New soft computing techniques for system modelling pattern classification and image processing 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. TOC:Probabilistic Neural Networks in a Non stationary Environment. Soft Computing Techniques for Image Compression. Recursive Least Square Methods for Neural Network Learning and their Systolic Implementations