Non Linear Feedback Neural Networks

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Non Linear Feedback Neural Networks

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Author : Mohd. Samar Ansari
language : en
Publisher: Springer
Release Date : 2013-09-03

Non Linear Feedback Neural Networks written by Mohd. Samar Ansari and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-09-03 with Technology & Engineering categories.

This book aims to present a viable alternative to the Hopfield Neural Network (HNN) model for analog computation. It is well known the standard HNN suffers from problems of convergence to local minima, and requirement of a large number of neurons and synaptic weights. Therefore, improved solutions are needed. The non-linear synapse neural network (NoSyNN) is one such possibility and is discussed in detail in this book. This book also discusses the applications in computationally intensive tasks like graph coloring, ranking, and linear as well as quadratic programming. The material in the book is useful to students, researchers and academician working in the area of analog computation.

Differential Neural Networks For Robust Nonlinear Control

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Author : Alexander S. Poznyak
language : en
Publisher: World Scientific
Release Date : 2001

Differential Neural Networks For Robust Nonlinear Control written by Alexander S. Poznyak and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001 with Computers categories.

This book deals with continuous time dynamic neural networks theory applied to the solution of basic problems in robust control theory, including identification, state space estimation (based on neuro-observers) and trajectory tracking. The plants to be identified and controlled are assumed to be a priori unknown but belonging to a given class containing internal unmodelled dynamics and external perturbations as well. The error stability analysis and the corresponding error bounds for different problems are presented. The effectiveness of the suggested approach is illustrated by its application to various controlled physical systems (robotic, chaotic, chemical, etc.).

Neural Network Control Of Nonlinear Discrete Time Systems

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Author : Jagannathan Sarangapani
language : en
Publisher: CRC Press
Release Date : 2018-10-03

Neural Network Control Of Nonlinear Discrete Time Systems written by Jagannathan Sarangapani and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-10-03 with Technology & Engineering categories.

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.

Nonlinear H2 H Infinity Constrained Feedback Control

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Author : Murad Abu-Khalaf
language : en
Publisher: Springer
Release Date : 2010-10-21

Nonlinear H2 H Infinity Constrained Feedback Control written by Murad Abu-Khalaf and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-10-21 with Technology & Engineering categories.

This book provides techniques to produce robust, stable and useable solutions to problems of H-infinity and H2 control in high-performance, non-linear systems for the first time. The book is of importance to control designers working in a variety of industrial systems. Case studies are given and the design of nonlinear control systems of the same caliber as those obtained in recent years using linear optimal and bounded-norm designs is explained.

Neural Network Control Of Robot Manipulators And Non Linear Systems

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Author : F W Lewis
language : en
Publisher: CRC Press
Release Date : 1998-11-30

Neural Network Control Of Robot Manipulators And Non Linear Systems written by F W Lewis and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1998-11-30 with Technology & Engineering categories.

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.

Springer Handbook Of Computational Intelligence

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Author : Janusz Kacprzyk
language : en
Publisher: Springer
Release Date : 2015-05-28

Springer Handbook Of Computational Intelligence written by Janusz Kacprzyk and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-05-28 with Technology & Engineering categories.

The Springer Handbook for Computational Intelligence is the first book covering the basics, the state-of-the-art and important applications of the dynamic and rapidly expanding discipline of computational intelligence. This comprehensive handbook makes readers familiar with a broad spectrum of approaches to solve various problems in science and technology. Possible approaches include, for example, those being inspired by biology, living organisms and animate systems. Content is organized in seven parts: foundations; fuzzy logic; rough sets; evolutionary computation; neural networks; swarm intelligence and hybrid computational intelligence systems. Each Part is supervised by its own Part Editor(s) so that high-quality content as well as completeness are assured.

Advances In Neural Networks Isnn 2004

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Author : Fuliang Yin
language : en
Publisher: Springer
Release Date : 2011-04-07

Advances In Neural Networks Isnn 2004 written by Fuliang Yin and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-04-07 with Computers categories.

This book constitutes the proceedings of the International Symposium on Neural N- works (ISNN 2004) held in Dalian, Liaoning, China duringAugust 19–21, 2004. ISNN 2004 received over 800 submissions from authors in ?ve continents (Asia, Europe, North America, South America, and Oceania), and 23 countries and regions (mainland China, Hong Kong, Taiwan, South Korea, Japan, Singapore, India, Iran, Israel, Turkey, Hungary, Poland, Germany, France, Belgium, Spain, UK, USA, Canada, Mexico, - nezuela, Chile, andAustralia). Based on reviews, the Program Committee selected 329 high-quality papers for presentation at ISNN 2004 and publication in the proceedings. The papers are organized into many topical sections under 11 major categories (theo- tical analysis; learning and optimization; support vector machines; blind source sepa- tion,independentcomponentanalysis,andprincipalcomponentanalysis;clusteringand classi?cation; robotics and control; telecommunications; signal, image and time series processing; detection, diagnostics, and computer security; biomedical applications; and other applications) covering the whole spectrum of the recent neural network research and development. In addition to the numerous contributed papers, ?ve distinguished scholars were invited to give plenary speeches at ISNN 2004. ISNN 2004 was an inaugural event. It brought together a few hundred researchers, educators,scientists,andpractitionerstothebeautifulcoastalcityDalianinnortheastern China. It provided an international forum for the participants to present new results, to discuss the state of the art, and to exchange information on emerging areas and future trends of neural network research. It also created a nice opportunity for the participants to meet colleagues and make friends who share similar research interests.

Artificial Neural Networks For Modelling And Control Of Non Linear Systems

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Author : Johan A.K. Suykens
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06

Artificial Neural Networks For Modelling And Control Of Non Linear Systems written by Johan A.K. Suykens and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Technology & Engineering categories.

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 NLqemTheory, that have applications in control theory, system theory, circuit theory and Time Series Analysis.

Proceedings Of The 5th International Conference On Electrical Engineering And Control Applications Volume 2

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Author : Salim Ziani
language : en
Publisher: Springer Nature
Release Date : 2024-09-02

Proceedings Of The 5th International Conference On Electrical Engineering And Control Applications Volume 2 written by Salim Ziani and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-09-02 with Technology & Engineering categories.

This book gathers papers presented during the 5th International Conference on Electrical Engineering and Control Applications (ICEECA 2022), held on November, 15–17, 2022, Khenchela, Algeria. It covers new control system models, troubleshooting tips, and complex system requirements, such as increased speed, precision, and remote capabilities. Additionally, the book discusses not only the engineering aspects of signal processing and various practical issues in the broad field of information transmission, but also novel technologies for communication networks and modern antenna design. The later part of the book covers important related topics such as fault diagnosis and fault-tolerant control strategies for nonlinear systems and alternative energy sources. This book is intended for researchers, engineers, and advanced postgraduate students in the fields of control and electrical engineering, computer science, signal processing, as well as mechanical and chemical engineering.

Neural Network Control Of Robot Manipulators And Non Linear Systems

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Author : F W Lewis
language : en
Publisher: CRC Press
Release Date : 2020-08-13

Neural Network Control Of Robot Manipulators And Non Linear Systems written by F W Lewis and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-08-13 with Technology & Engineering categories.

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.