Foundations of Wavelet Networks and Applications
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A01=S. Sitharama Iyengar
A01=V.V. Phoha
advanced pattern recognition
Author_S. Sitharama Iyengar
Author_V.V. Phoha
Autocovariance Sequence
Cal Error
Category=PBK
Chaotic Attractors
Chaotic Time Series
computational neuroscience
Dynamical Neural System
EEG Data
EEG Database
Embedding Dimension
Empirical Risk Minimization
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
Equilibrium Point
financial data modeling
Hebb's Rule
Hebb’s Rule
machine learning theory
McCulloch Pitts Model
Mother Wavelet
Network Coefficients
Nonlinear Discriminant Functions
Recurrent Neural Networks
Rox Im Ation
Scalar Time Series
signal processing methods
Stochastic Approximation Algorithms
time series prediction
Tr Ue
Translation Coefficients
Wavelet Analysis
Wavelet Decomposition
Wavelet Networks
Wavelet Neural Network
wavelet neural network applications
Product details
- ISBN 9781584882749
- Weight: 526g
- Dimensions: 156 x 234mm
- Publication Date: 27 Jun 2002
- Publisher: Taylor & Francis Inc
- Publication City/Country: US
- Product Form: Hardback
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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.
S. Sitharama Iyengar, S. Sitharama Iyengar, V.V. Phoha
