Neural Systems For Control

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Neural Systems for Control

Omid Omidvar,David L. Elliott
Neural Systems for Control Book PDF

Author : Omid Omidvar,David L. Elliott
Publisher : Elsevier
Page : 358 pages
File Size : 41,8 Mb
Release : 1997-02-24
Category : Computers
ISBN : 0080537391

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Neural Systems for Control by Omid Omidvar,David L. Elliott Pdf

Control problems offer an industrially important application and a guide to understanding control systems for those working in Neural Networks. Neural Systems for Control represents the most up-to-date developments in the rapidly growing aplication area of neural networks and focuses on research in natural and artifical neural systems directly applicable to control or making use of modern control theory. The book covers such important new developments in control systems such as intelligent sensors in semiconductor wafer manufacturing; the relation between muscles and cerebral neurons in speech recognition; online compensation of reconfigurable control for spacecraft aircraft and other systems; applications to rolling mills, robotics and process control; the usage of past output data to identify nonlinear systems by neural networks; neural approximate optimal control; model-free nonlinear control; and neural control based on a regulation of physiological investigation/blood pressure control. All researchers and students dealing with control systems will find the fascinating Neural Systems for Control of immense interest and assistance. Focuses on research in natural and artifical neural systems directly applicable to contol or making use of modern control theory Represents the most up-to-date developments in this rapidly growing application area of neural networks Takes a new and novel approach to system identification and synthesis

Neural Networks for Control

W. Thomas Miller,Richard S. Sutton,Paul J. Werbos
Neural Networks for Control Book PDF

Author : W. Thomas Miller,Richard S. Sutton,Paul J. Werbos
Publisher : MIT Press
Page : 548 pages
File Size : 48,5 Mb
Release : 1995
Category : Computers
ISBN : 026263161X

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Neural Networks for Control by W. Thomas Miller,Richard S. Sutton,Paul J. Werbos Pdf

Neural Networks for Control brings together examples of all the most important paradigms for the application of neural networks to robotics and control. Primarily concerned with engineering problems and approaches to their solution through neurocomputing systems, the book is divided into three sections: general principles, motion control, and applications domains (with evaluations of the possible applications by experts in the applications areas.) Special emphasis is placed on designs based on optimization or reinforcement, which will become increasingly important as researchers address more complex engineering challenges or real biological-control problems.A Bradford Book. Neural Network Modeling and Connectionism series

Artificial Neural Networks for Modelling and Control of Non-Linear Systems

Johan A.K. Suykens,Joos P.L. Vandewalle,B.L. de Moor
Artificial Neural Networks for Modelling and Control of Non Linear Systems Book PDF

Author : Johan A.K. Suykens,Joos P.L. Vandewalle,B.L. de Moor
Publisher : Springer Science & Business Media
Page : 242 pages
File Size : 46,9 Mb
Release : 2012-12-06
Category : Technology & Engineering
ISBN : 9781475724936

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Artificial Neural Networks for Modelling and Control of Non-Linear Systems by Johan A.K. Suykens,Joos P.L. Vandewalle,B.L. de Moor Pdf

Artificial neural networks possess several properties that make them particularly attractive for applications to modelling and control of complex non-linear systems. Among these properties are their universal approximation ability, their parallel network structure and the availability of on- and off-line learning methods for the interconnection weights. However, dynamic models that contain neural network architectures might be highly non-linear and difficult to analyse as a result. Artificial Neural Networks for Modelling and Control of Non-Linear Systems investigates the subject from a system theoretical point of view. However the mathematical theory that is required from the reader is limited to matrix calculus, basic analysis, differential equations and basic linear system theory. No preliminary knowledge of neural networks is explicitly required. The book presents both classical and novel network architectures and learning algorithms for modelling and control. Topics include non-linear system identification, neural optimal control, top-down model based neural control design and stability analysis of neural control systems. A major contribution of this book is to introduce NLq Theory as an extension towards modern control theory, in order to analyze and synthesize non-linear systems that contain linear together with static non-linear operators that satisfy a sector condition: neural state space control systems are an example. Moreover, it turns out that NLq Theory is unifying with respect to many problems arising in neural networks, systems and control. Examples show that complex non-linear systems can be modelled and controlled within NLq theory, including mastering chaos. The didactic flavor of this book makes it suitable for use as a text for a course on Neural Networks. In addition, researchers and designers will find many important new techniques, in particular NLq emTheory, that have applications in control theory, system theory, circuit theory and Time Series Analysis.

Neural Networks Modeling and Control

Jorge D. Rios,Alma Y. Alanis,Nancy Arana-Daniel,Carlos Lopez-Franco
Neural Networks Modeling and Control Book PDF

Author : Jorge D. Rios,Alma Y. Alanis,Nancy Arana-Daniel,Carlos Lopez-Franco
Publisher : Academic Press
Page : 0 pages
File Size : 50,5 Mb
Release : 2020-01-20
Category : Science
ISBN : 0128170786

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Neural Networks Modeling and Control by Jorge D. Rios,Alma Y. Alanis,Nancy Arana-Daniel,Carlos Lopez-Franco Pdf

Neural Networks Modelling and Control: Applications for Unknown Nonlinear Delayed Systems in Discrete Time focuses on modeling and control of discrete-time unknown nonlinear delayed systems under uncertainties based on Artificial Neural Networks. First, a Recurrent High Order Neural Network (RHONN) is used to identify discrete-time unknown nonlinear delayed systems under uncertainties, then a RHONN is used to design neural observers for the same class of systems. Therefore, both neural models are used to synthesize controllers for trajectory tracking based on two methodologies: sliding mode control and Inverse Optimal Neural Control. As well as considering the different neural control models and complications that are associated with them, this book also analyzes potential applications, prototypes and future trends.

Neural Network-Based Adaptive Control of Uncertain Nonlinear Systems

Kasra Esfandiari,Farzaneh Abdollahi,Heidar A. Talebi
Neural Network Based Adaptive Control of Uncertain Nonlinear Systems Book PDF

Author : Kasra Esfandiari,Farzaneh Abdollahi,Heidar A. Talebi
Publisher : Springer Nature
Page : 181 pages
File Size : 51,9 Mb
Release : 2021-06-18
Category : Technology & Engineering
ISBN : 9783030731366

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Neural Network-Based Adaptive Control of Uncertain Nonlinear Systems by Kasra Esfandiari,Farzaneh Abdollahi,Heidar A. Talebi Pdf

The focus of this book is the application of artificial neural networks in uncertain dynamical systems. It explains how to use neural networks in concert with adaptive techniques for system identification, state estimation, and control problems. The authors begin with a brief historical overview of adaptive control, followed by a review of mathematical preliminaries. In the subsequent chapters, they present several neural network-based control schemes. Each chapter starts with a concise introduction to the problem under study, and a neural network-based control strategy is designed for the simplest case scenario. After these designs are discussed, different practical limitations (i.e., saturation constraints and unavailability of all system states) are gradually added, and other control schemes are developed based on the primary scenario. Through these exercises, the authors present structures that not only provide mathematical tools for navigating control problems, but also supply solutions that are pertinent to real-life systems.

Fuzzy Neural Networks for Real Time Control Applications

Erdal Kayacan,Mojtaba Ahmadieh Khanesar
Fuzzy Neural Networks for Real Time Control Applications Book PDF

Author : Erdal Kayacan,Mojtaba Ahmadieh Khanesar
Publisher : Butterworth-Heinemann
Page : 264 pages
File Size : 54,5 Mb
Release : 2015-10-07
Category : Computers
ISBN : 9780128027035

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Fuzzy Neural Networks for Real Time Control Applications by Erdal Kayacan,Mojtaba Ahmadieh Khanesar Pdf

AN INDISPENSABLE RESOURCE FOR ALL THOSE WHO DESIGN AND IMPLEMENT TYPE-1 AND TYPE-2 FUZZY NEURAL NETWORKS IN REAL TIME SYSTEMS Delve into the type-2 fuzzy logic systems and become engrossed in the parameter update algorithms for type-1 and type-2 fuzzy neural networks and their stability analysis with this book! Not only does this book stand apart from others in its focus but also in its application-based presentation style. Prepared in a way that can be easily understood by those who are experienced and inexperienced in this field. Readers can benefit from the computer source codes for both identification and control purposes which are given at the end of the book. A clear and an in-depth examination has been made of all the necessary mathematical foundations, type-1 and type-2 fuzzy neural network structures and their learning algorithms as well as their stability analysis. You will find that each chapter is devoted to a different learning algorithm for the tuning of type-1 and type-2 fuzzy neural networks; some of which are: • Gradient descent • Levenberg-Marquardt • Extended Kalman filter In addition to the aforementioned conventional learning methods above, number of novel sliding mode control theory-based learning algorithms, which are simpler and have closed forms, and their stability analysis have been proposed. Furthermore, hybrid methods consisting of particle swarm optimization and sliding mode control theory-based algorithms have also been introduced. The potential readers of this book are expected to be the undergraduate and graduate students, engineers, mathematicians and computer scientists. Not only can this book be used as a reference source for a scientist who is interested in fuzzy neural networks and their real-time implementations but also as a course book of fuzzy neural networks or artificial intelligence in master or doctorate university studies. We hope that this book will serve its main purpose successfully. Parameter update algorithms for type-1 and type-2 fuzzy neural networks and their stability analysis Contains algorithms that are applicable to real time systems Introduces fast and simple adaptation rules for type-1 and type-2 fuzzy neural networks Number of case studies both in identification and control Provides MATLAB® codes for some algorithms in the book

Adaptive Control with Recurrent High-order Neural Networks

George A. Rovithakis,Manolis A. Christodoulou
Adaptive Control with Recurrent High order Neural Networks Book PDF

Author : George A. Rovithakis,Manolis A. Christodoulou
Publisher : Springer Science & Business Media
Page : 203 pages
File Size : 44,7 Mb
Release : 2012-12-06
Category : Computers
ISBN : 9781447107859

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Adaptive Control with Recurrent High-order Neural Networks by George A. Rovithakis,Manolis A. Christodoulou Pdf

The series Advances in Industrial Control aims to report and encourage technology transfer in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. New theory, new controllers, actuators, sensors, new industrial processes, computer methods, new applications, new philosophies ... , new challenges. Much of this development work resides in industrial reports, feasibility study papers and the reports of advanced collaborative projects. The series offers an opportunity for researchers to present an extended exposition of such new work in all aspects of industrial control for wider and rapid dissemination. Neural networks is one of those areas where an initial burst of enthusiasm and optimism leads to an explosion of papers in the journals and many presentations at conferences but it is only in the last decade that significant theoretical work on stability, convergence and robustness for the use of neural networks in control systems has been tackled. George Rovithakis and Manolis Christodoulou have been interested in these theoretical problems and in the practical aspects of neural network applications to industrial problems. This very welcome addition to the Advances in Industrial Control series provides a succinct report of their research. The neural network model at the core of their work is the Recurrent High Order Neural Network (RHONN) and a complete theoretical and simulation development is presented. Different readers will find different aspects of the development of interest. The last chapter of the monograph discusses the problem of manufacturing or production process scheduling.

Neural Control Engineering

Steven J. Schiff
Neural Control Engineering Book PDF

Author : Steven J. Schiff
Publisher : MIT Press
Page : 403 pages
File Size : 49,9 Mb
Release : 2022-11-01
Category : Medical
ISBN : 9780262546713

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Neural Control Engineering by Steven J. Schiff Pdf

How powerful new methods in nonlinear control engineering can be applied to neuroscience, from fundamental model formulation to advanced medical applications. Over the past sixty years, powerful methods of model-based control engineering have been responsible for such dramatic advances in engineering systems as autolanding aircraft, autonomous vehicles, and even weather forecasting. Over those same decades, our models of the nervous system have evolved from single-cell membranes to neuronal networks to large-scale models of the human brain. Yet until recently control theory was completely inapplicable to the types of nonlinear models being developed in neuroscience. The revolution in nonlinear control engineering in the late 1990s has made the intersection of control theory and neuroscience possible. In Neural Control Engineering, Steven Schiff seeks to bridge the two fields, examining the application of new methods in nonlinear control engineering to neuroscience. After presenting extensive material on formulating computational neuroscience models in a control environment—including some fundamentals of the algorithms helpful in crossing the divide from intuition to effective application—Schiff examines a range of applications, including brain-machine interfaces and neural stimulation. He reports on research that he and his colleagues have undertaken showing that nonlinear control theory methods can be applied to models of single cells, small neuronal networks, and large-scale networks in disease states of Parkinson's disease and epilepsy. With Neural Control Engineering the reader acquires a working knowledge of the fundamentals of control theory and computational neuroscience sufficient not only to understand the literature in this trandisciplinary area but also to begin working to advance the field. The book will serve as an essential guide for scientists in either biology or engineering and for physicians who wish to gain expertise in these areas.

Neural Networks for Identification, Prediction and Control

Duc T. Pham,Xing Liu
Neural Networks for Identification Prediction and Control Book PDF

Author : Duc T. Pham,Xing Liu
Publisher : Springer Science & Business Media
Page : 243 pages
File Size : 41,6 Mb
Release : 2012-12-06
Category : Technology & Engineering
ISBN : 9781447132448

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Neural Networks for Identification, Prediction and Control by Duc T. Pham,Xing Liu Pdf

In recent years, there has been a growing interest in applying neural networks to dynamic systems identification (modelling), prediction and control. Neural networks are computing systems characterised by the ability to learn from examples rather than having to be programmed in a conventional sense. Their use enables the behaviour of complex systems to be modelled and predicted and accurate control to be achieved through training, without a priori information about the systems' structures or parameters. This book describes examples of applications of neural networks In modelling, prediction and control. The topics covered include identification of general linear and non-linear processes, forecasting of river levels, stock market prices and currency exchange rates, and control of a time-delayed plant and a two-joint robot. These applications employ the major types of neural networks and learning algorithms. The neural network types considered in detail are the muhilayer perceptron (MLP), the Elman and Jordan networks and the Group-Method-of-Data-Handling (GMDH) network. In addition, cerebellar-model-articulation-controller (CMAC) networks and neuromorphic fuzzy logic systems are also presented. The main learning algorithm adopted in the applications is the standard backpropagation (BP) algorithm. Widrow-Hoff learning, dynamic BP and evolutionary learning are also described.

Neural Systems for Robotics

Omid Omidvar,Patrick van der Smagt
Neural Systems for Robotics Book PDF

Author : Omid Omidvar,Patrick van der Smagt
Publisher : Elsevier
Page : 346 pages
File Size : 50,9 Mb
Release : 2012-12-02
Category : Computers
ISBN : 9780080925097

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Neural Systems for Robotics by Omid Omidvar,Patrick van der Smagt Pdf

Neural Systems for Robotics represents the most up-to-date developments in the rapidly growing aplication area of neural networks, which is one of the hottest application areas for neural networks technology. The book not only contains a comprehensive study of neurocontrollers in complex Robotics systems, written by highly respected researchers in the field but outlines a novel approach to solving Robotics problems. The importance of neural networks in all aspects of Robot arm manipulators, neurocontrol, and Robotic systems is also given thorough and in-depth coverage. All researchers and students dealing with Robotics will find Neural Systems for Robotics of immense interest and assistance. Key Features * Focuses on the use of neural networks in robotics-one of the hottest application areas for neural networks technology * Represents the most up-to-date developments in this rapidly growing application area of neural networks * Contains a new and novel approach to solving Robotics problems

Stable Adaptive Neural Network Control

S.S. Ge,C.C. Hang,T.H. Lee,Tao Zhang
Stable Adaptive Neural Network Control Book PDF

Author : S.S. Ge,C.C. Hang,T.H. Lee,Tao Zhang
Publisher : Springer Science & Business Media
Page : 296 pages
File Size : 40,7 Mb
Release : 2013-03-09
Category : Science
ISBN : 9781475765779

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Stable Adaptive Neural Network Control by S.S. Ge,C.C. Hang,T.H. Lee,Tao Zhang Pdf

Recent years have seen a rapid development of neural network control tech niques and their successful applications. Numerous simulation studies and actual industrial implementations show that artificial neural network is a good candidate for function approximation and control system design in solving the control problems of complex nonlinear systems in the presence of different kinds of uncertainties. Many control approaches/methods, reporting inventions and control applications within the fields of adaptive control, neural control and fuzzy systems, have been published in various books, journals and conference proceedings. In spite of these remarkable advances in neural control field, due to the complexity of nonlinear systems, the present research on adaptive neural control is still focused on the development of fundamental methodologies. From a theoretical viewpoint, there is, in general, lack of a firmly mathematical basis in stability, robustness, and performance analysis of neural network adaptive control systems. This book is motivated by the need for systematic design approaches for stable adaptive control using approximation-based techniques. The main objec tives of the book are to develop stable adaptive neural control strategies, and to perform transient performance analysis of the resulted neural control systems analytically. Other linear-in-the-parameter function approximators can replace the linear-in-the-parameter neural networks in the controllers presented in the book without any difficulty, which include polynomials, splines, fuzzy systems, wavelet networks, among others. Stability is one of the most important issues being concerned if an adaptive neural network controller is to be used in practical applications.

Neural Network Applications in Control

George William Irwin,K. Warwick,Kenneth J. Hunt
Neural Network Applications in Control Book PDF

Author : George William Irwin,K. Warwick,Kenneth J. Hunt
Publisher : IET
Page : 320 pages
File Size : 40,7 Mb
Release : 1995
Category : Computers
ISBN : 0852968523

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Neural Network Applications in Control by George William Irwin,K. Warwick,Kenneth J. Hunt Pdf

The aim is to present an introduction to, and an overview of, the present state of neural network research and development, with an emphasis on control systems application studies. The book is useful to a range of levels of reader. The earlier chapters introduce the more popular networks and the fundamental control principles, these are followed by a series of application studies, most of which are industrially based, and the book concludes with a consideration of some recent research.

Adaptive Sliding Mode Neural Network Control for Nonlinear Systems

Yang Li,Jianhua Zhang,Wu Qiong
Adaptive Sliding Mode Neural Network Control for Nonlinear Systems Book PDF

Author : Yang Li,Jianhua Zhang,Wu Qiong
Publisher : Academic Press
Page : 186 pages
File Size : 49,8 Mb
Release : 2018-11-16
Category : Technology & Engineering
ISBN : 9780128154328

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Adaptive Sliding Mode Neural Network Control for Nonlinear Systems by Yang Li,Jianhua Zhang,Wu Qiong Pdf

Adaptive Sliding Mode Neural Network Control for Nonlinear Systems introduces nonlinear systems basic knowledge, analysis and control methods, and applications in various fields. It offers instructive examples and simulations, along with the source codes, and provides the basic architecture of control science and engineering. Introduces nonlinear systems' basic knowledge, analysis and control methods, along with applications in various fields Offers instructive examples and simulations, including source codes Provides the basic architecture of control science and engineering

Neural Network Control Of Robot Manipulators And Non-Linear Systems

F W Lewis,S. Jagannathan,A Yesildirak
Neural Network Control Of Robot Manipulators And Non Linear Systems Book PDF

Author : F W Lewis,S. Jagannathan,A Yesildirak
Publisher : CRC Press
Page : 468 pages
File Size : 46,9 Mb
Release : 2020-08-14
Category : Technology & Engineering
ISBN : 9781000162776

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Neural Network Control Of Robot Manipulators And Non-Linear Systems by F W Lewis,S. Jagannathan,A Yesildirak Pdf

There has been great interest in "universal controllers" that mimic the functions of human processes to learn about the systems they are controlling on-line so that performance improves automatically. Neural network controllers are derived for robot manipulators in a variety of applications including position control, force control, link flexibility stabilization and the management of high-frequency joint and motor dynamics. The first chapter provides a background on neural networks and the second on dynamical systems and control. Chapter three introduces the robot control problem and standard techniques such as torque, adaptive and robust control. Subsequent chapters give design techniques and Stability Proofs For NN Controllers For Robot Arms, Practical Robotic systems with high frequency vibratory modes, force control and a general class of non-linear systems. The last chapters are devoted to discrete- time NN controllers. Throughout the text, worked examples are provided.

Neural Network Control of Nonlinear Discrete-Time Systems

Jagannathan Sarangapani
Neural Network Control of Nonlinear Discrete Time Systems Book PDF

Author : Jagannathan Sarangapani
Publisher : CRC Press
Page : 624 pages
File Size : 49,7 Mb
Release : 2018-10-03
Category : Technology & Engineering
ISBN : 9781420015454

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Neural Network Control of Nonlinear Discrete-Time Systems by Jagannathan Sarangapani Pdf

Intelligent systems are a hallmark of modern feedback control systems. But as these systems mature, we have come to expect higher levels of performance in speed and accuracy in the face of severe nonlinearities, disturbances, unforeseen dynamics, and unstructured uncertainties. Artificial neural networks offer a combination of adaptability, parallel processing, and learning capabilities that outperform other intelligent control methods in more complex systems. Borrowing from Biology Examining neurocontroller design in discrete-time for the first time, Neural Network Control of Nonlinear Discrete-Time Systems presents powerful modern control techniques based on the parallelism and adaptive capabilities of biological nervous systems. At every step, the author derives rigorous stability proofs and presents simulation examples to demonstrate the concepts. Progressive Development After an introduction to neural networks, dynamical systems, control of nonlinear systems, and feedback linearization, the book builds systematically from actuator nonlinearities and strict feedback in nonlinear systems to nonstrict feedback, system identification, model reference adaptive control, and novel optimal control using the Hamilton-Jacobi-Bellman formulation. The author concludes by developing a framework for implementing intelligent control in actual industrial systems using embedded hardware. Neural Network Control of Nonlinear Discrete-Time Systems fosters an understanding of neural network controllers and explains how to build them using detailed derivations, stability analysis, and computer simulations.