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Foundations of Wavelet Networks and Applications, by S. Sitharama Iyengar, V.V. Phoha
Free PDF Foundations of Wavelet Networks and Applications, by S. Sitharama Iyengar, V.V. Phoha
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Traditionally, neural networks and wavelet theory have been two separate disciplines, taught separately and practiced separately. In recent years the offspring of wavelet theory and neural networks-wavelet networks-have emerged and grown vigorously both in research and applications. Yet the material needed to learn or teach wavelet networks has remained scattered in various research monographs.
Foundations of Wavelet Networks and Applications unites these two fields in a comprehensive, integrated presentation of wavelets and neural networks. It begins by building a foundation, including the necessary mathematics. A transitional chapter on recurrent learning then leads to an in-depth look at wavelet networks in practice, examining important applications that include using wavelets as stock market trading advisors, as classifiers in electroencephalographic drug detection, and as predictors of chaotic time series. The final chapter explores concept learning and approximation by wavelet networks.
The potential of wavelet networks in engineering, economics, and social science applications is rich and still growing. Foundations of Wavelet Networks and Applications prepares and inspires its readers not only to help ensure that potential is achieved, but also to open new frontiers in research and applications.
- Sales Rank: #7415016 in Books
- Brand: Brand: Chapman and Hall/CRC
- Published on: 2002-06-27
- Original language: English
- Number of items: 1
- Dimensions: 9.25" h x 6.25" w x .75" l, 1.17 pounds
- Binding: Hardcover
- 288 pages
- Used Book in Good Condition
Review
"This book reviews both the theory of some kinds of wavelet networks and a number of applications . The book is self-contained, as it contains both some mathematical preliminaries and a review of fundamentals about wavelets as well as neural networks. Moreover, at the end of each chapter it contains a number of exercises useful to help the reader to verify the degree of his/her understanding . The book is highly recommended to all those looking for new methods in neural networks devoted to signal analysis." - Mathematical Reviews, Issue 2005d
Most helpful customer reviews
1 of 2 people found the following review helpful.
Disorganized, messy and helpful
By S. Coughlin
Having worked my way up to a level of expertise in computational mathematics where only obscure journal articles and academic compendiums can add anything to my knowledge, I find myself rarely being challenged. This book presented one, though not in the way I would hope.
A certain level of forgiveness for not having English as ones mother tongue should be granted for international works such as this, but this is beyond condoning. The unnecessary spelling and grammatical errors are punctuated only with incorrectly labeled graphs and incomplete and embarrassingly mistyped equations. The organization of knowledge presentation is abysmal, as entire concepts are referenced before later being introduced. But however frustrating the text itself may be, for a review of the application of this arcane topic, there is none better. The first seven chapters regarding the foundations of wavelets and neural networks are worthless; the last three justify the book's existence. The useful chapters cover important areas of use for wavenets, describing applicability and limitations, and even venture into cost calculations though stochastic learning (a rare gem indeed). Missing are critical references to base function selection, online updating, or optimization.
Overall, this book is not for the inexperienced. Ignore the first half of the book entirely, and read the latter selectively, if forgivingly.
0 of 3 people found the following review helpful.
boring; no worth your money
By H. C. Ming
Save yourself time and money and don't buy this book! This is an extremely boring book and I don't suggest it. Big waste of money! It is just common sense about wavelet and neural networks nothing new.
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