Intelligent Signal Processing

"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 the input-output behavior of a complex system is modeled by using "intelligent" or "model-free" techniques, rather than relying on the shortcomings of a mathematical model. Information is extracted from incoming signal and noise data, making 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 15 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, 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.

About the Editors

Simon Haykin is the founding director of the Communications Research Laboratory at McMaster University, Hamilton, Ontario, Canada, where he serves as university professor. His research interests include nonlinear dynamics, neural networks and adaptive filters and their applications in radar and communications systems. Dr. Haykin is the editor for a series of books on "Adaptive and Learning Systems for Signal Processing, Communications and Control" (Publisher) and is both an IEEE Fellow and Fellow of the Royal Society of Canada.

Bart Kosko is a past director of the University of Southern California's (USC) Signal and Image Processing Institute. He has authored several books, including Neural Networks and Fuzzy Systems, Neural Networks for Signal Processing (Publisher, copyright date) and Fuzzy Thinking (Publisher, copyright date), as well as the novel Nanotime (Publisher, copyright date). Dr. Kosko is an elected governor of the International Neural Network Society and has chaired many neural and fuzzy system conferences. Currently, he is associate professor of electrical engineering at USC."

Simon Haykin is University Professor at McMaster University, Hamilton, Ontario, Canada. His research interests include nonlinear dynamics, neural networks and adaptive filters and their applications in radar and communications systems. Dr. Haykin is the editor for a series of books on Adaptive and Learning Systems for Signal Processing, Communications and Control published by John Wiley & Sons, Inc. He is both an IEEE Fellow and Fellow of the Royal Society of Canada.

Bart Kosko is a past director of the University of Southern California’s (USC) Signal and Image Processing Institute. He has authored several books, including Neural Networks and Fuzzy Systems, Neural Networks for Signal Processing (Prentice Hall, 1992) Fuzzy Engineering (Prentice Hall, 1997) and Fuzzy Thinking (Hyperion, 1993), as well as the novel Nanotime (Avon Books, 1997) and Heaven in a Chip (Random House, 2000). Dr. Kosko is an elected governor of the International Neural Network Society and has chaired many neural and fuzzy system conferences. He is a faculty member of electrical engineering at USC.

Preface.

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.

Index.

About the Editors.