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Traditionally, neural networks and wavelet theory have been two separate disciplines, taught separately and practiced separately. Foundations of Wavelet Networks and Applications unites these two fields to provide a comprehensive, integrated presentation of wavelets and neural networks that forms a self-contained treatment of wavelet networks that requires minimal prerequisites. Focusing on establishing insight and understanding rather than rigorous mathematical foundations, it prepares and inspires readers not only to help ensure that the potential of wavelet networks is achieved, but also to open new frontiers in research and applications. Each chapter includes exercises.
List of contents
PART A: Mathematical Preliminaries. Wavelets. Neural Networks. Wavelet Networks. PART B: Recurrent Learning. Separating Order from Disorder. Radial Wavelet Neural Networks. Predicting Chaotic Time Series. Concept Learning. Bibliography. Index.
About the author
S. Sitharama Iyengar, S. Sitharama Iyengar, V.V. Phoha
Summary
Featues wavelets and neural networks. This book contains a chapter on recurrent learning that looks 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.
Additional text
"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
