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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.
Yu Hen Hu, Jenq-Neng Hwang
NEURAL NETWORK FUNDAMENTS. Introduction to Artificial Neural Network for Signal Processing. Multilayer Perceptrons. Radial Basis Networks. Support Vector Machine. Committee Machines. NEURAL NETWORK SOLUTIONS TO STATISTICAL SIGNAL PROCESSING PROBLEMS. Applications of ANN to Nonlinear Signal Processing. Applications of ANN to Blind Deconvolution and Source Separation. Adaptive Principle Component Analysis. Applications of ANN to System Identification. Applications of ANN to Time Series Prediction. SIGNAL PROCESSING APPLICATIONS USING NEURAL NETWORK. Applications of ANN to Speech Processing. Applications of ANN to Video Signal Processing. Applications of ANN to Biomedical Signal Processing. Hierarchical Fuzzy Neural Networks for Pattern Classification.
