Evolutionary Learning Algorithms for Neural Adaptive Control (Perspectives in Neural Computing)

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. This book has been designed for a final year undergraduate course in stochastic processes. It will also be suitable for mathematics undergraduates and others with interest in probability and stochastic processes, who wish to study on their own. The main prerequisite is probability theory: probability measures, random variables, expectation, independence, conditional probability, and the laws of large numbers. The only other prerequisite is calculus. This covers limits, series, the notion of continuity, differentiation and the Riemann integral. Familiarity with the Lebesgue integral would be a bonus. A certain level of fundamental mathematical experience, such as elementary set theory, is assumed implicitly. Throughout the book the exposition is interlaced with numerous exercises, which form an integral part of the course. Complete solutions are provided at the end of each chapter. Also, each exercise is accompanied by a hint to guide the reader in an informal manner. This feature willbe particularly useful for self-study and may be of help in tutorials. It also presents a challenge for the lecturer to involve the students as active participants in the course. This Book Is A Final Year Undergraduate Text On Stochastic Processes, A Tool Used Widely By Statisticians And Researchers Working In The Mathematics Of Finance. The Book Will Give A Detailed Treatment Of Conditional Expectation And Probability, A Topic Which In Principle Belongs To Probability Theory, But Is Essential As A Tool For Stochastic Processes. Although The Book Is A Final Year Text, The Author Has Chosen To Use Exercises As The Main Means Of Explanation For The Various Topics, And The Book Will Have A Strong Self-study Element. The Author Has Concentrated On The Major Topics Within Stochastic Analysis: Stochastic Processes, Markov Chains, Spectral Theory, Renewal Theory, Martingales And Itô Stochastic Processes. Preliminaries -- Stochastic Processes: Case Studies -- Markov Chains -- Spectral Theory Of Stationary Processes -- Renewal Theory -- Martingales -- Itô Stochastic Processes. Zdzisław Brzeźniak And Tomasz Zastawniak. Includes Bibliographical References And Index. After an introduction to neural networks and genetic algorithms, this volume describes in detail how neural networks and evolutionary techniques (specifically genetic algorithms and genetic programming) can be applied to the adaptive control of complex dynamic systems (including chaotic ones). A number of examples are presented and useful tips are given for the application of the techniques described. The fundamentals of dynamic systems theory and classical adaptive control are also given. This volume will be of particular interest to undergraduate and postgraduate students taking courses in neural networks, genetic algorithms or control systems, researchers in neural networks and genetic algorithms who need to extend their field of application to dynamic systems and control, and control theorists/professionals who would like to use these advanced learning techniques for solving high-nonlinear control theory problems "This book is a final year undergraduate text on stochastic processes, a tool used widely by statisticians and researchers working, for example, in the mathematics of finance. The book will give a detailed treatment of conditional expectation and probability, a topic which is essential as a tool for stochastic processes. Although the book is a final year text, the authors have chosen to use exercises as the main means of explanation for the various topics, hence the course has a strong self-study element. The authors have concentrated on major topics within stochastic analysis: martingales in discrete time and their convergence, Markov chains, stochastic processes in continuous time, with emphasis on the Poisson process and Brownian motion, as well as Ito stochastic calculus including stochastic differential equations."--Jacket Front Matter....Pages i-xi Introduction....Pages 1-4 Dynamic Systems and Control....Pages 5-21 The Attitude Control Problem....Pages 23-46 Artificial Neural Networks....Pages 47-70 Neuromodels of Dynamic Systems....Pages 71-96 Current Neurocontrol Techniques....Pages 97-109 Genetic Algorithms....Pages 111-131 Adaptive Control Architecture....Pages 133-163 Conclusions and the Future....Pages 165-167 Back Matter....Pages 169-211 This advanced textbook 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 cannot provide satisfactory results. It focuses on the principles involved, rather than on the modelling of the applications themselves, and aims to provide the reader with an introduction to the fundamental issues involved Stochastic processes are tools used widely by statisticians and researchers working in the mathematics of finance. This book for self-study provides a detailed treatment of conditional expectation and probability, a topic that in principle belongs to probability theory, but is essential as a tool for stochastic processes. The book centers on exercises as the main means of explanation. Cd-rom Includes A Collection Of Graphics And Animations On Mathematical Topics As Well As Programs For Design Of New Examples.