Full Description
Science has made great progress in the twentieth century, with the establishment of proper disciplines in the fields of physics, computer science, molecular biology, and many others. At the same time, there have also emerged many engineering ideas that are interdisciplinary in nature, beyond the realm of such orthodox disciplines. These in clude, for example, artificial intelligence, fuzzy logic, artificial neural networks, evolutional computation, data mining, and so on. In or der to generate new technology that is truly human-friendly in the twenty-first century, integration of various methods beyond specific disciplines is required. Soft computing is a key concept for the creation of such human friendly technology in our modern information society. Professor Rutkowski is a pioneer in this field, having devoted himself for many years to publishing a large variety of original work. The present vol ume, based mostly on his own work, is a milestone in the devel opment of soft computing, integrating various disciplines from the fields of information science and engineering. The book consists of three parts, the first of which is devoted to probabilistic neural net works. Neural excitation is stochastic, so it is natural to investi gate the Bayesian properties of connectionist structures developed by Professor Rutkowski. This new approach has proven to be par ticularly useful for handling regression and classification problems vi Preface in time-varying environments. Throughout this book, major themes are selected from theoretical subjects that are tightly connected with challenging applications.
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.
