Neural Networks: An Introduction (Physics of Neural Networks) [Hardcover]

Berndt Müller (Author), Joachim Reinhardt (Author)

Book Description

Publication Date: October 17, 1990 | ISBN-10: 3540523804 | ISBN-13: 978-3540523802

The concepts of neural-network models and techniques of parallel distributed processing are comprehensively presented in a three-step approach: - After a brief overview of the neural structure of the brain and the history of neural-network modeling, the reader is introduced to "neural" information processing, i.e. associative memory, perceptrons, feature-sensitive networks, learning strategies, and practical applications. - Part 2 covers more advanced subjects such as spin glasses, the mean-field theory of the Hopfield model, and the space of interactions in neural networks. - The self-contained final part discusses seven programs that provide practical demonstrations of neural-network models and their learning strategies. Ample opportunity is given to improve and modify the source codes. The software is included on a 5 1/4 inch MS DOS diskette and can be run using Borland's TURBO C 2.0 compiler, the Microsoft C compiler (5.0), or compatible compilers.

Editorial Reviews

Review

"I have enjoyed using the previous edition of this well-known book both as a personal text and as a class manual. Although it claims to be only an introduction, it contains a wealth of material and addresses real problems in physics." Computing Reviews --This text refers to an out of print or unavailable edition of this title.

From the Back Cover

Neural Networks The concepts of neural-network models and techniques of parallel distributed processing are comprehensively presented in a three-step approach: - After a brief overview of the neural structure of the brain and the history of neural-network modeling, the reader is introduced to associative memory, preceptrons, feature-sensitive networks, learning strategies, and practical applications. - The second part covers more advanced subjects such as the statistical physics of spin glasses, the mean-field theory of the Hopfield model, and the "space of interactions" approach to the storage capacity of neural networks. - In the self-contained final part, seven programs that provide practical demonstrations of neural-network models and their learning strategies are discussed. The software is included on a 3 1/2-inch MS-DOS diskette. The source code can be modified using Borland's TURBO-C 2.0 compiler, the Microsoft C compiler (5.0), or compatible compilers. --This text refers to an out of print or unavailable edition of this title.

Product Details

• Hardcover: 266 pages
• Publisher: Springer (October 17, 1990)
• Language: English
• ISBN-10: 3540523804
• ISBN-13: 978-3540523802
• Average Customer Review: 5.0 out of 5 stars  See all reviews (1 customer review)
• Amazon Best Sellers Rank: #3,984,797 in Books (See Top 100 in Books)

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Customer Reviews

Most Helpful Customer Reviews

2 of 2 people found the following review helpful:

5.0 out of 5 stars Perfect for beginners, March 29, 2001

By 

Roberto Castro Alamino (Sao Paulo, Sao Paulo Brazil) - See all my reviews

This review is from: Neural Networks: An Introduction (Physics of Neural Networks) (Paperback)

This book have the basics of neural networks (NN) treated at undergraduate level adequated to students of physics, mathematics or engineering. The relevant concepts are all explained and it recquires no previous knowledge in the area from the reader. It is a perfect introduction to the theory of neural networks for those who are iniciating in the field and have no experience with NN.