Neural Networks: A Comprehensive Foundation [Hardcover]

Simon Haykin (Author)

Book Description

ISBN-10: 0023527617 | ISBN-13: 978-0023527616 | Publication Date: January 1994

This book presents the first comprehensive treatment of neural networks from an engineering perspective. Thorough, well-organized, and completely up-to-date, it examines all the important aspects of this emerging technology.

Editorial Reviews

From the Publisher

This text represents the first comprehensive treatment of neural networks from an engineering perspective. Thorough, well-organized, and completely up-to-date, it examines all the important aspects of this emerging technology. Neural Networks provides broad coverage of the subject, including the learning process, back propogation, radial basis functions, recurrent networks, self-organizing systems, modular networks, temporal processing, neurodynamics, and VLSI implementations. Chapter objectives, computer experiments, problems, worked examples, a bibliography, photographs, illustrations, and a thorough glossary reinforce key concepts. The author's concise and fluid writing style makes the material more accessible.

From the Back Cover

Renowned for its thoroughness and readability, this well-organized and completely up-to-date text remains the most comprehensive treatment of neural networks from an engineering perspective. Thoroughly revised.

NEW TO THIS EDITION

• NEW—New chapters now cover such areas as:
• Support vector machines.
• Reinforcement learning/neurodynamic programming.
• Dynamically driven recurrent networks.
• NEW-End—of-chapter problems revised, improved and expanded in number.
FEATURES
• Extensive, state-of-the-art coverage exposes the reader to the many facets of neural networks and helps them appreciate the technology's capabilities and potential applications.
• Detailed analysis of back-propagation learning and multi-layer perceptrons.
• Explores the intricacies of the learning process—an essential component for understanding neural networks.
• Considers recurrent networks, such as Hopfield networks, Boltzmann machines, and meanfield theory machines, as well as modular networks, temporal processing, and neurodynamics.
• Integrates computer experiments throughout, giving the opportunity to see how neural networks are designed and perform in practice.
• Reinforces key concepts with chapter objectives, problems, worked examples, a bibliography, photographs, illustrations, and a thorough glossary.
• Includes a detailed and extensive bibliography for easy reference.
• Computer-oriented experiments distributed throughout the book
• Uses Matlab SE version 5.
--This text refers to an out of print or unavailable edition of this title.

Product Details

• Hardcover: 716 pages
• Publisher: Macmillan Coll Div (January 1994)
• Language: English
• ISBN-10: 0023527617
• ISBN-13: 978-0023527616
• Product Dimensions: 9.9 x 6.9 x 1.2 inches
• Shipping Weight: 2.9 pounds
• Average Customer Review: 4.1 out of 5 stars  See all reviews (23 customer reviews)
• Amazon Best Sellers Rank: #614,345 in Books (See Top 100 in Books)
  • #84 in Books > Computers & Technology > Computer Science > Artificial Intelligence > Neural Networks

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Customer Reviews

Most Helpful Customer Reviews

64 of 68 people found the following review helpful:

5.0 out of 5 stars Good detail with rigorous mathematics, July 12, 2001

By 

Dr. Lee D. Carlson (Baltimore, Maryland USA) - See all my reviews
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This review is from: Neural Networks: A Comprehensive Foundation (2nd Edition) (Hardcover)

This book, excellent for self-study and for use as a textbook, covers a subject that has had enormous impact in science and technology. One can say with confidence that neural networks will increase in importance in the decades ahead, especially in the field of artificial intelligence. The book is a comprehensive overview, and does take some time to read and digest, but it is worth the effort, as there are many applications of neural networks and the author is detailed in his discussion.

In the first part of the book, the author introduces neural networks and modeling brain functions. A good overview of the modeling of neural networks and knowledge representation is given, along with a discussion of how they are used in artificial intelligence. Ideas from computational learning are introduced, as well as the important concept of the Vapnik-Chervonenkis (VC) dimension. The VC dimension is defined in this book in terms of the maximum number of training examples that a machine can learn without errors. The author shows it to be a useful parameter, and allows one to avoid the difficult problem of finding an exact formula for the growth function of a hypothesis space.

In the next part of the book, the author discusses learning machines that have a teacher. The single-layer perceptron is introduced and shown to have an error-correction learning algorithm that is convergent. There is a fine discussion of optimization techniques and Bayes classifiers in this part. The least-mean-square algorithm is generalized to the back-propagation algorithm in order to train multi-layer perceptrons along with a discussion on how to optimize its performance using heuristics. The author gives a detailed discussion of the limitations of back-propagation learning. In addition, the radial-basis function networks are introduced. Supervised learning is viewed as an ill-posed hypersurface reconstruction problem, which is then solved using regularization methods. Support vector machines are introduced as neural networks that arise from statistical learning theory considerations via the VC dimension. A summary is given of the differences between the different approaches in neural network learning machines. Committee machines, based on the divide and conquer algorithm, are also treated. Here the strategy is to divide the learning process into a number of experts, with the expectation that the collective efforts of these experts will more efficiently arrive at the solution.

The next part of the book introduces unsupervised learning machines. The ability of machines to discover useful information, such as patterns or features, in the input data is taken as an acid test for real intelligence. Hebbian learning via principal components analysis is discussed, along with competitive learning via self-organizing maps. The author uses computer simulations to illustrate the behavior of systems of neurons. Vector quantization is brought in as another supervised learning technique to fine tune the quality of the classifiers. Most interestingly, information-theoretic models are discussed with mutual information techniques used effectively as unsupervised learning algorithms. Some elementary but interesting examples of single neurons under the influence of noise are discussed in detail. The topic of Boltzmann machines is discussed also, and the physicists will find the treatment particularly fascinating, as it takes ideas from statistical mechanics and applies them to solve combinatorial optimization problems. Other more general statistical machines, such as Helmholtz machines, and mean-field theoretic approaches are discussed also. Reinforcement learning, using dynamic programming techniques is treated in detail.

The book ends with a treatment of nonlinear dynamical techniques to study the behavior of neural networks. The discussion makes use of many examples and computer experiments, and there are some good exercises at the end of the chapters for further analysis. Dynamical systems employing short-term memory and feedforward are discussed along with a treatment of stability in nonlinear dynamical systems. Feedback mechanisms are used to obtain input-output mapping. The definition of chaos is very weak, as it only employs the positivity of a Lyapunov exponent, but this is suitable for the purposes in the book.

The applications of neural networks are vast and space prevents here a comprehensive list. I have found them an excellent tool to study load balancing algorithms in distributed computing and networks, modeling of mobile communications, options pricing, and computational biology. There are also dozens of companies that specialize solely in neural network algorithms. No doubt as new learning algorithms are discovered and computers become faster, neural networks will play a major role in creating independent, autonomous, intelligent machines. This book will give the reader a solid understanding of how neural networks fit into the computational learning paradigm.

26 of 28 people found the following review helpful:

5.0 out of 5 stars I wish all books were like this., March 6, 2002

By 

Luke Palmer (Boulder, CO United States) - See all my reviews
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This review is from: Neural Networks: A Comprehensive Foundation (2nd Edition) (Hardcover)

Extremely concise, extremely complete. Every new page has a new concept or method. In the first chapter, I knew more than I did after reading two other books I bought on the subject.
I would suggest, however, not to use this as an introduction. It's a bit more rigorous mathematically than some others, so use it if you understand the concepts first. It will shine new insight onto the concepts you already know, but it will probably fail at teaching them to you from the ground up.
I suggest this for the experienced Artificial Intelligence experimenter.
And for the love of god, use Perl for your test programs! Writing C++ classes for artificial intelligence is wholly impractical!

15 of 16 people found the following review helpful:

2.0 out of 5 stars Need practical examples., July 24, 2005

By 

Itzcoatl Muoz Martinez (Mexico) - See all my reviews
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This review is from: Neural Networks: A Comprehensive Foundation (2nd Edition) (Hardcover)

About theory, the book is good, but; it needs more practical or numerical examples, in order to get the information understandable.
There are too many concepts and ideas that without a good example, it is very hard to assimilate.
Also the computer oriented experiments in matlab, do not use the
neural network tool box, so it is not possible to get the gap to convert knowledge into computer code.
If these two recommendations are improved in a next edition, the book will become and excellent one.

thanks,