Ebook: Intelligent Signal Processing

This book 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.
Content:
Front Matter
• Preface
• Table of Contents
1. Humanistic Intelligence: "Wear Comp" as a New Framework and Application for Intelligent Signal Processing
2. Adaptive Stochastic Resonance
3. Learning in the Presence of Noise
4. Incorporating Prior Information in Machine Learning by Creating Virtual Examples
5. Deterministic Annealing for Clustering, Compression, Classification, Regression, and Speech Recognition
6. Local Dynamic Modeling with Self-Organizing Maps and Applications to Nonlinear System Identification and Control
7. A Signal Processing Framework Based on Dynamic Neural Networks with Application to Problems in Adaptation, Filtering and Classification
8. Semiparametric Support Vector Machines for Nonlinear Model Estimation
9. Gradient-Based Learning Applied to Document Recognition
10. Pattern Recognition Using A Family of Design Algorithms Based Upon Generalized Probabilistic Descent Method
11. An Approach to Adaptive Classification
12. Reduced-Rank Intelligent Signal Processing with Application to Radar
13. Signal Detection in a Nonstationary Environment Reformulated as an Adaptive Pattern Classification Problem
14. Data Representation Using Mixtures of Principal Components
15. Image Denoising by Sparse Code Shrinkage
Index