New Soft Computing Techniques for System Modeling, Pattern Classification and Image Processing

• Main Author: Rutkowski, Leszek (Author, http://id.loc.gov/vocabulary/relators/aut)
• Corporate Author: SpringerLink (Online service)
• Format: Electronic eBook
• Language: English
• Published: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2004.
• Edition: 1st ed. 2004.
• Series: Studies in Fuzziness and Soft Computing, 143
• Subjects: Computers.; Applied mathematics.; Engineering mathematics.; Artificial intelligence.; Optical data processing.; Pattern recognition.; Information theory.; Theory of Computation.; Mathematical and Computational Engineering.; Artificial Intelligence.; Image Processing and Computer Vision.; Pattern Recognition.; Information and Communication, Circuits.

This book presents new soft computing techniques for system modeling, pattern classification and image processing. The book consists of three parts, the first of which is devoted to probabilistic neural networks including a new approach which has proven to be useful for handling regression and class...

Table of Contents:

• 1 Introduction
• I Probabilistic Neural Networks in a Non-stationary Environment
• 2 Kernel Functions for Construction of Probabilistic Neural Networks
• 3 Introduction to Probabilistic Neural Networks
• 4 General Learning Procedure in a Time-Varying Environment
• 5 Generalized Regression Neural Networks in a Time-Varying Environment
• 6 Probabilistic Neural Networks for Pattern Classification in a Time-Varying Environment
• II Soft Computing Techniques for Image Compression
• 7 Vector Quantization for Image Compression
• 8 The DPCM Technique
• 9 The PVQ Scheme
• 10 Design of the Predictor
• 11 Design of the Code-book
• 12 Design of the PVQ Schemes
• III Recursive Least Squares Methods for Neural Network Learning and their Systolic Implementations
• 13 A Family of the RLS Learning Algorithms
• 14 Systolic Implementations of the RLS Learning Algorithms
• References.