Transit Time Estimation by Artificial Neural Networks
The use of interactive activation and competition (IAC) and backpropagation (BP) artificial neural networks (ANNs) for transit time estimation has been investigated in this piece of research. Owing to its competitive nature, the IAC ANN has been found abl
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George D. Smith Nigel C. Steele Rudolf F. Albrecht Artificial Neural Nets and Genetic Algorithms Proceedings of the International Conference in Norwich, U.K., 1997
Springer-Verlag Wien GmbH
Dr. George D. Smith School of Information Systems University of East Anglia, Norwieh, U.K.
Dr. Nigel C. Steele Division of Mathematics School of Mathematical and Information Sciences Coventry University, Coventry, u.K.
Dr. Rudolf F. Albrecht Institut für Informatik Universität Innsbruck, Innsbruck, Austria
This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned, specifieally those of translation, reprinting, re-use of illustrations, broadcasting, reproduction by photocopying machines or similar means, and storage in data banks. © 1998 Springer-Verlag Wien Originally published by Springer-Verlag Wien 1998
Camera-ready copies provided by authors and editors Graphie design: Ecke Bonk Printed on acid-free and chlorine-free bleached paper SPIN 10635776
With 384 Figures
ISBN 978-3-211-83087-1 ISBN 978-3-7091-6492-1 (eBook) DOI 10.1007/978-3-7091-6492-1
Preface
This is the third in a series of conferences devoted primarily to the theory and applications of artificial neural networks and genetic algorithms. The first such event was held in Innsbruck, Austria, in April 1993, the second in Ales, France, in April 1995. We are pleased to host the 1997 event in the mediaeval city of Norwich, England, and to carryon the fine tradition set by its predecessors of providing a relaxed and stimulating environment for both established and emerging researchers working in these and other, related fields. This series of conferences is unique in recognising the relation between the two main themes of artificial neural networks and genetic algorithms, each having its origin in a natural process fundamental to life on earth, and each now well established as a paradigm fundamental to continuing technological development through the solution of complex, industrial, commercial and financial problems. This is well illustrated in this volume by the numerous applications of both paradigms to new and challenging problems. The third key theme of the series, therefore, is the integration of both technologies, either through the use of the genetic algorithm to construct the most effective network architecture for the problem in hand, or, more recently, the use of neural networks as approximate fitness functions for a genetic algorithm searching for good solutions in an 'incomplete' solution space, i.e. one for which the fitness is not easily established for every possible solution instance. Turning to the contributions, of particular interest is the number of contributions devoted to the development of 'modular' neural networks, where a divide and conquer approach is adopted and each module is trained to solve a part of the problem. Contributions also abound in the field of robotics and, in particular, evolutionary robotics, in which the controllers are adapted through the use of some evolutio
