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am 24. Juli 2000
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.
0Kommentar| 2 Personen fanden diese Informationen hilfreich. War diese Rezension für Sie hilfreich?JaNeinMissbrauch melden
am 24. Juli 2000
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.
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am 25. Februar 2000
Before start reading this book, read "Uderstanding Digital Signal Processing" by Lyons first.
0Kommentar| 2 Personen fanden diese Informationen hilfreich. War diese Rezension für Sie hilfreich?JaNeinMissbrauch melden
am 19. April 1998
The text is well organized the overview which is given in the beginning of the book is very usefull and it provides an excellent explaination to stochastic signal processing. the background material is a suitable training for those who want to refresh their knowledge in stochastic signals. The second part explains in detail all the prelimanary subjects in statistical signal processing. All the rest of the book is just what any dsp engineer needs as far as theory is concern. What is a bit missing is an interface to the practical world (maybe a few examples of the algorithms which are introduced in the book implemented on of the shelf dsp cores) but anyone can achieve staff like this in manufacturer benchmarks. In short great book
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am 1. Februar 2001
The book describes the mathematical theory of different adaptive filters. It is great for researchers, who are interested in the theoretical background of the filters and it is not of the type "implement and run". Knowledge in statistics/probability of signals and some basics in "normal" digital filters are useful.
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am 23. Mai 2013
Die Erklärungen sind sehr gut und detailreich geschildert, aber
ich habe mir mehr praktische Anwendungsbeispiele erwartet (bzw. mehr praxisnahe Anwendungen, welche auch
wirklich in der Wirtschaft verwendet werden). Ist aber sehr sehr gut mit anderen Büchern kombinierbar.
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am 30. Oktober 1999
Simon Haykin's Adaptive Filter Theory is a perfect example of everything which is wrong with modern engineering text books. It begs the question of why great educators like Van Trees and Kailath write out of print books, and Sallys like Haykin, Kay and Papoulis can make it to third editions. As a cookbook, one can't do better, but then again, what is science without proper motivation? Even when Haykin attempts to illustrate the theory, he adopts the uninsightful but straightforward mechanics of undergraduate linear algebra, and then again, why did it take hundreds of years to develop these ideas if Haykin has a 3 line proof. The book should be re-titled "Why I'm so smart" By Simon Haykin.
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