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

✍ Scribed by Prof. Ph. D., D. Sc. Leszek Rutkowski (auth.)

Publisher: Springer-Verlag Berlin Heidelberg
Year: 2004
Tongue: English
Leaves: 376
Series: Studies in Fuzziness and Soft Computing 143
Edition: 1
Category: Library

No coin nor oath required. For personal study only.

✦ Synopsis

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 classification problems in time-varying environments. The second part of the book is devoted to Soft Computing techniques for Image Compression including the vector quantization technique. The third part analyzes various types of recursive least square techniques for neural network learning as well as discussing hardware implemenations using systolic technology. By integrating various disciplines from the fields of soft computing science and engineering the book presents the key concepts for the creation of a human-friendly technology in our modern information society.

✦ Table of Contents

Front Matter....Pages i-xi
Introduction....Pages 1-6
Front Matter....Pages 7-7
Kernel Functions for Construction of Probabilistic Neural Networks....Pages 9-19
Introduction to Probabilistic Neural Networks....Pages 21-57
General Learning Procedure in a Time-Varying Environment....Pages 59-71
Generalized Regression Neural Networks in a Time-Varying Environment....Pages 73-134
Probabilistic Neural Networks for Pattern Classification in a Time-Varying Environment....Pages 135-167
Front Matter....Pages 168-168
Vector Quantization for Image Compression....Pages 169-177
The DPCM Technique....Pages 179-184
The PVQ Scheme....Pages 185-191
Design of the Predictor....Pages 193-202
Design of the Code-book....Pages 203-216
Design of the PVQ Schemes....Pages 217-239
Front Matter....Pages 168-168
A Family of the RLS Learning Algorithms....Pages 241-281
Systolic Implementations of the RLS Learning Algorithms....Pages 283-328
Back Matter....Pages 329-373

✦ Subjects

Appl.Mathematics/Computational Methods of Engineering; Artificial Intelligence (incl. Robotics); Image Processing and Computer Vision; Pattern Recognition; Information and Communication, Circuits; Systems Theory, Control

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