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Neural Networks: A Comprehensive Foundation, by Simon Haykin
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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.
- Sales Rank: #389723 in Books
- Published on: 1994-01
- Original language: English
- Number of items: 1
- Dimensions: 10.25" h x 7.25" w x 1.25" l,
- Binding: Hardcover
- 716 pages
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.
- 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.
Most helpful customer reviews
46 of 46 people found the following review helpful.
Well written and fairly comprehensive
By David Elder
Haykin's book is probably the most comprehensive compendium of traditional neural network theory currently available. I say "traditional" because historically neural networks developed within the field of computer science, only loosely inspired by actual neuroscience. Feedforward networks, backpropagation, self-organizing maps, PCA, and hierarchical machines fit into this traditional lineage. A second branch of neural networks, inspired more heavily by biology, have sought to model brain function and structure. Within this camp are network models such as adaptive resonance theory (ART), BCS/FCS, integrate-and-fire models, and a variety of others. Though this second branch of neural network theory has applications in pattern recognition, image processing, clustering, etc., Haykin barely mentions it. In other words, Haykin presents the material that computer scientists and engineers want to see, but skimps on the more biological side of the field. That being said, the material covered in Haykin is very well-presented, with clear mathematical notation and typesetting throughout. The book is accessible to graduates and advanced undergraduates. It should be on the shelf of every serious researcher, though workers in the biological sciences may want supplementary material. Computer scientists, mathematicians, and other engineers will not be disappointed at all.
19 of 21 people found the following review helpful.
Very Mathematical
By Sidhant
I used this as a textbook for a Neural Networks course I did in the second year of my undergraduate program in Mathematics and Computing.
My mathematical background till that point of time comprised Linear Algebra and upper level Calculus. This being rather 'limited' mathematical exposure, I found the book quite difficult to follow. It becomes harder when you are expected to convert the mathematical equations into working programs (without using tool-boxes or libraries, i.e.). The end-of-chapter exercises are pretty hard, and try to go beyond what the text talks about, most undergraduates may not be able be able to appreciate that. I think this is an excellent reference book for those who are pretty comfortable with Math. For undergraduates doing a first course in Neural Networks, I strongly recommend Timothy Masters' "Practical Neural Network recipes in C++". The math there is manageable, and yes, it comes with working code to make your life easier.
10 of 10 people found the following review helpful.
Theoretically Great
By A Customer
I found this book to be an excellent "research" reference. It's mathematical presentation is rigorous and provides good (up-to-date)theoretical foundation for the experienced scientist/engineer. Saying this, it is not a good book for the beginner especially when one only wants to know the general physical meaning of neural networks and where it is best applied.
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