An Introduction to Neural Networks

Front Cover
CRC Press, Dec 16, 2003 - Computers - 234 pages
Though mathematical ideas underpin the study of neural networks, the author presents the fundamentals without the full mathematical apparatus. All aspects of the field are tackled, including artificial neurons as models of their real counterparts; the geometry of network action in pattern space; gradient descent methods, including back-propagation; associative memory and Hopfield nets; and self-organization and feature maps. The traditionally difficult topic of adaptive resonance theory is clarified within a hierarchical description of its operation. The book also includes several real-world examples to provide a concrete focus. This should enhance its appeal to those involved in the design, construction and management of networks in commercial environments and who wish to improve their understanding of network simulator packages. As a comprehensive and highly accessible introduction to one of the most important topics in cognitive and computer science, this volume should interest a wide range of readers, both students and professionals, in cognitive science, psychology, computer science and electrical engineering.
 

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Contents

Chapter One Neural networksan overview
1
Chapter Two Real and artificial neurons
5
Chapter Three TLUs linear separability and vectors
16
the perceptron rule
25
Chapter Five The delta rule
34
Chapter Six Multilayer nets and backpropagation
41
the Hopfield net
57
Chapter Eight Selforganization
70
ART
89
further alternatives
101
Chapter Eleven Taxonomies contexts and hierarchies
117
Appendix A The cosine function
128
References
130
Index
135
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