Adaptive Filter Theory (4th Edition) (Paperback)

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Key Phrases: excited subspace, lattice predictor, linear optimum filters, Least-Mean-Square Algorithm, Frequency-Domain Adaptive Filters, Recursive Least-Squares Algorithm (more...)

Editorial Reviews

Product Description

For courses in Adaptive Filters. Haykin examines both the mathematical theory behind various linear adaptive filters and the elements of supervised multilayer perceptrons. In its fourth edition, this highly successful book has been updated and refined to stay current with the field and develop concepts in as unified and accessible a manner as possible.

From the Publisher

Haykin examines both the mathematical theory behind various linear adaptive filters with finite-duration impulse response (FIR) and the elements of supervised neural networks. The Third Edition of this highly successful book has been updated and refined to keep current with the field and develop concepts in as unified and accessible a manner as possible. --This text refers to the Hardcover edition.

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Product Details

• Paperback: 936 pages
• Publisher: Prentice Hall; 4 edition (September 24, 2001)
• Language: English
• ISBN-10: 0130901261
• ISBN-13: 978-0130901262
• Product Dimensions: 9.3 x 7.2 x 1.6 inches
• Shipping Weight: 3.4 pounds (View shipping rates and policies)
• Average Customer Review: 3.8 out of 5 stars  See all reviews (11 customer reviews)
• Amazon.com Sales Rank: #243,070 in Books (See Bestsellers in Books)

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Customer Reviews

Most Helpful Customer Reviews

20 of 25 people found the following review helpful:

5.0 out of 5 stars Adventures in the development of stochastic DSP, July 23, 2000

By	Julius Kusuma (Cambridge, MA, USA) - See all my reviews (REAL NAME)

This review is from: Adaptive Filter Theory (3rd Edition) (Hardcover)

Despite the commonly negative opinion against Simon Haykin's book, I find this book to be a very fun reading. It starts off with a very brief review of DSP (more useful just for getting familiar with the notation, really), properties of random processes, and a small section on linear algebra in the middle of the book.

The rest of the book can be viewed as a story of how different approaches and algorithms were developed, and is a little difficult to use as reference due to its lack of structure and over-dependency on the previous chapters, both for technical content and notation.

But there's a lot of hidden treasures within this book that should have been more emphasized. For example, Mold's theorem that states that any discrete stationary process can be decomposed into a deterministic component and a random component, which are uncorrelated to each other. I'm sorry, but a reference to a proof in another book is not enough to really motivate me. This is a very fundamental theorem if you're interested in stochastic signal processing. Sure, you don't cover the Fundamental Theorem of Calculus in your very first calculus class, but then again this is supposed to be a fairly advanced book.

So if you're interested in learning certain things quickly, this is NOT the book to get. Consider Munson Hayes' book instead. Save this one when you feel like investing a little time to hear Haykin's story on stochastic signal processing.

2 of 2 people found the following review helpful:

3.0 out of 5 stars A Recipe Book, May 31, 2008

By	Canaima "cartel2" - See all my reviews

The book is sound, but I have to agree with others here. Formulas, procedures are presented without an intuitive sense of why things turn out the way they are, or even from the beginning of derivations. Good to implement mechanically all those algorithms without proper understanding.

As it is usually the case, in this very important subject, one has to learn from many sources.

6 of 8 people found the following review helpful:

2.0 out of 5 stars Not a bad reference book., June 16, 2004

By	Farzad Etemadi "farzad3" (Aliso Viejo, Ca USA) - See all my reviews

This book looks very impressive, but if you try to understand it you'll find it very mechanical. There is not much motivation behind the many pages of formulas and derivations. I'm not even sure how many people actually read those derivations becuase even in its 4th edition the book and its solution manual both have many typos (see, for example, equations 8.11 and 12.5). Even the problems are more focused on derivations than on numerical examples. This is a good cookbook if you just want to implement an algorithm or find some pointers to the original research papers. Like many other reviewers, I beleive that engineering textbooks are losing their depth and becoming more and more like instruction manuals.