Haykin S., Kosko B. (eds.) Intelligent Signal Processing

IEEE Press, 2001. — 594 p."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.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