The use of neural networks is permeating every area of signal processing. They can provide powerful means for solving many problems, especially in nonlinear, real-time, adaptive, and blind signal processing. The Handbook of Neural Network Signal Processing brings together applications that were previously scattered among various publications to provide an up-to-date, detailed treatment of the subject from an engineering point of view.
The authors cover basic principles, modeling, algorithms, architectures, implementation procedures, and well-designed simulation examples of audio, video, speech, communication, geophysical, sonar, radar, medical, and many other signals. The subject of neural networks and their application to signal processing is constantly improving. You need a handy reference that will inform you of current applications in this new area. The Handbook of Neural Network Signal Processing provides this much needed service for all engineers and scientists in the field.
Table of Contents
PART I - NEURAL NETWORK FUNDAMENTS
Introduction to Artificial Neural Network for Signal Processing, Yu Hen Hu and Jenq-Neng Hwang
Multilayer Perceptrons, M.T. Manry
Radial Basis Networks, Andrew Back
Support Vector Machine, Klaus-Robert Muller
Committee Machines, Volker Tresp
PART II - NEURAL NETWORK SOLUTIONS TO STATISTICAL SIGNAL PROCESSING PROBLEMS
Applications of ANN to Nonlinear Signal Processing, Jose Principe
Applications of ANN to Blind Deconvolution and Source Separation, Scott C. Douglas
Adaptive Principle Component Analysis, Konstantinos I. Diamantaras
Applications of ANN to System Identification, Jan Larsen
Applications of ANN to Time Series Prediction, Li Zhong Wu
PART III - SIGNAL PROCESSING APPLICATIONS USING NEURAL NETWORKS
Applications of ANN to Speech Processing, Shigeru Katagiri
Applications of ANN to Video Signal Processing, Ling Guan
Applications of ANN to Biomedical Signal Processing, Tulay Adali and Yue Joseph Wang
Hierarchical Fuzzy Neural Networks for Pattern Classification, Jinshiuh Taur, Sung-Yuan Kung, and Shang Hung Lin.
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