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eBook Adaptive Filter Theory (3rd Edition) ePub

eBook Adaptive Filter Theory (3rd Edition) ePub

by Simon Haykin

  • ISBN: 013322760X
  • Category: Engineering
  • Subcategory: Engineering
  • Author: Simon Haykin
  • Language: English
  • Publisher: Prentice Hall; 3rd edition (December 27, 1995)
  • Pages: 989
  • ePub book: 1239 kb
  • Fb2 book: 1356 kb
  • Other: rtf mbr lrf azw
  • Rating: 4.8
  • Votes: 646


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by Simon O. Haykin (Author). ISBN-13: 978-0130901262.

Simon S. Haykin Simon Haykin. Download (djvu, 1. 7 Mb) Donate Read.

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Redes Neurais Simon Haykin. Multiple-Input Multiple-Output Channel Models: Theory and Practice (Adaptive and Learning Systems for Signal Processing, Communications and Control Series). Nelson Costa, Simon Haykin. Категория: Образование.

Simon O. Haykin, McMaster University, Ontario Canada. adds a new chapter on Tracking Time-Varying Systems. adds two new chapters on Neural Networks. enhances material on RLS algorithms.

13. Finite-Precision Effects. Background and Overview. 14. Tracking of Time-Varying Systems. 16. Blind Deconvolution. 17. Back-Propagation Learning. Appendix A. Complex Variables. Appendix B. Differentiation with Respect to a Vector. 1. Stochastic Processes and Models. 3. Linear Prediction. 4. Method of Steepest Descent.

Represents the most comprehensive treatment available of neural networks from an engineering perspective. Examines all the important aspects of this emerging technology. DLC: Adaptive filters.


Kifer Kifer
This book is perfect for professionals!
It has very detailed tables on how to implement the algorithms which makes it really easy to implement them.
Many people complain on the mathematical nature of the book, which I feel is a strong point and is required for a proper understanding of the algorithms. Though students may find this a drawback as it does not provide an application based approach. It is more for professionals who want a book that helps them quickly implement the algos and provides a lot of mathematical background, which is necessary to diagnose issues and understand proper usages and requirements.
The comparison of algos is also very useful and helps one in making decisions on the right one for the task.
Halloween Halloween
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.
Soustil Soustil
The item arrived on time and the quality of product is good. No any problem can be found. Fantastic seller.
Coirad Coirad
Wrathshaper Wrathshaper
It is new but not original. The quality of the paper is not so good. But it matches the price.
Kulwes Kulwes
I'm sorry but I'm not happy to pay almost (+-$150) + shipping and taxes (+$24) for a damaged book (Clearly not a cheap book), I bought the product like two weeks ago and today when I went to look for the package and I opened the box I found one of the books was damaged. First the corners damaged then the cover with details. The box was not the problem Seem the one who packed the books didn't take care about it. I wanted a new book not a "condition: good" book in that case I would bought a n used and cheaper book. Next time please cover the book with plastic to avoid damaging the product.
Gaeuney Gaeuney
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.
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