Foundations of Wavelet Networks and Applications

S. Sitharama Lyengar, E.C. Cho, Vir V. Phoha

278 pages, parution le 28/11/2002

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Résumé

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.

Provides the first comprehensive, self-contained treatment of wavelet networks
Focuses on establishing insight and understanding rather than rigorous mathematical foundations
Presents a variety of neural architectures to inspire new ideas and build a foundation for adapting wavelets to still other neural architectures
Includes in each chapter exercises sets that help solidify understanding
Contains a full bibliography that forms a rich resource for further explorations

Contents

PART A

Mathematical Preliminaries
Wavelets
Neural Networks
Wavelets Networks

PART B

Recurrent Learning
Separating Order From Disorder
Radial Wavelet Neural Networks
Predicting Chaotic Time Series
Concept Learning

L'auteur - S. Sitharama Lyengar

Louisiana State University, Baton Rouge, Louisiana, USA

L'auteur - E.C. Cho

Kentucky State University, Frankfort, Kentucky, USA

Caractéristiques techniques

	PAPIER
Éditeur(s)	Chapman and Hall / CRC
Auteur(s)	S. Sitharama Lyengar, E.C. Cho, Vir V. Phoha
Parution	28/11/2002
Nb. de pages	278
Format	16 x 24
Couverture	Relié
Poids	528g
Intérieur	Noir et Blanc
EAN13	9781584882749
ISBN13	978-1-58488-274-9