Intelligent Signal Processing / Edition 1by Simon Haykin
IEEE Press is proud to present the first selected reprint volume devoted to the new field of intelligent signal processing (ISP). ISP differs fundamentally from the classical approach to statistical signal processing in that it models the input-out-put behavior of a complex system by using "intelligent" or "model-free" techniques rather than relying on the… See more details below
IEEE Press is proud to present the first selected reprint volume devoted to the new field of intelligent signal processing (ISP). ISP differs fundamentally from the classical approach to statistical signal processing in that it models the input-out-put behavior of a complex system by using "intelligent" or "model-free" techniques rather than relying on the shortcomings of a mathematical model. ISP systems extract information from incoming signal and noise data and makes few assumptions about the statistical structure of signals and their environment. Intelligent Signal Processing explores how ISP tools address the problems of practical neural systems, new signal data, and blind fuzzy approximators. The editors have compiled 20 articles written by prominent researchers covering diverse practical applications of this nascent topic, exposing the reader to the signal processing power of learning and adaptive systems. This essential reference is intended for researchers, professional engineers, and scientists working in statistical signal processing and its applications in various fields such as humanistic intelligence, stochastic resonance, financial markets, noise processing optimization, pattern recognition, signal detection, speech processing, and sensor fusion. Intelligent Signal Processing is also invaluable for graduate students and academics with a background in computer science, computer engineering, or electrical engineering.
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Table of Contents
List of Contributors.
Humanistic Intelligence: "Wear Comp" As a New Framework and Application for Intelligent Signal Processing.
Adaptive Stochastic Resonance.
Learning in the Presence of Noise.
Incorporating Prior Information in Machine Learning by Creating Virtual Examples.
Deterministic Annealing for Clustering, Compression, Classification, Regression, and Speech recognition.
Local Dynamic Modeling with Self-Organizing Maps and Applications to Nonlinear System Identification and Control.
A Signal Processing Framework Based on Dynamic Neural Networks with Application to Problems in Adaptation, Filtering and Classification.
Semiparametric Support Vector Machines for Nonlinear Model Estimation.
Gradient-Based Learning Applied to Document Recognition.
Pattern Recognition Using A Family of Design Algorithms Based Upon Generalized Probabilistic Descent Method.
An Approach to Adaptive Classification.
Reduced-Rank Intelligent Signal Processing with Application to Radar.
Signal Detection in a Nonstationary Environment Reformulated as an Adaptive Pattern Classification Problem.
Data Representation Using Mixtures of Principal Components.
Image Denoising by Sparse Code Shrinkage.
About the Editors.
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