K. Warwick – författare

Neural networks are an exciting technology of growing importance in real industrial situations, particularly in control and systems. This book aims to give a detailed appreciation of the use of neural nets in these applications; it is aimed particularly at those with a control or systems background who wish to gain an insight into the technology in the context of real applications.The book introduces a wide variety of network types, including Kohenen nets, n-tuple nets and radial basis function networks, as well as the more usual multi-layer perception back-propagation networks. It begins by describing the basic principles and some essential design features, then goes on to examine in depth several application studies illustrating a range of advanced approaches to the topic.

This text is an extremely useful guide for those wishing to investigate the application of self-tuning control systems. The contents have been chosen in order to restrict the amount of theoretical detail to that necessary for explanation purposes, whilst application examples and programming suggestions are highlighted. The overall text is suitable for those wishing to gain the flavour of adaptive control, although those already familiar with selftuning techniques will find the problem solutions discussed to be most attractive. Parameter estimation, numerical solutions and software aspects are all considered at length, while simplified procedures and predictive self-tuning schemes are shown in terms of fundamental concepts.The level of the book is such that practising control engineers and postgraduate or final year control, engineering or cybernetics students would find it most suitable. A certain amount of basic digital/sampled data control systems terminology is assumed, although this does not mean that the reader needs to be familiar with more mathematical ideas, such as discrete transform methods or statistical theory. The text could be used as the foundation of a Masters course module, particularly if an emphasis is placed on the practical aspects of self-tuning control.The book is an ideal source for those interested in self-tuning and adaptive control, and contains many useful suggestions for future trends, research directions and implementation/ application possibilities.

This book provides an introduction to the techniques employed in the design and implementation of digital control systems. The text has arisen out of a highly successful IEE Vacation School held annually at Oxford University and the contents have been tailored to a digestible level and breadth by means of the feedback provided from previous School participants. As well as a tutorial approach to the description of basic concepts and general ideas used, most chapters include numerous worked examples and details of particular applications in order to enhance the theory introduced. The importance of putting over a technique by making use of practical examples is highlighted by the inclusion of several chapters in which selected case studies are presented detailing applications of both the more conventional and the more modern digital control ideas.The book is aimed at engineers, managers and engineering students who wish to become more widely informed about how they may employ the latest advances in digital control technology. To this end, earlier chapters concentrate on the theoretical framework of digital control, whereas later chapters concentrate on its implementation. This results in an up-to-date overview of digital control schemes which is an excellent reference text for industrialists and academics alike. This substantially revised 2nd edition (more than a third of the book is completely new) represents a distinct improvement on the extremely successful 1st edition, and bears witness to the constant modernisation required in such a rapidly changing area.

Recent rapid developments in computing power, such as parallel processing and neural networks, have stimulated new trends in control. However a discrepancy exists between available computing power and exploitable algorithms obtained classically from control theory. The aim of this book is to address the discrepancy from both the com­ putational power and control theory viewpoints. Areas such as identification, adaptive control, signal processing and neural networks therefore hold a prominent position in the text presented. The form of the book is such that it should be useful for readers at various levels, particularly those at the research and/or application stage. The book has resulted from the IFAC Workshop on the Mutual Impact of Computing Power and Control Theory, which was held at the Institute of Information Theory and Automation (UTIA), Prague, in September 1992. Organisation of the event was provided jointly by the Department of Adaptive Systems, UTIA, Prague and the School of Engineering and Information Sciences, University of Reading, UK. Selected papers from the Workshop have been chosen to give a good balance across the field, whilst at the same time highlighting important areas for future research. In this way the book represents edited Proceedings from the Workshop. One point, quickly apparent, is the international nature of the presentations themselves, which provide not only a technical appraisal of the field but also inject cultural aspects which are vitally important on the path ahead.