Foundations of Wavelet Networks and Applications

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CRC Press, Jun 27, 2002 - Mathematics - 288 pages
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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.
 

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Contents

Mathematical Preliminaries
3
Wavelets
41
Neural Networks
61
Wavelet Networks
107
II
129
Separating Order from Disorder
145
Radial Wavelet Neural Networks
155
Predicting Chaotic Time Series
171
Concept Learning
205
Bibliography
223
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