Evolutionary Learning Algorithms For Neural Adaptive Control

Author : Dimitris C. Dracopoulos
Publisher : Springer
Page : 214 pages
File Size : 54,8 Mb
Release : 2013-12-21
Category : Computers
ISBN : 9781447109037

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Evolutionary Learning Algorithms for Neural Adaptive Control by Dimitris C. Dracopoulos Pdf

Evolutionary Learning Algorithms for Neural Adaptive Control is an advanced textbook, which investigates how neural networks and genetic algorithms can be applied to difficult adaptive control problems which conventional results are either unable to solve , or for which they can not provide satisfactory results. It focuses on the principles involved, rather than on the modelling of the applications themselves, and therefore provides the reader with a good introduction to the fundamental issues involved.

Author : Dimitris Dracopoulos
Publisher : Unknown
Page : 224 pages
File Size : 49,6 Mb
Release : 2014-09-01
Category : Electronic
ISBN : 144710904X

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Evolutionary Learning Algorithms for Neural Adaptive Control by Dimitris Dracopoulos Pdf

Author : P. Mars
Publisher : CRC Press
Page : 231 pages
File Size : 51,9 Mb
Release : 2018-01-18
Category : Technology & Engineering
ISBN : 9781351090872

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Learning Algorithms by P. Mars Pdf

Over the past decade, interest in computational or non-symbolic artificial intelligence has grown. The algorithms involved have the ability to learn from past experience, and therefore have significant potential in the adaptive control of signals and systems. This book focuses on the theory and applications of learning algorithms-stochastic learning automata; artificial neural networks; and genetic algorithms, evolutionary strategies, and evolutionary programming. Hybrid combinations of various algorithms are also discussed.Chapter 1 provides a brief overview of the topics discussed and organization of the text. The first half of the book (Chapters 2 through 4) discusses the basic theory of the learning algorithms, with one chapter devoted to each type. In the second half (Chapters 5 through 7), the emphasis is on a wide range of applications drawn from adaptive signal processing, system identification, and adaptive control problems in telecommunication networks.Learning Algorithms: Theory and Applications in Signal Processing, Control and Communications is an excellent text for final year undergraduate and first year graduate students in engineering, computer science, and related areas. Professional engineers and everyone involved in the application of learning techniques in adaptive signal processing, control, and communications will find this text a valuable synthesis of theory and practical application of the most useful algorithms.

Author : Lakhmi C. Jain,Clarence W. de Silva
Publisher : CRC Press
Page : 440 pages
File Size : 42,8 Mb
Release : 1998-12-29
Category : Computers
ISBN : 0849398053

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Intelligent Adaptive Control by Lakhmi C. Jain,Clarence W. de Silva Pdf

This book describes important techniques, developments, and applications of computational intelligence in system control. Chapters present: an introduction to the fundamentals of neural networks, fuzzy logic, and evolutionary computing a rigorous treatment of intelligent control industrial applications of intelligent control and soft computing, including transportation, petroleum, motor drive, industrial automation, and fish processing other knowledge-based techniques, including vehicle driving aid and air traffic management Intelligent Adaptive Control provides a state-of-the-art treatment of practical applications of computational intelligence in system control. The book cohesively covers introductory and advanced theory, design, implementation, and industrial use - serving as a singular resource for the theory and application of intelligent control, particularly employing fuzzy logic, neural networks, and evolutionary computing.

Author : Fouad Sabry
Publisher : One Billion Knowledgeable
Page : 164 pages
File Size : 41,5 Mb
Release : 2023-07-03
Category : Computers
ISBN : PKEY:6610000473953

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Intelligent Control by Fouad Sabry Pdf

What Is Intelligent Control The term "intelligent control" refers to a category of control methods that make use of a number of different artificial intelligence computing methodologies, including neural networks, Bayesian probability, fuzzy logic, machine learning, reinforcement learning, evolutionary computation, and genetic algorithms. How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: Intelligent Control Chapter 2: Artificial Intelligence Chapter 3: Machine Learning Chapter 4: Reinforcement Learning Chapter 5: Neural Network Chapter 6: Adaptive Control Chapter 7: Computational Intelligence Chapter 8: Outline of Artificial Intelligence Chapter 9: Machine Learning Control Chapter 10: Data-driven Model (II) Answering the public top questions about intelligent control. (III) Real world examples for the usage of intelligent control in many fields. (IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of intelligent control' technologies. Who This Book Is For Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of intelligent control.

Author : Phil Mars,J. R. Chen,Raghu Nambiar
Publisher : CRC Press
Page : 240 pages
File Size : 49,5 Mb
Release : 1996-10-15
Category : Technology & Engineering
ISBN : 0849378966

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Learning Algorithms by Phil Mars,J. R. Chen,Raghu Nambiar Pdf

Over the past decade, interest in computational or non-symbolic artificial intelligence has grown. The algorithms involved have the ability to learn from past experience, and therefore have significant potential in the adaptive control of signals and systems. This book focuses on the theory and applications of learning algorithms-stochastic learning automata; artificial neural networks; and genetic algorithms, evolutionary strategies, and evolutionary programming. Hybrid combinations of various algorithms are also discussed. Chapter 1 provides a brief overview of the topics discussed and organization of the text. The first half of the book (Chapters 2 through 4) discusses the basic theory of the learning algorithms, with one chapter devoted to each type. In the second half (Chapters 5 through 7), the emphasis is on a wide range of applications drawn from adaptive signal processing, system identification, and adaptive control problems in telecommunication networks. Learning Algorithms: Theory and Applications in Signal Processing, Control and Communications is an excellent text for final year undergraduate and first year graduate students in engineering, computer science, and related areas. Professional engineers and everyone involved in the application of learning techniques in adaptive signal processing, control, and communications will find this text a valuable synthesis of theory and practical application of the most useful algorithms.

Author : Gee Wah Ng
Publisher : Unknown
Page : 0 pages
File Size : 45,6 Mb
Release : 1997
Category : Adaptive control systems
ISBN : 0863802141

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Application of Neural Networks to Adaptive Control of Nonlinear Systems by Gee Wah Ng Pdf

This book investigates the ability of a neural network (NN) to learn how to control an unknown (nonlinear, in general) system, using data acquired on-line, that is during the process of attempting to exert control. Two algorithms are developed to train the neural network for real-time control applications. The first algorithm is known as Learning by Recursive Least Squares (LRLS) algorithm and the second algorithm is known as Integrated Gradient and Least Squares (IGLS) algorithm. The ability of these algorithms to train the NN controller for real-time control is demonstrated on practical applications and the local convergence and stability requirements of these algorithms are analysed. In addition, network topology, learning algorithms (particularly supervised learning) and neural network control strategies are presented.

Author : John R. Koza
Publisher : Morgan Kaufmann
Page : 1516 pages
File Size : 50,7 Mb
Release : 1999
Category : Computers
ISBN : 1558605436

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Genetic Programming III by John R. Koza Pdf

Genetic programming (GP) is a method for getting a computer to solve a problem by telling it what needs to be done instead of how to do it. Koza, Bennett, Andre, and Keane present genetically evolved solutions to dozens of problems of design, control, classification, system identification, and computational molecular biology. Among the solutions are 14 results competitive with human-produced results, including 10 rediscoveries of previously patented inventions.

Author : Gisbert Schneider
Publisher : CRC Press
Page : 169 pages
File Size : 42,5 Mb
Release : 2002-10-01
Category : Science
ISBN : 9781498713702

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Adaptive Systems in Drug Design by Gisbert Schneider Pdf

A brief history of drug design presented to make clear that there are fashions in this important field and that they change rather rapidly. This is due in part to the fact that the way that a new paradigm is accepted in a drug company often does not depend on its scientific merit alone.

Author : Iztok Fister,Iztok Fister Jr.
Publisher : Springer
Page : 242 pages
File Size : 55,8 Mb
Release : 2015-01-24
Category : Technology & Engineering
ISBN : 9783319144009

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Adaptation and Hybridization in Computational Intelligence by Iztok Fister,Iztok Fister Jr. Pdf

This carefully edited book takes a walk through recent advances in adaptation and hybridization in the Computational Intelligence (CI) domain. It consists of ten chapters that are divided into three parts. The first part illustrates background information and provides some theoretical foundation tackling the CI domain, the second part deals with the adaptation in CI algorithms, while the third part focuses on the hybridization in CI. This book can serve as an ideal reference for researchers and students of computer science, electrical and civil engineering, economy, and natural sciences that are confronted with solving the optimization, modeling and simulation problems. It covers the recent advances in CI that encompass Nature-inspired algorithms, like Artificial Neural networks, Evolutionary Algorithms and Swarm Intelligence –based algorithms.

Author : Duc T. Pham,Xing Liu
Publisher : Springer Science & Business Media
Page : 243 pages
File Size : 44,8 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.

Author : Lio, Pietro,Verma, Dinesh
Publisher : IGI Global
Page : 312 pages
File Size : 50,6 Mb
Release : 2011-08-31
Category : Computers
ISBN : 9781613500934

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Biologically Inspired Networking and Sensing: Algorithms and Architectures by Lio, Pietro,Verma, Dinesh Pdf

Biologically Inspired Networking and Sensing: Algorithms and Architectures offers current perspectives and trends in biologically inspired networking, exploring various approaches aimed at improving network paradigms. Research contained within this compendium of research papers and surveys introduces researches in the fields of communication networks, performance modeling, and distributed computing to new advances in networking.

Author : Simon Whiteson
Publisher : Springer Science & Business Media
Page : 127 pages
File Size : 54,8 Mb
Release : 2010-10-05
Category : Computers
ISBN : 9783642139314

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Adaptive Representations for Reinforcement Learning by Simon Whiteson Pdf

This book presents new algorithms for reinforcement learning, a form of machine learning in which an autonomous agent seeks a control policy for a sequential decision task. Since current methods typically rely on manually designed solution representations, agents that automatically adapt their own representations have the potential to dramatically improve performance. This book introduces two novel approaches for automatically discovering high-performing representations. The first approach synthesizes temporal difference methods, the traditional approach to reinforcement learning, with evolutionary methods, which can learn representations for a broad class of optimization problems. This synthesis is accomplished by customizing evolutionary methods to the on-line nature of reinforcement learning and using them to evolve representations for value function approximators. The second approach automatically learns representations based on piecewise-constant approximations of value functions. It begins with coarse representations and gradually refines them during learning, analyzing the current policy and value function to deduce the best refinements. This book also introduces a novel method for devising input representations. This method addresses the feature selection problem by extending an algorithm that evolves the topology and weights of neural networks such that it evolves their inputs too. In addition to introducing these new methods, this book presents extensive empirical results in multiple domains demonstrating that these techniques can substantially improve performance over methods with manual representations.

Author : Robert M. French,Jacques P. Sougne
Publisher : Springer Science & Business Media
Page : 327 pages
File Size : 49,8 Mb
Release : 2012-12-06
Category : Psychology
ISBN : 9781447102816

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Connectionist Models of Learning, Development and Evolution by Robert M. French,Jacques P. Sougne Pdf

Connectionist Models of Learning, Development and Evolution comprises a selection of papers presented at the Sixth Neural Computation and Psychology Workshop - the only international workshop devoted to connectionist models of psychological phenomena. With a main theme of neural network modelling in the areas of evolution, learning, and development, the papers are organized into six sections: The neural basis of cognition Development and category learning Implicit learning Social cognition Evolution Semantics Covering artificial intelligence, mathematics, psychology, neurobiology, and philosophy, it will be an invaluable reference work for researchers and students working on connectionist modelling in computer science and psychology, or in any area related to cognitive science.

Author : Thomas Duriez,Steven L. Brunton,Bernd R. Noack
Publisher : Springer
Page : 211 pages
File Size : 53,6 Mb
Release : 2016-11-02
Category : Technology & Engineering
ISBN : 9783319406244

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Machine Learning Control – Taming Nonlinear Dynamics and Turbulence by Thomas Duriez,Steven L. Brunton,Bernd R. Noack Pdf

This is the first textbook on a generally applicable control strategy for turbulence and other complex nonlinear systems. The approach of the book employs powerful methods of machine learning for optimal nonlinear control laws. This machine learning control (MLC) is motivated and detailed in Chapters 1 and 2. In Chapter 3, methods of linear control theory are reviewed. In Chapter 4, MLC is shown to reproduce known optimal control laws for linear dynamics (LQR, LQG). In Chapter 5, MLC detects and exploits a strongly nonlinear actuation mechanism of a low-dimensional dynamical system when linear control methods are shown to fail. Experimental control demonstrations from a laminar shear-layer to turbulent boundary-layers are reviewed in Chapter 6, followed by general good practices for experiments in Chapter 7. The book concludes with an outlook on the vast future applications of MLC in Chapter 8. Matlab codes are provided for easy reproducibility of the presented results. The book includes interviews with leading researchers in turbulence control (S. Bagheri, B. Batten, M. Glauser, D. Williams) and machine learning (M. Schoenauer) for a broader perspective. All chapters have exercises and supplemental videos will be available through YouTube.