Overview

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 implementations 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.

Full Product Details

Author:   Leszek Rutkowski
Publisher:   Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
Imprint:   Springer-Verlag Berlin and Heidelberg GmbH & Co. K
Edition:   Softcover reprint of hardcover 1st ed. 2004
Volume:   143
Dimensions:   Width: 15.50cm , Height: 2.00cm , Length: 23.50cm
Weight:   0.593kg
ISBN:  

9783642058202

ISBN 10:   3642058205
Pages:   374
Publication Date:   04 December 2010
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   In Print  
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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.

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