Adaptive Filter Theory (5th Edition) 5th Edition by Simon O. Haykin (PDF)

Description

<> Adaptive Filter Theory, 5e, is ideal 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 fifth 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.

User’s Reviews

Editorial Reviews: About the Author Simon Haykin received his B.Sc. (First-class Honours), Ph.D., and D.Sc., all in Electrical Engineering from the University of Birmingham, England. He is a Fellow of the Royal Society of Canada, and a Fellow of the Institute of Electrical and Electronics Engineers. He is the recipient of the Henry Booker Gold Medal from URSI, 2002, the Honorary Degree of Doctor of Technical Sciences from ETH Zentrum, Zurich, Switzerland, 1999, and many other medals and prizes.He is a pioneer in adaptive signal-processing with emphasis on applications in radar and communications, an area of research which has occupied much of his professional life.

Reviews from Amazon users which were colected at the time this book was published on the website:

⭐a great book for adaptive filters. I like the fact that a large part of the book is appendices that review the math. Anyone can understand Hykin’s explanations.The only thing missing the the neural net stuff that was in the 4th edition.

⭐The item arrived on time and the quality of product is good. No any problem can be found. Fantastic seller.

⭐It is new but not original. The quality of the paper is not so good. But it matches the price.

⭐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. I have to admit that the notation used in this book is very, very poor and can be a source of frustration. The dependency is also a pain because you always have to keep flipping 100 pages back because Mr. Haykin prefers to say “Eqn. (4.24)” instead of “an AR model”.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.

⭐I have always wondered why many people have negative opinions about books by Simon Haykin, whether it is ‘Communication Systems’ or ‘Adaptive Filter Theory’. Particularly, this book ‘Adaptive Filter Theory’, in my opinion, is one of the bestbooks on this subject. As Julius Kusuma correctly mentioned, this book is indeed an “adventure ride” into the field of Adaptive Filter Theory.I discovered this book when I was doing a class project on Self-Orthogonalizing algorithms for Adaptive Beamforming and I felt that all the relevant information that I needed was present in this book. I did’nt really feel the neccesity to refer anything outside this book.Apart from that, this book contains everything that a graduate student needs to know about this exciting field of adaptive filters. The author assumes some background on Random Signal Theory… I’d suggest to look up Sam Shanmugan et al’s, “Random Signals: Detection, Estimation and Data Analysis” before beginning to read (enjoy) this “adventure ride” on Adaptive Filters.

⭐Before start reading this book, read “Uderstanding Digital Signal Processing” by Lyons first.

⭐A good book for Adaptive Signal Processing. The book starts with basics of Stochastic Processes and then discusses various adaptive algorithms like Steepest Descent, LMS, NLMS, RLS and has some material on Back-propagation learning.

⭐Die Erklärungen sind sehr gut und detailreich geschildert, aberich habe mir mehr praktische Anwendungsbeispiele erwartet (bzw. mehr praxisnahe Anwendungen, welche auchwirklich in der Wirtschaft verwendet werden). Ist aber sehr sehr gut mit anderen Büchern kombinierbar.Author discusses every key point of the Adaptive filters and different methods , strategies , examples are discussed.Categories of filters and very basic knowledge are given.

⭐Good

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