Product details:
| ISBN13: | 9781857286731 |
| ISBN10: | 1857286731 |
| Binding: | Hardback |
| No. of pages: | 248 pages |
| Size: | 234x156 mm |
| Weight: | 498 g |
| Language: | English |
| 0 |
Category:
Combinatorics and graph theory
Electrical engineering and telecommunications, precision engineering
Energy industry
System organization
Environmental sciences
Discrete mathematics
Combinatorics and graph theory (charity campaign)
Electrical engineering and telecommunications, precision engineering (charity campaign)
Energy industry (charity campaign)
System organization (charity campaign)
Environmental sciences (charity campaign)
Discrete mathematics (charity campaign)
An Introduction to Neural Networks
Edition number: 1
Publisher: CRC Press
Date of Publication: 5 August 1997
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Short description:
Although mathematical ideas underpin the study of neural networks, this book presents the fundamentals without the full mathematical apparatus. The author tackles virtually all aspects of the field, including artificial neurons as models of their real counterparts; the geometry of network action in pattern space; gradient descent methods; associative memory and Hopfield nets; and self-organization and feature maps. The book provides a concrete focus through several real-world examples. This feature broadens the book's audience to include both students and professionals in cognitive science, psychology, and computer science as well as those involved in the design, construction, and management of networks.
Long description:
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.
Table of Contents:
Neural Net - A Preliminary Discussion. The von Neumann Machine and The Symbolic Paradigm. Real Neurons - A Review. Artificial neurons. Non- binary signal communication. Introducing Time. Network Features. Alternative Node Types. Cubic Nodes and Reward. Penalty Training. Drawing Things Together - Some Perspectives.
