No prior knowledge of neural networks assumed
Uses current examples to give good overall knowledge of developments in the field
Part of the attractive Grassroots series
PHIL PICTON is a Reader in Engineering Control Systems at Nene College in 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 patten recognition, intelligent control and logic design.
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.
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Neural Networks provides a gentle introduction to the subject, for undergraduates from Computer Science and Electrical Engineering degrees.
This updated and revised second edition assumes no prior knowledge and sets out to describe what neural nets are, what they do, and how they do it. The main networks covered include ADALINE, WISARD, the Hopfield Network, Bidirectional Associative Memory, the Boltzmann machine, counter-propogation and ART networks, and Kohonen's self-organizing maps. These networks are discussed by means of examples, giving the reader a good overall knowledge of current developments in the field.
Preface.- Introduction.- ADALINE.- Perceptrons.- Boolean Neural Networks.- Associative Memory and Feedback Networks.- Statistical Neural Networks.- Self-organizing Networks.- Neural Networks in Control Engineering.- Threshold Logic.- Implementation.- Conclusions.- Appendix A Derivation of the Delta Rule.- References.
"Neural Networks" provides a gentle introduction to the subject, for undergraduates from computer science and electrical engineering degrees. This updated and revised second edition assumes no prior knowledge and sets out to describe what neural nets are, what they do, and how they do it.
Über den Autor
P.D. Picton
Inhaltsverzeichnis
Preface.- Introduction.- ADALINE.- Perceptrons.- Boolean Neural Networks.- Associative Memory and Feedback Networks.- Statistical Neural Networks.- Self-organizing Networks.- Neural Networks in Control Engineering.- Threshold Logic.- Implementation.- Conclusions.- Appendix A Derivation of the Delta Rule.- References.
Klappentext
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.
No prior knowledge of neural networks assumed
Uses current examples to give good overall knowledge of developments in the field
Part of the attractive Grassroots series
