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

New Soft Computing Techniques for System Modeling, Pattern Classification and Image Processing
Author :
Publisher : Springer Science & Business Media
Total Pages : 398
Release :
ISBN-10 : 3540205845
ISBN-13 : 9783540205845
Rating : 4/5 (845 Downloads)

Book Synopsis New Soft Computing Techniques for System Modeling, Pattern Classification and Image Processing by : Leszek Rutkowski

Download or read book New Soft Computing Techniques for System Modeling, Pattern Classification and Image Processing written by Leszek Rutkowski and published by Springer Science & Business Media. This book was released on 2004-02-03 with total page 398 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.


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