Neural Networks, Second Edition
provides a complete introduction to neural networks. It describes what they are, what they can do, and how they do it. While some scientific background is assumed, the reader is not expected to have any prior knowledge of neural networks. These networks are explained and discussed by means of examples, so that by the end of the book the reader will have a good overall knowledge of developments right up to current work in the field.
- Updated and expanded second edition
- Main networks covered are: feedforward networks such as the multilayered perceptron, Boolean networks such as the WISARD, feedback networks such as the Hopfield network, statistical networks such as the Boltzmann machine and Radial-Basis function networks, and self-organising networks such as Kohonen's self-organizing maps. Other networks are referred to throughout the text to give historical interest and alternative architectures
- The applications discussed will appeal to student engineers and computer scientists interested in character recognition, intelligent control and threshold logic. The final chapter looks at ways of implementing a neural network, including electronic and optical systems
This book is suitable for undergraduates from Computer Science and Electrical Engineering Courses who are taking a one module course on neural networks, and for researchers and computer science professionals who need a quick introduction to the subject.
PHIL PICTON is Professor of Intelligent Computer Systems at University College Northampton. Prior to this he was a lecturer at the Open University where he contributed to distance learning courses on control engineering, electronics, mechatronics and artificial intelligence. His research interests include pattern recognition, intelligent control and logic design.