nuclear Information System (INIS)

Rosa, R.; Palomba, M.; Sepielli, M.

2008-01-01

Plant sensors drift or malfunction and operator actions in nuclear reactor control can be supported by sensor on-line monitoring, and data validation through soft-computing process. On-line recalibration can often avoid manual calibration or drifting component replacement. DSP requires prompt response to the modified conditions. Artificial Neural Network (ANN) and Fuzzy logic ensure: prompt response, link with field measurement and physical system behaviour, data incoming interpretation, and detection of discrepancy for mis-calibration or sensor faults. ANN (Artificial Neural Network) is a system based on the operation of biological neural networks. Although computing is day by day advancing, there are certain tasks that a program made for a common microprocessor is unable to perform. A software implementation of an ANN can be made with Pros and Cons. Pros: A neural network can perform tasks that a linear program can not; When an element of the neural network fails, it can continue without any problem by their parallel nature; A neural network learns and does not need to be reprogrammed; It can be implemented in any application; It can be implemented without any problem. Cons: The architecture of a neural network is different from the architecture of microprocessors therefore needs to be emulated; it requires high processing time for large neural networks; and the neural network needs training to operate. Three possibilities of training exist: Supervised learning: the network is trained providing input and matching output patterns; Unsupervised learning: input patterns are not a priori classified and the system must develop its own representation of the input stimuli; Reinforcement Learning: intermediate form of the above two types of learning, the learning machine does some action on the environment and gets a feedback response from the environment. Two TRIGAN ANN applications are considered: control rod position and fuel temperature. The outcome obtained in this

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.

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

African Journals Online (AJOL)

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

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.

Neural control of finger movement via intracortical brain-machine interface

Science.gov (United States)

Irwin, Z. T.; Schroeder, K. E.; Vu, P. P.; Bullard, A. J.; Tat, D. M.; Nu, C. S.; Vaskov, A.; Nason, S. R.; Thompson, D. E.; Bentley, J. N.; Patil, P. G.; Chestek, C. A.

2017-12-01

Objective. Intracortical brain-machine interfaces (BMIs) are a promising source of prosthesis control signals for individuals with severe motor disabilities. Previous BMI studies have primarily focused on predicting and controlling whole-arm movements; precise control of hand kinematics, however, has not been fully demonstrated. Here, we investigate the continuous decoding of precise finger movements in rhesus macaques. Approach. In order to elicit precise and repeatable finger movements, we have developed a novel behavioral task paradigm which requires the subject to acquire virtual fingertip position targets. In the physical control condition, four rhesus macaques performed this task by moving all four fingers together in order to acquire a single target. This movement was equivalent to controlling the aperture of a power grasp. During this task performance, we recorded neural spikes from intracortical electrode arrays in primary motor cortex. Main results. Using a standard Kalman filter, we could reconstruct continuous finger movement offline with an average correlation of ρ  =  0.78 between actual and predicted position across four rhesus macaques. For two of the monkeys, this movement prediction was performed in real-time to enable direct brain control of the virtual hand. Compared to physical control, neural control performance was slightly degraded; however, the monkeys were still able to successfully perform the task with an average target acquisition rate of 83.1%. The monkeys’ ability to arbitrarily specify fingertip position was also quantified using an information throughput metric. During brain control task performance, the monkeys achieved an average 1.01 bits s-1 throughput, similar to that achieved in previous studies which decoded upper-arm movements to control computer cursors using a standard Kalman filter. Significance. This is, to our knowledge, the first demonstration of brain control of finger-level fine motor skills. We believe

Sliding mode synchronization controller design with neural network for uncertain chaotic systems

Energy Technology Data Exchange (ETDEWEB)

Mou Chen [College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016 (China)], E-mail: chenmou@nuaa.edu.cn; Jiang Changsheng; Bin Jiang; Wu Qingxian [College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016 (China)

2009-02-28

A sliding mode synchronization controller is presented with RBF neural network for two chaotic systems in this paper. The compound disturbance of the synchronization error system consists of nonlinear uncertainties and exterior disturbances of chaotic systems. Based on RBF neural networks, a compound disturbance observer is proposed and the update law of parameters is given to monitor the compound disturbance. The synchronization controller is given based on the output of the compound disturbance observer. The designed controller can make the synchronization error convergent to zero and overcome the disruption of the uncertainty and the exterior disturbance of the system. Finally, an example is given to demonstrate the availability of the proposed synchronization control method.

Neural mechanisms underlying cognitive control of men with lifelong antisocial behavior.

Science.gov (United States)

Schiffer, Boris; Pawliczek, Christina; Mu Ller, Bernhard; Forsting, Michael; Gizewski, Elke; Leygraf, Norbert; Hodgins, Sheilagh

2014-04-30

Results of meta-analyses suggested subtle deficits in cognitive control among antisocial individuals. Because almost all studies focused on children with conduct problems or adult psychopaths, however, little is known about cognitive control mechanisms among the majority of persistent violent offenders who present an antisocial personality disorder (ASPD). The present study aimed to determine whether offenders with ASPD, relative to non-offenders, display dysfunction in the neural mechanisms underlying cognitive control and to assess the extent to which these dysfunctions are associated with psychopathic traits and trait impulsivity. Participants comprised 21 violent offenders and 23 non-offenders who underwent event-related functional magnetic resonance imaging while performing a non-verbal Stroop task. The offenders, relative to the non-offenders, exhibited reduced response time interference and a different pattern of conflict- and error-related activity in brain areas involved in cognitive control, attention, language, and emotion processing, that is, the anterior cingulate, dorsolateral prefrontal, superior temporal and postcentral cortices, putamen, thalamus, and amygdala. Moreover, between-group differences in behavioural and neural responses revealed associations with core features of psychopathy and attentional impulsivity. Thus, the results of the present study confirmed the hypothesis that offenders with ASPD display alterations in the neural mechanisms underlying cognitive control and that those alterations relate, at least in part, to personality characteristics. Copyright © 2014. Published by Elsevier Ireland Ltd.

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.

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.

Adaptive Gain Scheduled Semiactive Vibration Control Using a Neural Network

Directory of Open Access Journals (Sweden)

Kazuhiko Hiramoto

2018-01-01

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

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

Science.gov (United States)

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

2014-09-01

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

NL(q) Theory: A Neural Control Framework with Global Asymptotic Stability Criteria.

Science.gov (United States)

Vandewalle, Joos; De Moor, Bart L.R.; Suykens, Johan A.K.

1997-06-01

In this paper a framework for model-based neural control design is presented, consisting of nonlinear state space models and controllers, parametrized by multilayer feedforward neural networks. The models and closed-loop systems are transformed into so-called NL(q) system form. NL(q) systems represent a large class of nonlinear dynamical systems consisting of q layers with alternating linear and static nonlinear operators that satisfy a sector condition. For such NL(q)s sufficient conditions for global asymptotic stability, input/output stability (dissipativity with finite L(2)-gain) and robust stability and performance are presented. The stability criteria are expressed as linear matrix inequalities. In the analysis problem it is shown how stability of a given controller can be checked. In the synthesis problem two methods for neural control design are discussed. In the first method Narendra's dynamic backpropagation for tracking on a set of specific reference inputs is modified with an NL(q) stability constraint in order to ensure, e.g., closed-loop stability. In a second method control design is done without tracking on specific reference inputs, but based on the input/output stability criteria itself, within a standard plant framework as this is done, for example, in H( infinity ) control theory and &mgr; theory. Copyright 1997 Elsevier Science Ltd.

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.

A Low-Cost Maximum Power Point Tracking System Based on Neural Network Inverse Model Controller

Directory of Open Access Journals (Sweden)

Carlos Robles Algarín

2018-01-01

Full Text Available This work presents the design, modeling, and implementation of a neural network inverse model controller for tracking the maximum power point of a photovoltaic (PV module. A nonlinear autoregressive network with exogenous inputs (NARX was implemented in a serial-parallel architecture. The PV module mathematical modeling was developed, a buck converter was designed to operate in the continuous conduction mode with a switching frequency of 20 KHz, and the dynamic neural controller was designed using the Neural Network Toolbox from Matlab/Simulink (MathWorks, Natick, MA, USA, and it was implemented on an open-hardware Arduino Mega board. To obtain the reference signals for the NARX and determine the 65 W PV module behavior, a system made of a 0.8 W PV cell, a temperature sensor, a voltage sensor and a static neural network, was used. To evaluate performance a comparison with the P&O traditional algorithm was done in terms of response time and oscillations around the operating point. Simulation results demonstrated the superiority of neural controller over the P&O. Implementation results showed that approximately the same power is obtained with both controllers, but the P&O controller presents oscillations between 7 W and 10 W, in contrast to the inverse controller, which had oscillations between 1 W and 2 W.

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

Evolution of an artificial neural network based autonomous land vehicle controller.

Science.gov (United States)

Baluja, S

1996-01-01

This paper presents an evolutionary method for creating an artificial neural network based autonomous land vehicle controller. The evolved controllers perform better in unseen situations than those trained with an error backpropagation learning algorithm designed for this task. In this paper, an overview of the previous connectionist based approaches to this task is given, and the evolutionary algorithms used in this study are described in detail. Methods for reducing the high computational costs of training artificial neural networks with evolutionary algorithms are explored. Error metrics specific to the task of autonomous vehicle control are introduced; the evolutionary algorithms guided by these error metrics reveal improved performance over those guided by the standard sum-squared error metric. Finally, techniques for integrating evolutionary search and error backpropagation are presented. The evolved networks are designed to control Carnegie Mellon University's NAVLAB vehicles in road following tasks.

Neural mechanisms of interference control in working memory capacity.

Science.gov (United States)

Bomyea, Jessica; Taylor, Charles T; Spadoni, Andrea D; Simmons, Alan N

2018-02-01

The extent to which one can use cognitive resources to keep information in working memory is known to rely on (1) active maintenance of target representations and (2) downregulation of interference from irrelevant representations. Neurobiologically, the global capacity of working memory is thought to depend on the prefrontal and parietal cortices; however, the neural mechanisms involved in controlling interference specifically in working memory capacity tasks remain understudied. In this study, 22 healthy participants completed a modified complex working memory capacity task (Reading Span) with trials of varying levels of interference control demands while undergoing functional MRI. Neural activity associated with interference control demands was examined separately during encoding and recall phases of the task. Results suggested a widespread network of regions in the prefrontal, parietal, and occipital cortices, and the cingulate and cerebellum associated with encoding, and parietal and occipital regions associated with recall. Results align with prior findings emphasizing the importance of frontoparietal circuits for working memory performance, including the role of the inferior frontal gyrus, cingulate, occipital cortex, and cerebellum in regulation of interference demands. © 2017 Wiley Periodicals, Inc.

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.

123I-MIBG imaging detects cardiac involvement and predicts cardiac events in Churg-Strauss syndrome

International Nuclear Information System (INIS)

Horiguchi, Yoriko; Morita, Yukiko; Tsurikisawa, Naomi; Akiyama, Kazuo

2011-01-01

In Churg-Strauss syndrome (CSS) it is important to detect cardiac involvement, which predicts poor prognosis. This study evaluated whether 123 I-metaiodobenzylguanidine (MIBG) scintigraphy could detect cardiac damage and predict cardiac events in CSS. 123 I-MIBG scintigraphy was performed in 28 patients with CSS, 12 of whom had cardiac involvement. The early and delayed heart to mediastinum ratio (early H/M and delayed H/M) and washout rate were calculated by using 123 I-MIBG scintigraphy and compared with those in control subjects. Early H/M and delayed H/M were significantly lower and the washout rate was significantly higher in patients with cardiac involvement than in those without and in controls (early H/M, p = 0.0024, p = 0.0001; delayed H/M, p = 0.0002, p = 0.0001; washout rate, p = 0.0012, p = 0.0052 vs those without and vs controls, respectively). Accuracy for detecting cardiac involvement was 86% for delayed H/M and washout rate and 79% for early H/M and B-type natriuretic peptide (BNP). Kaplan-Meier analysis showed significantly lower cardiac event-free rates in patients with early H/M ≤ 2.18 and BNP > 21.8 pg/ml than those with early H/M > 2.18 and BNP ≤ 21.8 pg/ml (log-rank test p = 0.006). Cardiac sympathetic nerve function was damaged in CSS patients with cardiac involvement. 123 I-MIBG scintigraphy was useful in detecting cardiac involvement and in predicting cardiac events. (orig.)

Self-teaching neural network learns difficult reactor control problem

International Nuclear Information System (INIS)

Jouse, W.C.

1989-01-01

A self-teaching neural network used as an adaptive controller quickly learns to control an unstable reactor configuration. The network models the behavior of a human operator. It is trained by allowing it to operate the reactivity control impulsively. It is punished whenever either the power or fuel temperature stray outside technical limits. Using a simple paradigm, the network constructs an internal representation of the punishment and of the reactor system. The reactor is constrained to small power orbits

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 application, .... The partial derivative of E with respect to ele- ments of Γ, for example W, ... Ki = any non-negative value. Figure 7: Neural Network ...

A randomized controlled trial of stage-matched intervention for smoking cessation in cardiac out-patients.

Science.gov (United States)

Chan, Sophia S C; Leung, Doris Y P; Wong, David C N; Lau, Chu-Pak; Wong, Vivian T; Lam, Tai-Hing

2012-04-01

To examine the effectiveness of a stage-matched smoking cessation counselling intervention for smokers who had cardiac diseases. A total of 1860 Chinese cardiac patients who smoked at least one cigarette in the past 7 days and aged 18 years or above recruited from cardiac out-patient clinics in Hong Kong hospitals were allocated randomly to an intervention group or control group. The intervention group (n = 938) received counselling matched with their stage of readiness to quit by trained counsellors at baseline, 1 week and 1 month. The control group (n = 922) received brief counselling on healthy diet at baseline. The primary outcomes were self-reported 7-day and 30-day point prevalence (PP) of tobacco abstinence at 12 months after baseline. The secondary outcome measures included biochemically validated abstinence at 12-month follow-up, self-reported 7-day and 30-day PP abstinence and reduction of cigarette consumption by 50% at 3 and 6 months. By intention-to-treat analysis, the intervention and control groups showed no significant difference in self-reported 7-day PP abstinence (intervention: 26.5% versus control: 25.5%; P = 0.60) and 30-day PP (intervention: 25.4% versus control: 24.2%; P = 0.55), biochemically validated abstinence (intervention: 6.6% versus control: 4.9%; P = 0.14) and overall quit attempts of least 24 hours (intervention: 40.3% versus control: 34.3%; P = 0.007) at the 12-month follow-up, adjusted for the baseline stage of readiness to quit smoking. An intervention, based on the Stages of Change model, to promote smoking cessation in cardiac patients in China failed to find any long-term benefit. © 2011 The Authors, Addiction © 2011 Society for the Study of Addiction.

Bioelectrochemical control of neural cell development on conducting polymers.

Science.gov (United States)

Collazos-Castro, Jorge E; Polo, José L; Hernández-Labrado, Gabriel R; Padial-Cañete, Vanesa; García-Rama, Concepción

2010-12-01

Electrically conducting polymers hold promise for developing advanced neuroprostheses, bionic systems and neural repair devices. Among them, poly(3, 4-ethylenedioxythiophene) doped with polystyrene sulfonate (PEDOT:PSS) exhibits superior physicochemical properties but biocompatibility issues have limited its use. We describe combinations of electrochemical and molecule self-assembling methods to consistently control neural cell development on PEDOT:PSS while maintaining very low interfacial impedance. Electro-adsorbed polylysine enabled long-term neuronal survival and growth on the nanostructured polymer. Neurite extension was strongly inhibited by an additional layer of PSS or heparin, which in turn could be either removed electrically or further coated with spermine to activate cell growth. Binding basic fibroblast growth factor (bFGF) to the heparin layer inhibited neurons but promoted proliferation and migration of precursor cells. This methodology may orchestrate neural cell behavior on electroactive polymers, thus improving cell/electrode communication in prosthetic devices and providing a platform for tissue repair strategies. Copyright © 2010 Elsevier Ltd. All rights reserved.

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.

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

Science.gov (United States)

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

2015-07-01

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

Stochastic synchronization of coupled neural networks with intermittent control

International Nuclear Information System (INIS)

Yang Xinsong; Cao Jinde

2009-01-01

In this Letter, we study the exponential stochastic synchronization problem for coupled neural networks with stochastic noise perturbations. Based on Lyapunov stability theory, inequality techniques, the properties of Weiner process, and adding different intermittent controllers, several sufficient conditions are obtained to ensure exponential stochastic synchronization of coupled neural networks with or without coupling delays under stochastic perturbations. These stochastic synchronization criteria are expressed in terms of several lower-dimensional linear matrix inequalities (LMIs) and can be easily verified. Moreover, the results of this Letter are applicable to both directed and undirected weighted networks. A numerical example and its simulations are offered to show the effectiveness of our new results.

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

Directory of Open Access Journals (Sweden)

Xin Zhang

2014-01-01

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

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.

Aerobic exercise training rescues cardiac protein quality control and blunts endoplasmic reticulum stress in heart failure rats.

Science.gov (United States)

Bozi, Luiz H M; Jannig, Paulo R; Rolim, Natale; Voltarelli, Vanessa A; Dourado, Paulo M M; Wisløff, Ulrik; Brum, Patricia C

2016-11-01

Cardiac endoplasmic reticulum (ER) stress through accumulation of misfolded proteins plays a pivotal role in cardiovascular diseases. In an attempt to reestablish ER homoeostasis, the unfolded protein response (UPR) is activated. However, if ER stress persists, sustained UPR activation leads to apoptosis. There is no available therapy for ER stress relief. Considering that aerobic exercise training (AET) attenuates oxidative stress, mitochondrial dysfunction and calcium imbalance, it may be a potential strategy to reestablish cardiac ER homoeostasis. We test the hypothesis that AET would attenuate impaired cardiac ER stress after myocardial infarction (MI). Wistar rats underwent to either MI or sham surgeries. Four weeks later, rats underwent to 8 weeks of moderate-intensity AET. Myocardial infarction rats displayed cardiac dysfunction and lung oedema, suggesting heart failure. Cardiac dysfunction in MI rats was paralleled by increased protein levels of UPR markers (GRP78, DERLIN-1 and CHOP), accumulation of misfolded and polyubiquitinated proteins, and reduced chymotrypsin-like proteasome activity. These results suggest an impaired cardiac protein quality control. Aerobic exercise training improved exercise capacity and cardiac function of MI animals. Interestingly, AET blunted MI-induced ER stress by reducing protein levels of UPR markers, and accumulation of both misfolded and polyubiquinated proteins, which was associated with restored proteasome activity. Taken together, our study provide evidence for AET attenuation of ER stress through the reestablishment of cardiac protein quality control, which contributes to better cardiac function in post-MI heart failure rats. These results reinforce the importance of AET as primary non-pharmacological therapy to cardiovascular disease. © 2016 The Authors. Journal of Cellular and Molecular Medicine published by John Wiley & Sons Ltd and Foundation for Cellular and Molecular Medicine.

Adaptive neural network controller for the molten steel level control of strip casting processes

International Nuclear Information System (INIS)

Chen, Hung Yi; Huang, Shiuh Jer

2010-01-01

The twin-roll strip casting process is a steel-strip production method which combines continuous casting and hot rolling processes. The production line from molten liquid steel to the final steel-strip is shortened and the production cost is reduced significantly as compared to conventional continuous casting. The quality of strip casting process depends on many process parameters, such as molten steel level in the pool, solidification position, and roll gap. Their relationships are complex and the strip casting process has the properties of nonlinear uncertainty and time-varying characteristics. It is difficult to establish an accurate process model for designing a model-based controller to monitor the strip quality. In this paper, a model-free adaptive neural network controller is developed to overcome this problem. The proposed control strategy is based on a neural network structure combined with a sliding-mode control scheme. An adaptive rule is employed to on-line adjust the weights of radial basis functions by using the reaching condition of a specified sliding surface. This surface has the on-line learning ability to respond to the system's nonlinear and time-varying behaviors. Since this model-free controller has a simple control structure and small number of control parameters, it is easy to implement. Simulation results, based on a semi experimental system dynamic model and parameters, are executed to show the control performance of the proposed intelligent controller. In addition, the control performance is compared with that of a traditional Pid controller

Chaos control of the brushless direct current motor using adaptive dynamic surface control based on neural network with the minimum weights

International Nuclear Information System (INIS)

Luo, Shaohua; Wu, Songli; Gao, Ruizhen

2015-01-01

This paper investigates chaos control for the brushless DC motor (BLDCM) system by adaptive dynamic surface approach based on neural network with the minimum weights. The BLDCM system contains parameter perturbation, chaotic behavior, and uncertainty. With the help of radial basis function (RBF) neural network to approximate the unknown nonlinear functions, the adaptive law is established to overcome uncertainty of the control gain. By introducing the RBF neural network and adaptive technology into the dynamic surface control design, a robust chaos control scheme is developed. It is proved that the proposed control approach can guarantee that all signals in the closed-loop system are globally uniformly bounded, and the tracking error converges to a small neighborhood of the origin. Simulation results are provided to show that the proposed approach works well in suppressing chaos and parameter perturbation

Chaos control of the brushless direct current motor using adaptive dynamic surface control based on neural network with the minimum weights.

Science.gov (United States)

Luo, Shaohua; Wu, Songli; Gao, Ruizhen

2015-07-01

This paper investigates chaos control for the brushless DC motor (BLDCM) system by adaptive dynamic surface approach based on neural network with the minimum weights. The BLDCM system contains parameter perturbation, chaotic behavior, and uncertainty. With the help of radial basis function (RBF) neural network to approximate the unknown nonlinear functions, the adaptive law is established to overcome uncertainty of the control gain. By introducing the RBF neural network and adaptive technology into the dynamic surface control design, a robust chaos control scheme is developed. It is proved that the proposed control approach can guarantee that all signals in the closed-loop system are globally uniformly bounded, and the tracking error converges to a small neighborhood of the origin. Simulation results are provided to show that the proposed approach works well in suppressing chaos and parameter perturbation.

Fault detection and diagnosis using statistical control charts and artificial neural networks

International Nuclear Information System (INIS)

Leger, R.P.; Garland, W.J.; Poehlman, W.F.S.

1995-01-01

In order to operate a successful plant or process, continuous improvement must be made in the areas of safety, quality and reliability. Central to this continuous improvement is the early or proactive detection and correct diagnosis of process faults. This research examines the feasibility of using Cumulative Summation (CUSUM) Control Charts and artificial neural networks together for fault detection and diagnosis (FDD). The proposed FDD strategy was tested on a model of the heat transport system of a CANDU nuclear reactor. The results of the investigation indicate that a FDD system using CUSUM Control Charts and a Radial Basis Function (RBF) neural network is not only feasible but also of promising potential. The control charts and neural network are linked together by using a characteristic fault signature pattern for each fault which is to be detected and diagnosed. When tested, the system was able to eliminate all false alarms at steady state, promptly detect 6 fault conditions and correctly diagnose 5 out of the 6 faults. The diagnosis for the sixth fault was inconclusive. (author). 9 refs., 6 tabs., 7 figs

Endogenous Natural Complement Inhibitor Regulates Cardiac Development

DEFF Research Database (Denmark)

Mortensen, Simon A; Skov, Louise L; Kjaer-Sorensen, Kasper

2017-01-01

mechanisms during fetal development and adult homeostasis. In this article, we describe the function of an endogenous complement inhibitor, mannan-binding lectin (MBL)-associated protein (MAp)44, in regulating the composition of a serine protease-pattern recognition receptor complex, MBL-associated serine...... of MAp44 caused impaired cardiogenesis, lowered heart rate, and decreased cardiac output. These defects were associated with aberrant neural crest cell behavior. We found that MAp44 competed with MASP-3 for pattern recognition molecule interaction, and knockdown of endogenous MAp44 expression could...... be rescued by overexpression of wild-type MAp44. Our observations provide evidence that immune molecules are centrally involved in the orchestration of cardiac tissue development....

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

DEFF Research Database (Denmark)

Bhowmik, Subrata

2011-01-01

This paper presents a neural network based semi-active control method for a rotary type magnetorheological (MR) damper. The characteristics of the MR damper are described by the classic Bouc-Wen model, and the performance of the proposed control method is evaluated in terms of a base exited shear...... to determine the damper current based on the derived optimal damper force. For that reason an inverse MR damper model is also designed based on the neural network identification of the particular rotary MR damper. The performance of the proposed controller is compared to that of an optimal pure viscous damper...

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.

Neural-net based coordinated stabilizing control for the exciter and governor loops of low head hydropower plants

Energy Technology Data Exchange (ETDEWEB)

Djukanovic, M.; Novicevic, M.; Dobrijevic, D.; Babic, B. [Electrical Engineering Inst. Nikola Tesla, Belgrade (Yugoslavia); Sobajic, D.J. [Electric Power Research Inst., Palo Alto, CA (United States); Pao, Y.H. [Case Western Reserve Univ., Cleveland, OH (United States)]|[AI WARE, Inc., Cleveland, OH (United States)

1995-12-01

This paper presents a design technique of a new adaptive optimal controller of the low head hydropower plant using artificial neural networks (ANN). The adaptive controller is to operate in real time to improve the generating unit transients through the exciter input, the guide vane position and the runner blade position. The new design procedure is based on self-organization and the predictive estimation capabilities of neural-nets implemented through the cluster-wise segmented associative memory scheme. The developed neural-net based controller (NNC) whose control signals are adjusted using the on-line measurements, can offer better damping effects for generator oscillations over a wide range of operating conditions than conventional controllers. Digital simulations of hydropower plant equipped with low head Kaplan turbine are performed and the comparisons of conventional excitation-governor control, state-space optimal control and neural-net based control are presented. Results obtained on the non-linear mathematical model demonstrate that the effects of the NNC closely agree with those obtained using the state-space multivariable discrete-time optimal controllers.

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

Directory of Open Access Journals (Sweden)

Carlos Villaseñor

2017-12-01

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

A Novel Recurrent Neural Network for Manipulator Control With Improved Noise Tolerance.

Science.gov (United States)

Li, Shuai; Wang, Huanqing; Rafique, Muhammad Usman

2017-04-12

In this paper, we propose a novel recurrent neural network to resolve the redundancy of manipulators for efficient kinematic control in the presence of noises in a polynomial type. Leveraging the high-order derivative properties of polynomial noises, a deliberately devised neural network is proposed to eliminate the impact of noises and recover the accurate tracking of desired trajectories in workspace. Rigorous analysis shows that the proposed neural law stabilizes the system dynamics and the position tracking error converges to zero in the presence of noises. Extensive simulations verify the theoretical results. Numerical comparisons show that existing dual neural solutions lose stability when exposed to large constant noises or time-varying noises. In contrast, the proposed approach works well and has a low tracking error comparable to noise-free situations.

Identification and control of plasma vertical position using neural network in Damavand tokamak

International Nuclear Information System (INIS)

Rasouli, H.; Rasouli, C.; Koohi, A.

2013-01-01

In this work, a nonlinear model is introduced to determine the vertical position of the plasma column in Damavand tokamak. Using this model as a simulator, a nonlinear neural network controller has been designed. In the first stage, the electronic drive and sensory circuits of Damavand tokamak are modified. These circuits can control the vertical position of the plasma column inside the vacuum vessel. Since the vertical position of plasma is an unstable parameter, a direct closed loop system identification algorithm is performed. In the second stage, a nonlinear model is identified for plasma vertical position, based on the multilayer perceptron (MLP) neural network (NN) structure. Estimation of simulator parameters has been performed by back-propagation error algorithm using Levenberg–Marquardt gradient descent optimization technique. The model is verified through simulation of the whole closed loop system using both simulator and actual plant in similar conditions. As the final stage, a MLP neural network controller is designed for simulator model. In the last step, online training is performed to tune the controller parameters. Simulation results justify using of the NN controller for the actual plant.

Identification and control of plasma vertical position using neural network in Damavand tokamak

Energy Technology Data Exchange (ETDEWEB)

Rasouli, H. [School of Plasma Physics and Nuclear Fusion, Institute of Nuclear Science and Technology, AEOI, P.O. Box 14155-1339, Tehran (Iran, Islamic Republic of); Advanced Process Automation and Control (APAC) Research Group, Faculty of Electrical Engineering, K.N. Toosi University of Technology, P.O. Box 16315-1355, Tehran (Iran, Islamic Republic of); Rasouli, C.; Koohi, A. [School of Plasma Physics and Nuclear Fusion, Institute of Nuclear Science and Technology, AEOI, P.O. Box 14155-1339, Tehran (Iran, Islamic Republic of)

2013-02-15

In this work, a nonlinear model is introduced to determine the vertical position of the plasma column in Damavand tokamak. Using this model as a simulator, a nonlinear neural network controller has been designed. In the first stage, the electronic drive and sensory circuits of Damavand tokamak are modified. These circuits can control the vertical position of the plasma column inside the vacuum vessel. Since the vertical position of plasma is an unstable parameter, a direct closed loop system identification algorithm is performed. In the second stage, a nonlinear model is identified for plasma vertical position, based on the multilayer perceptron (MLP) neural network (NN) structure. Estimation of simulator parameters has been performed by back-propagation error algorithm using Levenberg-Marquardt gradient descent optimization technique. The model is verified through simulation of the whole closed loop system using both simulator and actual plant in similar conditions. As the final stage, a MLP neural network controller is designed for simulator model. In the last step, online training is performed to tune the controller parameters. Simulation results justify using of the NN controller for the actual plant.

A study on neural network representation of reactor power control procedures

International Nuclear Information System (INIS)

Moon, Byung Soo; Park, J. C.; Kim, Y. T.; Yang, S. U.; Lee, H. C.; Hwang, I. A.; Hwang, H. S.

1997-12-01

A neural algorithm to carry out the curve readings and arithmetic computations necessary for reactor power control is described in this report. The curve readings are for functions of the form z=f(x,y) and require fairly good interpolations. One of the functions is the total power defect as a function of reactor power and boron concentration. The second is the new position of control rod as a function of the current rod position and the increment of total power defect needed for the required power change. The curves involving xenon effect are also considered separately. We represented these curves by cubic spline interpolations first and then converted them to fuzzy systems so that they perform the identical interpolations as the splines. The resulting fuzzy systems are then converted to artificial neural networks similar to the RBF type neural network. These networks still carry the O(h'4) accuracy as the cubic spline interpolating functions. Also included is a description of an important result on how to find the spline interpolation coefficients without solving the matrix equation, when the function is a polynomial of the form f(t)=t'm. This result provides a systematic way of presenting continuous functions by fuzzy systems and hence by artificial neural networks without any training. (author). 10 refs., 2 tabs., 10 figs

Adaptive PID control based on orthogonal endocrine neural networks.

Science.gov (United States)

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

2016-12-01

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

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

Directory of Open Access Journals (Sweden)

Min Wang

2017-01-01

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

Cardiac Auscultation Using Smartphones: Pilot Study.

Science.gov (United States)

Kang, Si-Hyuck; Joe, Byunggill; Yoon, Yeonyee; Cho, Goo-Yeong; Shin, Insik; Suh, Jung-Won

2018-02-28

Cardiac auscultation is a cost-effective, noninvasive screening tool that can provide information about cardiovascular hemodynamics and disease. However, with advances in imaging and laboratory tests, the importance of cardiac auscultation is less appreciated in clinical practice. The widespread use of smartphones provides opportunities for nonmedical expert users to perform self-examination before hospital visits. The objective of our study was to assess the feasibility of cardiac auscultation using smartphones with no add-on devices for use at the prehospital stage. We performed a pilot study of patients with normal and pathologic heart sounds. Heart sounds were recorded on the skin of the chest wall using 3 smartphones: the Samsung Galaxy S5 and Galaxy S6, and the LG G3. Recorded heart sounds were processed and classified by a diagnostic algorithm using convolutional neural networks. We assessed diagnostic accuracy, as well as sensitivity, specificity, and predictive values. A total of 46 participants underwent heart sound recording. After audio file processing, 30 of 46 (65%) heart sounds were proven interpretable. Atrial fibrillation and diastolic murmur were significantly associated with failure to acquire interpretable heart sounds. The diagnostic algorithm classified the heart sounds into the correct category with high accuracy: Galaxy S5, 90% (95% CI 73%-98%); Galaxy S6, 87% (95% CI 69%-96%); and LG G3, 90% (95% CI 73%-98%). Sensitivity, specificity, positive predictive value, and negative predictive value were also acceptable for the 3 devices. Cardiac auscultation using smartphones was feasible. Discrimination using convolutional neural networks yielded high diagnostic accuracy. However, using the built-in microphones alone, the acquisition of reproducible and interpretable heart sounds was still a major challenge. ClinicalTrials.gov NCT03273803; https://clinicaltrials.gov/ct2/show/NCT03273803 (Archived by WebCite at http://www.webcitation.org/6x6g1f

Towards practical control design using neural computation

Science.gov (United States)

Troudet, Terry; Garg, Sanjay; Mattern, Duane; Merrill, Walter

1991-01-01

The objective is to develop neural network based control design techniques which address the issue of performance/control effort tradeoff. Additionally, the control design needs to address the important issue if achieving adequate performance in the presence of actuator nonlinearities such as position and rate limits. These issues are discussed using the example of aircraft flight control. Given a set of pilot input commands, a feedforward net is trained to control the vehicle within the constraints imposed by the actuators. This is achieved by minimizing an objective function which is the sum of the tracking errors, control input rates and control input deflections. A tradeoff between tracking performance and control smoothness is obtained by varying, adaptively, the weights of the objective function. The neurocontroller performance is evaluated in the presence of actuator dynamics using a simulation of the vehicle. Appropriate selection of the different weights in the objective function resulted in the good tracking of the pilot commands and smooth neurocontrol. An extension of the neurocontroller design approach is proposed to enhance its practicality.

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.

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

Directory of Open Access Journals (Sweden)

Abbas Ajorkar

2015-04-01

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

Control of 12-Cylinder Camless Engine with Neural Networks

Directory of Open Access Journals (Sweden)

Ashhab Moh’d Sami

2017-01-01

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

{sup 123}I-MIBG imaging detects cardiac involvement and predicts cardiac events in Churg-Strauss syndrome

Energy Technology Data Exchange (ETDEWEB)

Horiguchi, Yoriko; Morita, Yukiko [National Hospital Organization Sagamihara National Hospital, Department of Cardiology, Sagamihara City, Kanagawa (Japan); Tsurikisawa, Naomi; Akiyama, Kazuo [National Hospital Organization Sagamihara National Hospital, Clinical Research Centre for Allergy and Rheumatology, Sagamihara City, Kanagawa (Japan)

2011-02-15

In Churg-Strauss syndrome (CSS) it is important to detect cardiac involvement, which predicts poor prognosis. This study evaluated whether {sup 123}I-metaiodobenzylguanidine (MIBG) scintigraphy could detect cardiac damage and predict cardiac events in CSS. {sup 123}I-MIBG scintigraphy was performed in 28 patients with CSS, 12 of whom had cardiac involvement. The early and delayed heart to mediastinum ratio (early H/M and delayed H/M) and washout rate were calculated by using {sup 123}I-MIBG scintigraphy and compared with those in control subjects. Early H/M and delayed H/M were significantly lower and the washout rate was significantly higher in patients with cardiac involvement than in those without and in controls (early H/M, p = 0.0024, p = 0.0001; delayed H/M, p = 0.0002, p = 0.0001; washout rate, p = 0.0012, p = 0.0052 vs those without and vs controls, respectively). Accuracy for detecting cardiac involvement was 86% for delayed H/M and washout rate and 79% for early H/M and B-type natriuretic peptide (BNP). Kaplan-Meier analysis showed significantly lower cardiac event-free rates in patients with early H/M {<=} 2.18 and BNP > 21.8 pg/ml than those with early H/M > 2.18 and BNP {<=} 21.8 pg/ml (log-rank test p = 0.006). Cardiac sympathetic nerve function was damaged in CSS patients with cardiac involvement. {sup 123}I-MIBG scintigraphy was useful in detecting cardiac involvement and in predicting cardiac events. (orig.)

Proteostasis in cardiac health and disease.

Science.gov (United States)

Henning, Robert H; Brundel, Bianca J J M

2017-11-01

The incidence and prevalence of cardiac diseases, which are the main cause of death worldwide, are likely to increase because of population ageing. Prevailing theories about the mechanisms of ageing feature the gradual derailment of cellular protein homeostasis (proteostasis) and loss of protein quality control as central factors. In the heart, loss of protein patency, owing to flaws in genetically-determined design or because of environmentally-induced 'wear and tear', can overwhelm protein quality control, thereby triggering derailment of proteostasis and contributing to cardiac ageing. Failure of protein quality control involves impairment of chaperones, ubiquitin-proteosomal systems, autophagy, and loss of sarcomeric and cytoskeletal proteins, all of which relate to induction of cardiomyocyte senescence. Targeting protein quality control to maintain cardiac proteostasis offers a novel therapeutic strategy to promote cardiac health and combat cardiac disease. Currently marketed drugs are available to explore this concept in the clinical setting.

Master-slave exponential synchronization of delayed complex-valued memristor-based neural networks via impulsive control.

Science.gov (United States)

Li, Xiaofan; Fang, Jian-An; Li, Huiyuan

2017-09-01

This paper investigates master-slave exponential synchronization for a class of complex-valued memristor-based neural networks with time-varying delays via discontinuous impulsive control. Firstly, the master and slave complex-valued memristor-based neural networks with time-varying delays are translated to two real-valued memristor-based neural networks. Secondly, an impulsive control law is constructed and utilized to guarantee master-slave exponential synchronization of the neural networks. Thirdly, the master-slave synchronization problems are transformed into the stability problems of the master-slave error system. By employing linear matrix inequality (LMI) technique and constructing an appropriate Lyapunov-Krasovskii functional, some sufficient synchronization criteria are derived. Finally, a numerical simulation is provided to illustrate the effectiveness of the obtained theoretical results. Copyright © 2017 Elsevier Ltd. All rights reserved.

An application of neural networks to process and materials control

International Nuclear Information System (INIS)

Howell, J.A.; Whiteson, R.

1991-01-01

Process control consists of two basic elements: a model of the process and knowledge of the desired control algorithm. In some cases the level of the control algorithm is merely supervisory, as in an alarm-reporting or anomaly-detection system. If the model of the process is known, then a set of equations may often be solved explicitly to provide the control algorithm. Otherwise, the model has to be discovered through empirical studies. Neural networks have properties that make them useful in this application. They can learn (make internal models from experience or observations). The problem of anomaly detection in materials control systems fits well into this general control framework. To successfully model a process with a neutral network, a good set of observables must be chosen. These observables must in some sense adequately span the space of representable events, so that a signature metric can be built for normal operation. In this way, a non-normal event, one that does not fit within the signature, can be detected. In this paper, we discuss the issues involved in applying a neural network model to anomaly detection in materials control systems. These issues include data selection and representation, network architecture, prediction of events, the use of simulated data, and software tools. 10 refs., 4 figs., 1 tab

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

OpenAIRE

Tao, Yong; Zheng, Jiaqi; Lin, Yuanchang

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...

Neural network-based run-to-run controller using exposure and resist thickness adjustment

Science.gov (United States)

Geary, Shane; Barry, Ronan

2003-06-01

This paper describes the development of a run-to-run control algorithm using a feedforward neural network, trained using the backpropagation training method. The algorithm is used to predict the critical dimension of the next lot using previous lot information. It is compared to a common prediction algorithm - the exponentially weighted moving average (EWMA) and is shown to give superior prediction performance in simulations. The manufacturing implementation of the final neural network showed significantly improved process capability when compared to the case where no run-to-run control was utilised.

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.

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.

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.

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.

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

Current state of cardiac rehabilitation in Germany: patient characteristics, risk factor management and control status, by education level.

Science.gov (United States)

Bestehorn, Kurt; Jannowitz, Christina; Horack, Martin; Karmann, Barbara; Halle, Martin; Völler, Heinz

2011-01-01

After the acute hospital stay, most cardiac patients in Germany are transferred for a 3-4-week period of inpatient cardiac rehabilitation. We aim to describe patient characteristics and risk factor management of cardiac rehabilitation patients with a focus on drug treatment and control status, differentiated by education level (low level, elementary school; intermediate level, secondary modern school; high level, grammar school/university). Data covering a time period between 2003 and 2008 from 68,191 hospitalized patients in cardiac rehabilitation from a large-scale registry (Transparency Registry to Objectify Guideline- Oriented Risk Factor Management) were analyzed descriptively. Further, a multivariate model was applied to assess factors associated with good control of risk factors. In the total cohort, patients with a manifestation of coronary artery disease (mean age 63.7 years, males 71.7%) were referred to cardiac rehabilitation after having received percutaneous coronary intervention (51.6%) or coronary bypass surgery (39.5%). Statin therapy increased from 76.3% at entry to 88.9% at discharge, and low density lipoprotein cholesterol education. In contrast with patients having high education, those with low education had more diabetes, hypertension, and peripheral arterial disease, had lower exercise capacity, and received less treatment with statins and guideline-orientated therapy in general. In the multivariate model, good control was significantly more likely in men (odds ratio 1.38; 95% confidence interval 1.30-1.46), less likely in patients of higher age (0.99; 0.99-0.99), with diabetes (0.90; 0.85-0.95), or peripheral arterial disease (0.88; 0.82-0.95). Compared with a low level education, a mid level education was associated with poor control (0.94; 0.89-0.99), while high education did not have a significant effect (1.08; 0.99-1.17). Patients with different levels of education treated in cardiac rehabilitation did not differ relevantly in terms of

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

Science.gov (United States)

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

2015-02-01

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

Cardiac expression of microsomal triglyceride transfer protein is increased in obesity and serves to attenuate cardiac triglyceride accumulation.

Directory of Open Access Journals (Sweden)

Emil D Bartels

Full Text Available Obesity causes lipid accumulation in the heart and may lead to lipotoxic heart disease. Traditionally, the size of the cardiac triglyceride pool is thought to reflect the balance between uptake and beta-oxidation of fatty acids. However, triglycerides can also be exported from cardiomyocytes via secretion of apolipoproteinB-containing (apoB lipoproteins. Lipoprotein formation depends on expression of microsomal triglyceride transfer protein (MTP; the mouse expresses two isoforms of MTP, A and B. Since many aspects of the link between obesity-induced cardiac disease and cardiac lipid metabolism remain unknown, we investigated how cardiac lipoprotein synthesis affects cardiac expression of triglyceride metabolism-controlling genes, insulin sensitivity, and function in obese mice. Heart-specific ablation of MTP-A in mice using Cre-loxP technology impaired upregulation of MTP expression in response to increased fatty acid availability during fasting and fat feeding. This resulted in cardiac triglyceride accumulation but unaffected cardiac insulin-stimulated glucose uptake. Long-term fat-feeding of male C57Bl/6 mice increased cardiac triglycerides, induced cardiac expression of triglyceride metabolism-controlling genes and attenuated heart function. Abolishing cardiac triglyceride accumulation in fat-fed mice by overexpression of an apoB transgene in the heart prevented the induction of triglyceride metabolism-controlling genes and improved heart function. The results suggest that in obesity, the physiological increase of cardiac MTP expression serves to attenuate cardiac triglyceride accumulation albeit without major effects on cardiac insulin sensitivity. Nevertheless, the data suggest that genetically increased lipoprotein secretion prevents development of obesity-induced lipotoxic heart disease.

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.

Artificial neural networks for control of a grid-connected rectifier/inverter under disturbance, dynamic and power converter switching conditions.

Science.gov (United States)

Li, Shuhui; Fairbank, Michael; Johnson, Cameron; Wunsch, Donald C; Alonso, Eduardo; Proaño, Julio L

2014-04-01

Three-phase grid-connected converters are widely used in renewable and electric power system applications. Traditionally, grid-connected converters are controlled with standard decoupled d-q vector control mechanisms. However, recent studies indicate that such mechanisms show limitations in their applicability to dynamic systems. This paper investigates how to mitigate such restrictions using a neural network to control a grid-connected rectifier/inverter. The neural network implements a dynamic programming algorithm and is trained by using back-propagation through time. To enhance performance and stability under disturbance, additional strategies are adopted, including the use of integrals of error signals to the network inputs and the introduction of grid disturbance voltage to the outputs of a well-trained network. The performance of the neural-network controller is studied under typical vector control conditions and compared against conventional vector control methods, which demonstrates that the neural vector control strategy proposed in this paper is effective. Even in dynamic and power converter switching environments, the neural vector controller shows strong ability to trace rapidly changing reference commands, tolerate system disturbances, and satisfy control requirements for a faulted power system.

Neural-Network-Based Fuzzy Logic Navigation Control for Intelligent Vehicles

Directory of Open Access Journals (Sweden)

Ahcene Farah

2002-06-01

Full Text Available This paper proposes a Neural-Network-Based Fuzzy logic system for navigation control of intelligent vehicles. First, the use of Neural Networks and Fuzzy Logic to provide intelligent vehicles  with more autonomy and intelligence is discussed. Second, the system  for the obstacle avoidance behavior is developed. Fuzzy Logic improves Neural Networks (NN obstacle avoidance approach by handling imprecision and rule-based approximate reasoning. This system must make the vehicle able, after supervised learning, to achieve two tasks: 1- to make one’s way towards its target by a NN, and 2- to avoid static or dynamic obstacles by a Fuzzy NN capturing the behavior of a human expert. Afterwards, two association phases between each task and the appropriate actions are carried out by Trial and Error learning and their coordination allows to decide the appropriate action. Finally, the simulation results display the generalization and adaptation abilities of the system by testing it in new unexplored environments.

Prospective randomized controlled intervention trial: Comprehensive Yogic Breathing Improves Cardiac autonomic functions and Quality of life in Diabetes

Directory of Open Access Journals (Sweden)

V P Jyotsna

2012-01-01

Full Text Available Aims and Objectives: To assess the effect of Comprehensive Yogic Breathing Program on glycemic control, quality of life, and cardiac autonomic functions in diabetes. Material and Methods: This is a prospective randomized controlled intervention trial. Cardiac autonomic functions were assessed in 120 diabetics. Patients were randomized into two groups, one group receiving standard therapy for diabetes (n = 56 and the other group receiving standard therapy for diabetes and comprehensive yogic breathing program (n = 64. Standard therapy included advice on diet, walk, and oral antidiabetic drugs. Comprehensive yogic breathing program was an interactive session in which Sudarshan kriya yoga, a rhythmic cyclical breathing, preceded by Pranayam was taught under guidance of a certified teacher. Change in fasting, post prandial blood sugars, glycated hemoglobin, and quality of life were assessed. Cardiac autonomic function tests were done before and six months after intervention. Results: There was significant improvement in psychological (P = 0.006 and social domains (P = 0.04 and total quality of life (P = 0.02 in the group practicing comprehensive yogic breathing program as compared to the group following standard therapy alone. In the group following breathing program, the improvement in sympathetic cardiac autonomic functions was statistically significant (P = 0.01, while the change in the standard group was not significant (P = 0.17. When both parasympathetic and sympathetic cardiac autonomic functions were considered, there was a trend toward improvement in patients following comprehensive yogic breathing program (P = 0.07. In the standard therapy group, no change in cardiac autonomic functions was noted (P = 0.76. The parameters of glycemic control were comparable in both groups. Conclusion: There was significant improvement in quality of life and cardiac autonomic functions in the diabetes patients practicing comprehensive yogic breathing

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…

Global exponential synchronization of inertial memristive neural networks with time-varying delay via nonlinear controller.

Science.gov (United States)

Gong, Shuqing; Yang, Shaofu; Guo, Zhenyuan; Huang, Tingwen

2018-06-01

The paper is concerned with the synchronization problem of inertial memristive neural networks with time-varying delay. First, by choosing a proper variable substitution, inertial memristive neural networks described by second-order differential equations can be transformed into first-order differential equations. Then, a novel controller with a linear diffusive term and discontinuous sign term is designed. By using the controller, the sufficient conditions for assuring the global exponential synchronization of the derive and response neural networks are derived based on Lyapunov stability theory and some inequality techniques. Finally, several numerical simulations are provided to substantiate the effectiveness of the theoretical results. Copyright © 2018 Elsevier Ltd. All rights reserved.

Information content of neural networks with self-control and variable activity

International Nuclear Information System (INIS)

Bolle, D.; Amari, S.I.; Dominguez Carreta, D.R.C.; Massolo, G.

2001-01-01

A self-control mechanism for the dynamics of neural networks with variable activity is discussed using a recursive scheme for the time evolution of the local field. It is based upon the introduction of a self-adapting time-dependent threshold as a function of both the neural and pattern activity in the network. This mechanism leads to an improvement of the information content of the network as well as an increase of the storage capacity and the basins of attraction. Different architectures are considered and the results are compared with numerical simulations

A Lateral Control Method of Intelligent Vehicle Based on Fuzzy Neural Network

Directory of Open Access Journals (Sweden)

Linhui Li

2015-01-01

Full Text Available A lateral control method is proposed for intelligent vehicle to track the desired trajectory. Firstly, a lateral control model is established based on the visual preview and dynamic characteristics of intelligent vehicle. Then, the lateral error and orientation error are melded into an integrated error. Considering the system parameter perturbation and the external interference, a sliding model control is introduced in this paper. In order to design a sliding surface, the integrated error is chosen as the parameter of the sliding mode switching function. The sliding mode switching function and its derivative are selected as two inputs of the controller, and the front wheel angle is selected as the output. Next, a fuzzy neural network is established, and the self-learning functions of neural network is utilized to construct the fuzzy rules. Finally, the simulation results demonstrate the effectiveness and robustness of the proposed method.

A randomized controlled trial of levosimendan to reduce mortality in high-risk cardiac surgery patients (CHEETAH): Rationale and design.

Science.gov (United States)

Zangrillo, Alberto; Alvaro, Gabriele; Pisano, Antonio; Guarracino, Fabio; Lobreglio, Rosetta; Bradic, Nikola; Lembo, Rosalba; Gianni, Stefano; Calabrò, Maria Grazia; Likhvantsev, Valery; Grigoryev, Evgeny; Buscaglia, Giuseppe; Pala, Giovanni; Auci, Elisabetta; Amantea, Bruno; Monaco, Fabrizio; De Vuono, Giovanni; Corcione, Antonio; Galdieri, Nicola; Cariello, Claudia; Bove, Tiziana; Fominskiy, Evgeny; Auriemma, Stefano; Baiocchi, Massimo; Bianchi, Alessandro; Frontini, Mario; Paternoster, Gianluca; Sangalli, Fabio; Wang, Chew-Yin; Zucchetti, Maria Chiara; Biondi-Zoccai, Giuseppe; Gemma, Marco; Lipinski, Michael J; Lomivorotov, Vladimir V; Landoni, Giovanni

2016-07-01

Patients undergoing cardiac surgery are at risk of perioperative low cardiac output syndrome due to postoperative myocardial dysfunction. Myocardial dysfunction in patients undergoing cardiac surgery is a potential indication for the use of levosimendan, a calcium sensitizer with 3 beneficial cardiovascular effects (inotropic, vasodilatory, and anti-inflammatory), which appears effective in improving clinically relevant outcomes. Double-blind, placebo-controlled, multicenter randomized trial. Tertiary care hospitals. Cardiac surgery patients (n = 1,000) with postoperative myocardial dysfunction (defined as patients with intraaortic balloon pump and/or high-dose standard inotropic support) will be randomized to receive a continuous infusion of either levosimendan (0.05-0.2 μg/[kg min]) or placebo for 24-48 hours. The primary end point will be 30-day mortality. Secondary end points will be mortality at 1 year, time on mechanical ventilation, acute kidney injury, decision to stop the study drug due to adverse events or to start open-label levosimendan, and length of intensive care unit and hospital stay. We will test the hypothesis that levosimendan reduces 30-day mortality in cardiac surgery patients with postoperative myocardial dysfunction. This trial is planned to determine whether levosimendan could improve survival in patients with postoperative low cardiac output syndrome. The results of this double-blind, placebo-controlled randomized trial may provide important insights into the management of low cardiac output in cardiac surgery. Copyright © 2016 Elsevier Inc. All rights reserved.

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

Science.gov (United States)

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

2017-03-01

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

Partial state feedback control of chaotic neural network and its application

International Nuclear Information System (INIS)

He Guoguang; Shrimali, Manish Dev; Aihara, Kazuyuki

2007-01-01

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

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

National Research Council Canada - National Science Library

Wilson, Edward

1995-01-01

Four important generic issues are identified and addressed in some depth in this thesis as part of the development of an adaptive neural network based control system for an experimental free flying space robot prototype...

Deep breathing exercises performed 2 months following cardiac surgery: a randomized controlled trial.

Science.gov (United States)

Westerdahl, Elisabeth; Urell, Charlotte; Jonsson, Marcus; Bryngelsson, Ing-Liss; Hedenström, Hans; Emtner, Margareta

2014-01-01

Postoperative breathing exercises are recommended to cardiac surgery patients. Instructions concerning how long patients should continue exercises after discharge vary, and the significance of treatment needs to be determined. Our aim was to assess the effects of home-based deep breathing exercises performed with a positive expiratory pressure device for 2 months following cardiac surgery. The study design was a prospective, single-blinded, parallel-group, randomized trial. Patients performing breathing exercises 2 months after cardiac surgery (n = 159) were compared with a control group (n = 154) performing no breathing exercises after discharge. The intervention consisted of 30 slow deep breaths performed with a positive expiratory pressure device (10-15 cm H2O), 5 times a day, during the first 2 months after surgery. The outcomes were lung function measurements, oxygen saturation, thoracic excursion mobility, subjective perception of breathing and pain, patient-perceived quality of recovery (40-Item Quality of Recovery score), health-related quality of life (36-Item Short Form Health Survey), and self-reported respiratory tract infection/pneumonia and antibiotic treatment. Two months postoperatively, the patients had significantly reduced lung function, with a mean decrease in forced expiratory volume in 1 second to 93 ± 12% (P< .001) of preoperative values. Oxygenation had returned to preoperative values, and 5 of 8 aspects in the 36-Item Short Form Health Survey were improved compared with preoperative values (P< .01). There were no significant differences between the groups in any of the measured outcomes. No significant differences in lung function, subjective perceptions, or quality of life were found between patients performing home-based deep breathing exercises and control patients 2 months after cardiac surgery.

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

Indian Academy of Sciences (India)

Computer control; neural networks; nonlinear systems; adaptive ... considered in their design and so they are not applicable for many actual real-world .... particularly in the presence of unmodelled dynamics such as time delays. .... showing the algorithm operating successfully on such a (non-favoured) system, is a respec-.

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

NARCIS (Netherlands)

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

2007-01-01

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

Molecular parallels between neural and vascular development.

Science.gov (United States)

Eichmann, Anne; Thomas, Jean-Léon

2013-01-01

The human central nervous system (CNS) features a network of ~400 miles of blood vessels that receives >20% of the body's cardiac output and uses most of its blood glucose. Many human diseases, including stroke, retinopathy, and cancer, are associated with the biology of CNS blood vessels. These vessels originate from extrinsic cell populations, including endothelial cells and pericytes that colonize the CNS and interact with glia and neurons to establish the blood-brain barrier and control cerebrovascular exchanges. Neurovascular interactions also play important roles in adult neurogenic niches, which harbor a unique population of neural stem cells that are intimately associated with blood vessels. We here review the cellular and molecular mechanisms required to establish the CNS vascular network, with a special focus on neurovascular interactions and the functions of vascular endothelial growth factors.

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

Science.gov (United States)

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

2017-03-01

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

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

OpenAIRE

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

2012-01-01

Paralysis following spinal cord injury, brainstemstroke, amyotrophic lateral sclerosis and other disorders can disconnect the brain from the body, eliminating the ability to perform volitional movements. A neural interface system could restore mobility and independence for people with paralysis by translating neuronal activity directly into control signals for assistive devices. We have previously shown that people with long-standing tetraplegia can use a neural interface ...

Police work stressors and cardiac vagal control.

Science.gov (United States)

Andrew, Michael E; Violanti, John M; Gu, Ja K; Fekedulegn, Desta; Li, Shengqiao; Hartley, Tara A; Charles, Luenda E; Mnatsakanova, Anna; Miller, Diane B; Burchfiel, Cecil M

2017-09-10

This study examines relationships between the frequency and intensity of police work stressors and cardiac vagal control, estimated using the high frequency component of heart rate variability (HRV). This is a cross-sectional study of 360 officers from the Buffalo New York Police Department. Police stress was measured using the Spielberger police stress survey, which includes exposure indices created as the product of the self-evaluation of how stressful certain events were and the self-reported frequency with which they occurred. Vagal control was estimated using the high frequency component of resting HRV calculated in units of milliseconds squared and reported in natural log scale. Associations between police work stressors and vagal control were examined using linear regression for significance testing and analysis of covariance for descriptive purposes, stratified by gender, and adjusted for age and race/ethnicity. There were no significant associations between police work stressor exposure indices and vagal control among men. Among women, the inverse associations between the lack of support stressor exposure and vagal control were statistically significant in adjusted models for indices of exposure over the past year (lowest stressor quartile: M = 5.57, 95% CI 5.07 to 6.08, and highest stressor quartile: M = 5.02, 95% CI 4.54 to 5.51, test of association from continuous linear regression of vagal control on lack of support stressor β = -0.273, P = .04). This study supports an inverse association between lack of organizational support and vagal control among female but not male police officers. © 2017 Wiley Periodicals, Inc.

Neural-network-designed pulse sequences for robust control of singlet-triplet qubits

Science.gov (United States)

Yang, Xu-Chen; Yung, Man-Hong; Wang, Xin

2018-04-01

Composite pulses are essential for universal manipulation of singlet-triplet spin qubits. In the absence of noise, they are required to perform arbitrary single-qubit operations due to the special control constraint of a singlet-triplet qubit, while in a noisy environment, more complicated sequences have been developed to dynamically correct the error. Tailoring these sequences typically requires numerically solving a set of nonlinear equations. Here we demonstrate that these pulse sequences can be generated by a well-trained, double-layer neural network. For sequences designed for the noise-free case, the trained neural network is capable of producing almost exactly the same pulses known in the literature. For more complicated noise-correcting sequences, the neural network produces pulses with slightly different line shapes, but the robustness against noises remains comparable. These results indicate that the neural network can be a judicious and powerful alternative to existing techniques in developing pulse sequences for universal fault-tolerant quantum computation.

Neural network control of focal position during time-lapse microscopy of cells.

Science.gov (United States)

Wei, Ling; Roberts, Elijah

2018-05-09

Live-cell microscopy is quickly becoming an indispensable technique for studying the dynamics of cellular processes. Maintaining the specimen in focus during image acquisition is crucial for high-throughput applications, especially for long experiments or when a large sample is being continuously scanned. Automated focus control methods are often expensive, imperfect, or ill-adapted to a specific application and are a bottleneck for widespread adoption of high-throughput, live-cell imaging. Here, we demonstrate a neural network approach for automatically maintaining focus during bright-field microscopy. Z-stacks of yeast cells growing in a microfluidic device were collected and used to train a convolutional neural network to classify images according to their z-position. We studied the effect on prediction accuracy of the various hyperparameters of the neural network, including downsampling, batch size, and z-bin resolution. The network was able to predict the z-position of an image with ±1 μm accuracy, outperforming human annotators. Finally, we used our neural network to control microscope focus in real-time during a 24 hour growth experiment. The method robustly maintained the correct focal position compensating for 40 μm of focal drift and was insensitive to changes in the field of view. About ~100 annotated z-stacks were required to train the network making our method quite practical for custom autofocus applications.

Cognitive control in adolescence: neural underpinnings and relation to self-report behaviors.

Directory of Open Access Journals (Sweden)

Jessica R Andrews-Hanna

Full Text Available Adolescence is commonly characterized by impulsivity, poor decision-making, and lack of foresight. However, the developmental neural underpinnings of these characteristics are not well established.To test the hypothesis that these adolescent behaviors are linked to under-developed proactive control mechanisms, the present study employed a hybrid block/event-related functional Magnetic Resonance Imaging (fMRI Stroop paradigm combined with self-report questionnaires in a large sample of adolescents and adults, ranging in age from 14 to 25. Compared to adults, adolescents under-activated a set of brain regions implicated in proactive top-down control across task blocks comprised of difficult and easy trials. Moreover, the magnitude of lateral prefrontal activity in adolescents predicted self-report measures of impulse control, foresight, and resistance to peer pressure. Consistent with reactive compensatory mechanisms to reduced proactive control, older adolescents exhibited elevated transient activity in regions implicated in response-related interference resolution.Collectively, these results suggest that maturation of cognitive control may be partly mediated by earlier development of neural systems supporting reactive control and delayed development of systems supporting proactive control. Importantly, the development of these mechanisms is associated with cognitive control in real-life behaviors.

Usefulness of cardiac 125I-metaiodobenzylguanidine uptake for evaluation of cardiac sympathetic nerve abnormalities in diabetic rats

International Nuclear Information System (INIS)

Abe, Nanami; Kashiwagi, Atsunori; Shigeta, Yukio

1992-01-01

We investigated cardiac sympathetic nerve abnormalities in streptozocin-induced diabetic rats using 125 I-metaiodobenzylguanidine (MIBG). The radioactivity ratio of cardiac tissue to 1 ml blood (H/B) was used as an index of cardiac MIBG uptake. Cardiac 125 I-MIBG uptake (H/B) in 4-, 8- and 20-wk diabetic rats was 48% lower than that in control rats. Similar results were obtained even when the data were corrected for g wet tissue weight. Although there was no improvement in H/B following 2-wk insulin treatment, the H/B ratio increased significantly, to 85% of control levels, following 4 wk insulin treatment indicating the reversibility of impaired MIBG uptake in diabetic rats. In vivo reserpine treatment resulted in a 50% reduction in the H/B value in control rats. However, the treatment did not significantly suppress uptake in diabetic rats. Cardiac norepinephrine content in both * 4- and ** 8-wk diabetic rats was significantly ( * p ** p 125 I-MIBG in diabetic rats is significantly impaired due to cardiac sympathetic nerve abnormalities. These abnormalities are reversible, however, dependent on the diabetic state. (author)

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

Energy Technology Data Exchange (ETDEWEB)

Ondrej Linda; Milos Manic; Timothy R. McJunkin

2011-08-01

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

No Evidence That Gratitude Enhances Neural Performance Monitoring or Conflict-Driven Control.

Science.gov (United States)

Saunders, Blair; He, Frank F H; Inzlicht, Michael

2015-01-01

It has recently been suggested that gratitude can benefit self-regulation by reducing impulsivity during economic decision making. We tested if comparable benefits of gratitude are observed for neural performance monitoring and conflict-driven self-control. In a pre-post design, 61 participants were randomly assigned to either a gratitude or happiness condition, and then performed a pre-induction flanker task. Subsequently, participants recalled an autobiographical event where they had felt grateful or happy, followed by a post-induction flanker task. Despite closely following existing protocols, participants in the gratitude condition did not report elevated gratefulness compared to the happy group. In regard to self-control, we found no association between gratitude--operationalized by experimental condition or as a continuous predictor--and any control metric, including flanker interference, post-error adjustments, or neural monitoring (the error-related negativity, ERN). Thus, while gratitude might increase economic patience, such benefits may not generalize to conflict-driven control processes.

A Novel Neural Network Vector Control for Single-Phase Grid-Connected Converters with L, LC and LCL Filters

Directory of Open Access Journals (Sweden)

Xingang Fu

2016-04-01

Full Text Available This paper investigates a novel recurrent neural network (NN-based vector control approach for single-phase grid-connected converters (GCCs with L (inductor, LC (inductor-capacitor and LCL (inductor-capacitor-inductor filters and provides their comparison study with the conventional standard vector control method. A single neural network controller replaces two current-loop PI controllers, and the NN training approximates the optimal control for the single-phase GCC system. The Levenberg–Marquardt (LM algorithm was used to train the NN controller based on the complete system equations without any decoupling policies. The proposed NN approach can solve the decoupling problem associated with the conventional vector control methods for L, LC and LCL-filter-based single-phase GCCs. Both simulation study and hardware experiments demonstrate that the neural network vector controller shows much more improved performance than that of conventional vector controllers, including faster response speed and lower overshoot. Especially, NN vector control could achieve very good performance using low switch frequency. More importantly, the neural network vector controller is a damping free controller, which is generally required by a conventional vector controller for an LCL-filter-based single-phase grid-connected converter and, therefore, can overcome the inefficiency problem caused by damping policies.

Intelligent control a hybrid approach based on fuzzy logic, neural networks and genetic algorithms

CERN Document Server

Siddique, Nazmul

2014-01-01

Intelligent Control considers non-traditional modelling and control approaches to nonlinear systems. Fuzzy logic, neural networks and evolutionary computing techniques are the main tools used. The book presents a modular switching fuzzy logic controller where a PD-type fuzzy controller is executed first followed by a PI-type fuzzy controller thus improving the performance of the controller compared with a PID-type fuzzy controller.  The advantage of the switching-type fuzzy controller is that it uses one rule-base thus minimises the rule-base during execution. A single rule-base is developed by merging the membership functions for change of error of the PD-type controller and sum of error of the PI-type controller. Membership functions are then optimized using evolutionary algorithms. Since the two fuzzy controllers were executed in series, necessary further tuning of the differential and integral scaling factors of the controller is then performed. Neural-network-based tuning for the scaling parameters of t...

Reservoir-based Online Adaptive Forward Models with Neural Control for Complex Locomotion in a Hexapod Robot

DEFF Research Database (Denmark)

Manoonpong, Poramate; Dasgupta, Sakyasingha; Goldschmidt, Dennis

2014-01-01

Walking animals show fascinating locomotor abilities and complex behaviors. Biological study has revealed that such complex behaviors is a result of a combination of biomechanics and neural mechanisms. While biomechanics allows for flexibility and a variety of movements, neural mechanisms generate...... locomotion, make predictions, and provide adaptation. Inspired by this finding, we present here an artificial bio-inspired walking system which combines biomechanics (in terms of its body and leg structures) and neural mechanisms. The neural mechanisms consist of 1) central pattern generator-based control...... for generating basic rhythmic patterns and coordinated movements, 2) reservoir-based adaptive forward models with efference copies for sensory prediction as well as state estimation, and 3) searching and elevation control for adapting the movement of an individual leg to deal with different environmental...

Drive reinforcement neural networks for reactor control. Final report

International Nuclear Information System (INIS)

Williams, J.G.; Jouse, W.C.

1995-01-01

In view of the loss of the third year funding, the scope of the project goals has been revised. The revision in project scope no longer allows for the detailed modeling of the EBR-11 start-up task that was originally envisaged. The authors are continuing, however, to model the control of the rapid power ascent of the University of Arizona TRIGA reactor using a model-based controller and using a drive reinforcement neural network. These will be combined during the concluding period of the project into a hierarchical control architecture. In addition, the modeling of a PWR feedwater heater has continued, and an autonomous fault-tolerant software architecture for its control has been proposed

Finite-Time Synchronizing Control for Chaotic Neural Networks

Directory of Open Access Journals (Sweden)

Chao Zhang

2014-01-01

Full Text Available This paper addresses the finite-time synchronizing problem for a class of chaotic neural networks. In a real communication network, parameters of the master system may be time-varying and the system may be perturbed by external disturbances. A simple high-gain observer is designed to track all the nonlinearities, unknown system functions, and disturbances. Then, a dynamic active compensatory controller is proposed and by using the singular perturbation theory, the control method can guarantee the finite-time stability of the error system between the master system and the slave system. Finally, two illustrative examples are provided to show the effectiveness and applicability of the proposed scheme.

Development and Flight Testing of a Neural Network Based Flight Control System on the NF-15B Aircraft

Science.gov (United States)

Bomben, Craig R.; Smolka, James W.; Bosworth, John T.; Silliams-Hayes, Peggy S.; Burken, John J.; Larson, Richard R.; Buschbacher, Mark J.; Maliska, Heather A.

2006-01-01

The Intelligent Flight Control System (IFCS) project at the NASA Dryden Flight Research Center, Edwards AFB, CA, has been investigating the use of neural network based adaptive control on a unique NF-15B test aircraft. The IFCS neural network is a software processor that stores measured aircraft response information to dynamically alter flight control gains. In 2006, the neural network was engaged and allowed to learn in real time to dynamically alter the aircraft handling qualities characteristics in the presence of actual aerodynamic failure conditions injected into the aircraft through the flight control system. The use of neural network and similar adaptive technologies in the design of highly fault and damage tolerant flight control systems shows promise in making future aircraft far more survivable than current technology allows. This paper will present the results of the IFCS flight test program conducted at the NASA Dryden Flight Research Center in 2006, with emphasis on challenges encountered and lessons learned.

Studies on neutron noise diagnostics of control rod vibrations by neural networks

International Nuclear Information System (INIS)

Roston, G.; Kozma, R.; Kitamura, M.; Garis, N.S.; Pazsit, I.

1996-01-01

This work is focussed on the study of a neutron noise based technique for the diagnostics of reactor core internal, in particular, excessively vibrating control rods. The use of a combination of physical models and neural networks offers an alternative way of performing the inversion procedure. The application of a neural network technique to determine the rod position from the detector spectra is much faster, more effective and simpler to use than the conventional method. (author). 5 refs., 1 fig., 1 tab

Novel prescribed performance neural control of a flexible air-breathing hypersonic vehicle with unknown initial errors.

Science.gov (United States)

Bu, Xiangwei; Wu, Xiaoyan; Zhu, Fujing; Huang, Jiaqi; Ma, Zhen; Zhang, Rui

2015-11-01

A novel prescribed performance neural controller with unknown initial errors is addressed for the longitudinal dynamic model of a flexible air-breathing hypersonic vehicle (FAHV) subject to parametric uncertainties. Different from traditional prescribed performance control (PPC) requiring that the initial errors have to be known accurately, this paper investigates the tracking control without accurate initial errors via exploiting a new performance function. A combined neural back-stepping and minimal learning parameter (MLP) technology is employed for exploring a prescribed performance controller that provides robust tracking of velocity and altitude reference trajectories. The highlight is that the transient performance of velocity and altitude tracking errors is satisfactory and the computational load of neural approximation is low. Finally, numerical simulation results from a nonlinear FAHV model demonstrate the efficacy of the proposed strategy. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

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.

High-order tracking differentiator based adaptive neural control of a flexible air-breathing hypersonic vehicle subject to actuators constraints.

Science.gov (United States)

Bu, Xiangwei; Wu, Xiaoyan; Tian, Mingyan; Huang, Jiaqi; Zhang, Rui; Ma, Zhen

2015-09-01

In this paper, an adaptive neural controller is exploited for a constrained flexible air-breathing hypersonic vehicle (FAHV) based on high-order tracking differentiator (HTD). By utilizing functional decomposition methodology, the dynamic model is reasonably decomposed into the respective velocity subsystem and altitude subsystem. For the velocity subsystem, a dynamic inversion based neural controller is constructed. By introducing the HTD to adaptively estimate the newly defined states generated in the process of model transformation, a novel neural based altitude controller that is quite simpler than the ones derived from back-stepping is addressed based on the normal output-feedback form instead of the strict-feedback formulation. Based on minimal-learning parameter scheme, only two neural networks with two adaptive parameters are needed for neural approximation. Especially, a novel auxiliary system is explored to deal with the problem of control inputs constraints. Finally, simulation results are presented to test the effectiveness of the proposed control strategy in the presence of system uncertainties and actuators constraints. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Neural-network hybrid control for antilock braking systems.

Science.gov (United States)

Lin, Chih-Min; Hsu, C F

2003-01-01

The antilock braking systems are designed to maximize wheel traction by preventing the wheels from locking during braking, while also maintaining adequate vehicle steerability; however, the performance is often degraded under harsh road conditions. In this paper, a hybrid control system with a recurrent neural network (RNN) observer is developed for antilock braking systems. This hybrid control system is comprised of an ideal controller and a compensation controller. The ideal controller, containing an RNN uncertainty observer, is the principal controller; and the compensation controller is a compensator for the difference between the system uncertainty and the estimated uncertainty. Since for dynamic response the RNN has capabilities superior to the feedforward NN, it is utilized for the uncertainty observer. The Taylor linearization technique is employed to increase the learning ability of the RNN. In addition, the on-line parameter adaptation laws are derived based on a Lyapunov function, so the stability of the system can be guaranteed. Simulations are performed to demonstrate the effectiveness of the proposed NN hybrid control system for antilock braking control under various road conditions.

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.

Cardiac Arrhythmia Classification by Multi-Layer Perceptron and Convolution Neural Networks

Directory of Open Access Journals (Sweden)

Shalin Savalia

2018-05-01

Full Text Available The electrocardiogram (ECG plays an imperative role in the medical field, as it records heart signal over time and is used to discover numerous cardiovascular diseases. If a documented ECG signal has a certain irregularity in its predefined features, this is called arrhythmia, the types of which include tachycardia, bradycardia, supraventricular arrhythmias, and ventricular, etc. This has encouraged us to do research that consists of distinguishing between several arrhythmias by using deep neural network algorithms such as multi-layer perceptron (MLP and convolution neural network (CNN. The TensorFlow library that was established by Google for deep learning and machine learning is used in python to acquire the algorithms proposed here. The ECG databases accessible at PhysioBank.com and kaggle.com were used for training, testing, and validation of the MLP and CNN algorithms. The proposed algorithm consists of four hidden layers with weights, biases in MLP, and four-layer convolution neural networks which map ECG samples to the different classes of arrhythmia. The accuracy of the algorithm surpasses the performance of the current algorithms that have been developed by other cardiologists in both sensitivity and precision.

Cardiac Arrhythmia Classification by Multi-Layer Perceptron and Convolution Neural Networks.

Science.gov (United States)

Savalia, Shalin; Emamian, Vahid

2018-05-04

The electrocardiogram (ECG) plays an imperative role in the medical field, as it records heart signal over time and is used to discover numerous cardiovascular diseases. If a documented ECG signal has a certain irregularity in its predefined features, this is called arrhythmia, the types of which include tachycardia, bradycardia, supraventricular arrhythmias, and ventricular, etc. This has encouraged us to do research that consists of distinguishing between several arrhythmias by using deep neural network algorithms such as multi-layer perceptron (MLP) and convolution neural network (CNN). The TensorFlow library that was established by Google for deep learning and machine learning is used in python to acquire the algorithms proposed here. The ECG databases accessible at PhysioBank.com and kaggle.com were used for training, testing, and validation of the MLP and CNN algorithms. The proposed algorithm consists of four hidden layers with weights, biases in MLP, and four-layer convolution neural networks which map ECG samples to the different classes of arrhythmia. The accuracy of the algorithm surpasses the performance of the current algorithms that have been developed by other cardiologists in both sensitivity and precision.

Adaptive Neural-Sliding Mode Control of Active Suspension System for Camera Stabilization

Directory of Open Access Journals (Sweden)

Feng Zhao

2015-01-01

Full Text Available The camera always suffers from image instability on the moving vehicle due to the unintentional vibrations caused by road roughness. This paper presents a novel adaptive neural network based on sliding mode control strategy to stabilize the image captured area of the camera. The purpose is to suppress vertical displacement of sprung mass with the application of active suspension system. Since the active suspension system has nonlinear and time varying characteristics, adaptive neural network (ANN is proposed to make the controller robustness against systematic uncertainties, which release the model-based requirement of the sliding model control, and the weighting matrix is adjusted online according to Lyapunov function. The control system consists of two loops. The outer loop is a position controller designed with sliding mode strategy, while the PID controller in the inner loop is to track the desired force. The closed loop stability and asymptotic convergence performance can be guaranteed on the basis of the Lyapunov stability theory. Finally, the simulation results show that the employed controller effectively suppresses the vibration of the camera and enhances the stabilization of the entire camera, where different excitations are considered to validate the system performance.

Adaptive dynamic inversion robust control for BTT missile based on wavelet neural network

Science.gov (United States)

Li, Chuanfeng; Wang, Yongji; Deng, Zhixiang; Wu, Hao

2009-10-01

A new nonlinear control strategy incorporated the dynamic inversion method with wavelet neural networks is presented for the nonlinear coupling system of Bank-to-Turn(BTT) missile in reentry phase. The basic control law is designed by using the dynamic inversion feedback linearization method, and the online learning wavelet neural network is used to compensate the inversion error due to aerodynamic parameter errors, modeling imprecise and external disturbance in view of the time-frequency localization properties of wavelet transform. Weights adjusting laws are derived according to Lyapunov stability theory, which can guarantee the boundedness of all signals in the whole system. Furthermore, robust stability of the closed-loop system under this tracking law is proved. Finally, the six degree-of-freedom(6DOF) simulation results have shown that the attitude angles can track the anticipant command precisely under the circumstances of existing external disturbance and in the presence of parameter uncertainty. It means that the dependence on model by dynamic inversion method is reduced and the robustness of control system is enhanced by using wavelet neural network(WNN) to reconstruct inversion error on-line.

Effects of a Structured Discharge Planning Program on Perceived Functional Status, Cardiac Self-efficacy, Patient Satisfaction, and Unexpected Hospital Revisits Among Filipino Cardiac Patients: A Randomized Controlled Study.

Science.gov (United States)

Cajanding, Ruff Joseph

Cardiovascular diseases remain the leading cause of morbidity and mortality among Filipinos and are responsible for a very large number of hospital readmissions. Comprehensive discharge planning programs have demonstrated positive benefits among various populations of patients with cardiovascular disease, but the clinical and psychosocial effects of such intervention among Filipino patients with acute myocardial infarction (AMI) have not been studied. In this study we aimed to determine the effectiveness of a nurse-led structured discharge planning program on perceived functional status, cardiac self-efficacy, patient satisfaction, and unexpected hospital revisits among Filipino patients with AMI. A true experimental (randomized control) 2-group design with repeated measures and data collected before and after intervention and at 1-month follow-up was used in this study. Participants were assigned to either the control (n = 68) or the intervention group (n = 75). Intervention participants underwent a 3-day structured discharge planning program implemented by a cardiovascular nurse practitioner, which is comprised of a series of individualized lecture-discussion, provision of feedback, integrative problem solving, goal setting, and action planning. Control participants received standard routine care. Measures of functional status, cardiac self-efficacy, and patient satisfaction were measured at baseline; cardiac self-efficacy and patient satisfaction scores were measured prior to discharge, and perceived functional status and number of revisits were measured 1 month after discharge. Participants in the intervention group had significant improvement in functional status, cardiac self-efficacy, and patient satisfaction scores at baseline and at follow-up compared with the control participants. Furthermore, participants in the intervention group had significantly fewer hospital revisits compared with those who received only standard care. The results demonstrate that a

Force control of a magnetorheological damper using an elementary hysteresis model-based feedforward neural network

International Nuclear Information System (INIS)

Ekkachai, Kittipong; Nilkhamhang, Itthisek; Tungpimolrut, Kanokvate

2013-01-01

An inverse controller is proposed for a magnetorheological (MR) damper that consists of a hysteresis model and a voltage controller. The force characteristics of the MR damper caused by excitation signals are represented by a feedforward neural network (FNN) with an elementary hysteresis model (EHM). The voltage controller is constructed using another FNN to calculate a suitable input signal that will allow the MR damper to produce the desired damping force. The performance of the proposed EHM-based FNN controller is experimentally compared to existing control methodologies, such as clipped-optimal control, signum function control, conventional FNN, and recurrent neural network with displacement or velocity inputs. The results show that the proposed controller, which does not require force feedback to implement, provides excellent accuracy, fast response time, and lower energy consumption. (paper)

Neural Computation Scheme of Compound Control: Tacit Learning for Bipedal Locomotion

Science.gov (United States)

Shimoda, Shingo; Kimura, Hidenori

The growing need for controlling complex behaviors of versatile robots working in unpredictable environment has revealed the fundamental limitation of model-based control strategy that requires precise models of robots and environments before their operations. This difficulty is fundamental and has the same root with the well-known frame problem in artificial intelligence. It has been a central long standing issue in advanced robotics, as well as machine intelligence, to find a prospective clue to attack this fundamental difficulty. The general consensus shared by many leading researchers in the related field is that the body plays an important role in acquiring intelligence that can conquer unknowns. In particular, purposeful behaviors emerge during body-environment interactions with the help of an appropriately organized neural computational scheme that can exploit what the environment can afford. Along this line, we propose a new scheme of neural computation based on compound control which represents a typical feature of biological controls. This scheme is based on classical neuron models with local rules that can create macroscopic purposeful behaviors. This scheme is applied to a bipedal robot and generates the rhythm of walking without any model of robot dynamics and environments.

Symbolic dynamics of heart rate variability - a promising tool to investigate cardiac sympathovagal control in attention deficit/hyperactivity disorder (ADHD)?

Science.gov (United States)

Tonhajzerova, Ingrid; Farsky, Ivan; Mestanik, Michal; Visnovcova, Zuzana; Mestanikova, Andrea; Hrtanek, Igor; Ondrejka, Igor

2016-06-01

We aimed to evaluate complex cardiac sympathovagal control in attention deficit/hyperactivity disorder (ADHD) by using heart rate variability (HRV) nonlinear analysis - symbolic dynamics. We examined 29 boys with untreated ADHD and 25 healthy boys (age 8-13 years). ADHD symptoms were evaluated by ADHD-RS-IV scale. ECG was recorded in 3 positions: baseline supine position, orthostasis, and clinostasis. Symbolic dynamics indices were used for the assessment of complex cardiac sympathovagal regulation: normalised complexity index (NCI), normalised unpredictability index (NUPI), and pattern classification measures (0V%, 1V%, 2LV%, 2UV%). The results showed that HRV complexity was significantly reduced at rest (NUPI) and during standing position (NCI, NUPI) in ADHD group compared to controls. Cardiac-linked sympathetic index 0V% was significantly higher during all posture positions and cardiovagal index 2LV% was significantly lower to standing in boys suffering from ADHD. Importantly, ADHD symptom inattention positively correlated with 0V%, and negatively correlated with NCI, NUPI. Concluding, symbolic dynamics revealed impaired complex neurocardiac control characterised by potential cardiac beta-adrenergic overactivity and vagal deficiency at rest and to posture changes in boys suffering from ADHD that is correlated with inattention. We suggest that symbolic dynamics indices could represent promising cardiac biomarkers in ADHD.

Optimization and control of a small angle ion source using an adaptive neural network controller

Energy Technology Data Exchange (ETDEWEB)

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

1993-09-01

This project developed an automated controller based on an artificial neural network and evaluated its applicability in a real-time environment. This capability was developed within the context of a small angle negative ion source on the Discharge Test Stand at Los Alamos. The controller processes information obtained from the beam current waveform, developing a figure of merit (fom) to determine the ion source operating conditions. The fom is composed of the magnitude of the beam current, the stability of operation, and the quietness of the beam. Using no knowledge of operating conditions, the controller begins by making of rough scan of the four-dimensional operating surface. This surface uses as independent variables the anode and cathode temperatures, the hydrogen flow rate, and the arc voltage. `Me dependent variable is the fom described above. Once the rough approximation of the surface has been determined, the network formulates a model from which it determines the best operating point. The controller takes the ion source to that operating point for a reality check. As real data is fed in, the model of the operating surface is updated until the neural network`s model agrees with reality. The controller then uses a gradient ascent method to optimize the operation of the ion source. Initial tests of the controller indicate that it is remarkably capable. It has optimized the operation of the ion source on six different occasions bringing the beam to excellent quality and stability.

Robust synchronization of delayed neural networks based on adaptive control and parameters identification

International Nuclear Information System (INIS)

Zhou Jin; Chen Tianping; Xiang Lan

2006-01-01

This paper investigates synchronization dynamics of delayed neural networks with all the parameters unknown. By combining the adaptive control and linear feedback with the updated law, some simple yet generic criteria for determining the robust synchronization based on the parameters identification of uncertain chaotic delayed neural networks are derived by using the invariance principle of functional differential equations. It is shown that the approaches developed here further extend the ideas and techniques presented in recent literature, and they are also simple to implement in practice. Furthermore, the theoretical results are applied to a typical chaotic delayed Hopfied neural networks, and numerical simulation also demonstrate the effectiveness and feasibility of the proposed technique

Effect of perioperative beta blockade in patients with diabetes undergoing major non-cardiac surgery: randomised placebo controlled, blinded multicentre trial

DEFF Research Database (Denmark)

Juul, Anne Benedicte; Wetterslev, Jørn; Gluud, Christian

2006-01-01

Objectives To evaluate the long term effects of perioperative blockade on mortality and cardiac morbidity in patients with diabetes undergoing major non-cardiac surgery. Design Randomised placebo controlled and blinded multicentre trial. Analyses were by intention to treat. Setting University...

Adaptive Backstepping-Based Neural Tracking Control for MIMO Nonlinear Switched Systems Subject to Input Delays.

Science.gov (United States)

Niu, Ben; Li, Lu

2018-06-01

This brief proposes a new neural-network (NN)-based adaptive output tracking control scheme for a class of disturbed multiple-input multiple-output uncertain nonlinear switched systems with input delays. By combining the universal approximation ability of radial basis function NNs and adaptive backstepping recursive design with an improved multiple Lyapunov function (MLF) scheme, a novel adaptive neural output tracking controller design method is presented for the switched system. The feature of the developed design is that different coordinate transformations are adopted to overcome the conservativeness caused by adopting a common coordinate transformation for all subsystems. It is shown that all the variables of the resulting closed-loop system are semiglobally uniformly ultimately bounded under a class of switching signals in the presence of MLF and that the system output can follow the desired reference signal. To demonstrate the practicability of the obtained result, an adaptive neural output tracking controller is designed for a mass-spring-damper system.

Development of neural network driven fuzzy controller for outlet sodium temperature of DHX

International Nuclear Information System (INIS)

Okusa, Kyoichi; Endou, Akira; Yoshikawa, Shinji; Ozawa, Kenji

1996-01-01

Fuzzy controls are capable to exquisitely control non-linear dynamic systems in wide operating range, using linguistic description to define the control law. However the selection and the definition of the fuzzy rules and sets require a tedious trial and error process based on experience. As a method to overcome this limitation, a neural network driven fuzzy control (NDF), where the learning capability of the neural network (NN) is used to build the fuzzy rules and sets, is presented in this paper. In the NDF control the IF part of a fuzzy control is represented by a multilayer NN while the THEN part is represented by a series of multilayer NNs which calculate the desirable control action. In this work the usual stepwise variable reduction method, used for the selection of the input variable in the THEN part NN, is replaced with a learning algorithm with forgetting mechanism that realizes the automatic reduction of the variables and the tuning up of all the fuzzy control law i.e. the membership function. The NDF has been successfully applied to control the outlet sodium temperature of a dump heat exchanger (DHX) of a FBR plant

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

Science.gov (United States)

Li, Yongcheng; Sun, Rong; Wang, Yuechao; Li, Hongyi; Zheng, Xiongfei

2016-01-01

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

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

Directory of Open Access Journals (Sweden)

Yongcheng Li

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

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

Science.gov (United States)

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

2014-01-01

Auditory processing is presumed to be influenced by cognitive processes - including attentional control - in a top-down manner. In bilinguals, activation of both languages during daily communication hones inhibitory skills, which subsequently bolster attentional control. We hypothesize that the heightened attentional demands of bilingual communication strengthens connections between cognitive (i.e., attentional control) and auditory processing, leading to greater across-trial consistency in the auditory evoked response (i.e., neural consistency) in bilinguals. To assess this, we collected passively-elicited auditory evoked responses to the syllable [da] in adolescent Spanish-English bilinguals and English monolinguals and separately obtained measures of attentional control and language ability. Bilinguals demonstrated enhanced attentional control and more consistent brainstem and cortical responses. In bilinguals, but not monolinguals, brainstem consistency tracked with language proficiency and attentional control. We interpret these enhancements in neural consistency as the outcome of strengthened attentional control that emerged from experience communicating in two languages. Copyright © 2013 Elsevier Inc. All rights reserved.

Nonlinear Control of an Active Magnetic Bearing System Achieved Using a Fuzzy Control with Radial Basis Function Neural Network

Directory of Open Access Journals (Sweden)

Seng-Chi Chen

2014-01-01

Full Text Available Studies on active magnetic bearing (AMB systems are increasing in popularity and practical applications. Magnetic bearings cause less noise, friction, and vibration than the conventional mechanical bearings; however, the control of AMB systems requires further investigation. The magnetic force has a highly nonlinear relation to the control current and the air gap. This paper proposes an intelligent control method for positioning an AMB system that uses a neural fuzzy controller (NFC. The mathematical model of an AMB system comprises identification followed by collection of information from this system. A fuzzy logic controller (FLC, the parameters of which are adjusted using a radial basis function neural network (RBFNN, is applied to the unbalanced vibration in an AMB system. The AMB system exhibited a satisfactory control performance, with low overshoot, and produced improved transient and steady-state responses under various operating conditions. The NFC has been verified on a prototype AMB system. The proposed controller can be feasibly applied to AMB systems exposed to various external disturbances; demonstrating the effectiveness of the NFC with self-learning and self-improving capacities is proven.

A Wavelet Neural Network Optimal Control Model for Traffic-Flow Prediction in Intelligent Transport Systems

Science.gov (United States)

Huang, Darong; Bai, Xing-Rong

Based on wavelet transform and neural network theory, a traffic-flow prediction model, which was used in optimal control of Intelligent Traffic system, is constructed. First of all, we have extracted the scale coefficient and wavelet coefficient from the online measured raw data of traffic flow via wavelet transform; Secondly, an Artificial Neural Network model of Traffic-flow Prediction was constructed and trained using the coefficient sequences as inputs and raw data as outputs; Simultaneous, we have designed the running principium of the optimal control system of traffic-flow Forecasting model, the network topological structure and the data transmitted model; Finally, a simulated example has shown that the technique is effectively and exactly. The theoretical results indicated that the wavelet neural network prediction model and algorithms have a broad prospect for practical application.

Factors influencing the role of cardiac autonomic regulation in the service of cognitive control.

Science.gov (United States)

Capuana, Lesley J; Dywan, Jane; Tays, William J; Elmers, Jamie L; Witherspoon, Richelle; Segalowitz, Sidney J

2014-10-01

Working from a model of neurovisceral integration, we examined whether adding response contingencies and motivational involvement would increase the need for cardiac autonomic regulation in maintaining effective cognitive control. Respiratory sinus arrhythmia (RSA) was recorded during variants of the Stroop color-word task. The Basic task involved "accepting" congruent items and "rejecting" words printed in incongruent colors (BLUE in red font); an added contingency involved rejecting a particular congruent word (e.g., RED in red font), or a congruent word repeated on an immediately subsequent trial. Motivation was increased by adding a financial incentive phase. Results indicate that pre-task RSA predicted accuracy best when response contingencies required the maintenance of a specific item in memory or on the Basic Stroop task when errors resulted in financial loss. Overall, RSA appeared to be most relevant to performance when the task encouraged a more proactive style of cognitive control, a control strategy thought to be more metabolically costly, and hence, more reliant on flexible cardiac autonomic regulation. Copyright © 2014 Elsevier B.V. All rights reserved.

Continuous bilateral thoracic paravertebral blockade for analgesia after cardiac surgery: a randomised, controlled trial.

Science.gov (United States)

Lockwood, Geoff G; Cabreros, Leilani; Banach, Dorota; Punjabi, Prakash P

2017-10-01

Continuous bilateral thoracic paravertebral blockade has been used for analgesia after cardiac surgery, but its efficacy has never been formally tested. Fifty adult patients were enrolled in a double-blind, randomised, controlled study of continuous bilateral thoracic paravertebral infusion of 0.5% lidocaine (1 mg.kg -1 .hr -1 ) for analgesia after coronary surgery. Control patients received a subcutaneous infusion of lidocaine at the same rate through catheters inserted at the same locations as the study group. The primary outcome was morphine consumption at 48 hours using patient-controlled analgesia (PCA). Secondary outcomes included pain, respiratory function, nausea and vomiting. Serum lidocaine concentrations were measured on the first two post-operative days. There was no difference in morphine consumption or in any other outcome measure between the groups. Serum lidocaine concentrations increased during the study, with a maximum of 5.9 mg.l -1 . There were no adverse events as a consequence of the study. Bilateral paravertebral infusion of lidocaine confers no advantage over systemic lidocaine infusion after cardiac surgery. ISRCTN13424423 ( https://www.isrctn.com ).

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.

Artificial Neural Network for Total Laboratory Automation to Improve the Management of Sample Dilution.

Science.gov (United States)

Ialongo, Cristiano; Pieri, Massimo; Bernardini, Sergio

2017-02-01

Diluting a sample to obtain a measure within the analytical range is a common task in clinical laboratories. However, for urgent samples, it can cause delays in test reporting, which can put patients' safety at risk. The aim of this work is to show a simple artificial neural network that can be used to make it unnecessary to predilute a sample using the information available through the laboratory information system. Particularly, the Multilayer Perceptron neural network built on a data set of 16,106 cardiac troponin I test records produced a correct inference rate of 100% for samples not requiring predilution and 86.2% for those requiring predilution. With respect to the inference reliability, the most relevant inputs were the presence of a cardiac event or surgery and the result of the previous assay. Therefore, such an artificial neural network can be easily implemented into a total automation framework to sensibly reduce the turnaround time of critical orders delayed by the operation required to retrieve, dilute, and retest the sample.

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

Directory of Open Access Journals (Sweden)

Bo Fan

2014-01-01

Full Text Available Rotor resistance identification has been well recognized as one of the most critical factors affecting the theoretical study and applications of AC motor’s control for high performance variable frequency speed adjustment. This paper proposes a novel model for rotor resistance parameters identification based on Elman neural networks. Elman recurrent neural network is capable of performing nonlinear function approximation and possesses the ability of time-variable characteristic adaptation. Those influencing factors of specified parameter are analyzed, respectively, and various work states are covered to ensure the completeness of the training samples. Through signal preprocessing on samples and training dataset, different input parameters identifications with one network are compared and analyzed. The trained Elman neural network, applied in the identification model, is able to efficiently predict the rotor resistance in high accuracy. The simulation and experimental results show that the proposed method owns extensive adaptability and performs very well in its application to vector controlled induction motor. This identification method is able to enhance the performance of induction motor’s variable-frequency speed regulation.

Adaptive Neural Output-Feedback Control for a Class of Nonlower Triangular Nonlinear Systems With Unmodeled Dynamics.

Science.gov (United States)

Wang, Huanqing; Liu, Peter Xiaoping; Li, Shuai; Wang, Ding

2017-08-29

This paper presents the development of an adaptive neural controller for a class of nonlinear systems with unmodeled dynamics and immeasurable states. An observer is designed to estimate system states. The structure consistency of virtual control signals and the variable partition technique are combined to overcome the difficulties appearing in a nonlower triangular form. An adaptive neural output-feedback controller is developed based on the backstepping technique and the universal approximation property of the radial basis function (RBF) neural networks. By using the Lyapunov stability analysis, the semiglobally and uniformly ultimate boundedness of all signals within the closed-loop system is guaranteed. The simulation results show that the controlled system converges quickly, and all the signals are bounded. This paper is novel at least in the two aspects: 1) an output-feedback control strategy is developed for a class of nonlower triangular nonlinear systems with unmodeled dynamics and 2) the nonlinear disturbances and their bounds are the functions of all states, which is in a more general form than existing results.

An indirect adaptive neural control of a visual-based quadrotor robot for pursuing a moving target.

Science.gov (United States)

Shirzadeh, Masoud; Amirkhani, Abdollah; Jalali, Aliakbar; Mosavi, Mohammad R

2015-11-01

This paper aims to use a visual-based control mechanism to control a quadrotor type aerial robot which is in pursuit of a moving target. The nonlinear nature of a quadrotor, on the one hand, and the difficulty of obtaining an exact model for it, on the other hand, constitute two serious challenges in designing a controller for this UAV. A potential solution for such problems is the use of intelligent control methods such as those that rely on artificial neural networks and other similar approaches. In addition to the two mentioned problems, another problem that emerges due to the moving nature of a target is the uncertainty that exists in the target image. By employing an artificial neural network with a Radial Basis Function (RBF) an indirect adaptive neural controller has been designed for a quadrotor robot in search of a moving target. The results of the simulation for different paths show that the quadrotor has efficiently tracked the moving target. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

The hypoxia factor Hif-1α controls neural crest chemotaxis and epithelial to mesenchymal transition

Science.gov (United States)

Barriga, Elias H.; Maxwell, Patrick H.

2013-01-01

One of the most important mechanisms that promotes metastasis is the stabilization of Hif-1 (hypoxia-inducible transcription factor 1). We decided to test whether Hif-1α also was required for early embryonic development. We focused our attention on the development of the neural crest, a highly migratory embryonic cell population whose behavior has been likened to cancer metastasis. Inhibition of Hif-1α by antisense morpholinos in Xenopus laevis or zebrafish embryos led to complete inhibition of neural crest migration. We show that Hif-1α controls the expression of Twist, which in turn represses E-cadherin during epithelial to mesenchymal transition (EMT) of neural crest cells. Thus, Hif-1α allows cells to initiate migration by promoting the release of cell–cell adhesions. Additionally, Hif-1α controls chemotaxis toward the chemokine SDF-1 by regulating expression of its receptor Cxcr4. Our results point to Hif-1α as a novel and key regulator that integrates EMT and chemotaxis during migration of neural crest cells. PMID:23712262

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

Directory of Open Access Journals (Sweden)

Wang Chao

2016-03-01

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

Functional electrical stimulation controlled by artificial neural networks: pilot experiments with simple movements are promising for rehabilitation applications.

Science.gov (United States)

Ferrante, Simona; Pedrocchi, Alessandra; Iannò, Marco; De Momi, Elena; Ferrarin, Maurizio; Ferrigno, Giancarlo

2004-01-01

This study falls within the ambit of research on functional electrical stimulation for the design of rehabilitation training for spinal cord injured patients. In this context, a crucial issue is the control of the stimulation parameters in order to optimize the patterns of muscle activation and to increase the duration of the exercises. An adaptive control system (NEURADAPT) based on artificial neural networks (ANNs) was developed to control the knee joint in accordance with desired trajectories by stimulating quadriceps muscles. This strategy includes an inverse neural model of the stimulated limb in the feedforward line and a neural network trained on-line in the feedback loop. NEURADAPT was compared with a linear closed-loop proportional integrative derivative (PID) controller and with a model-based neural controller (NEUROPID). Experiments on two subjects (one healthy and one paraplegic) show the good performance of NEURADAPT, which is able to reduce the time lag introduced by the PID controller. In addition, control systems based on ANN techniques do not require complicated calibration procedures at the beginning of each experimental session. After the initial learning phase, the ANN, thanks to its generalization capacity, is able to cope with a certain range of variability of skeletal muscle properties.

Neural-Based Compensation of Nonlinearities in an Airplane Longitudinal Model with Dynamic-Inversion Control

Directory of Open Access Journals (Sweden)

YanBin Liu

2017-01-01

Full Text Available The inversion design approach is a very useful tool for the complex multiple-input-multiple-output nonlinear systems to implement the decoupling control goal, such as the airplane model and spacecraft model. In this work, the flight control law is proposed using the neural-based inversion design method associated with the nonlinear compensation for a general longitudinal model of the airplane. First, the nonlinear mathematic model is converted to the equivalent linear model based on the feedback linearization theory. Then, the flight control law integrated with this inversion model is developed to stabilize the nonlinear system and relieve the coupling effect. Afterwards, the inversion control combined with the neural network and nonlinear portion is presented to improve the transient performance and attenuate the uncertain effects on both external disturbances and model errors. Finally, the simulation results demonstrate the effectiveness of this controller.

Neural Networks for Self-tuning Control Systems