INTRODUCTION TO ARTIFICIAL NEURAL NETWORKS -A book by Jacek M. Zurada- In which you will find the

Newly developed paradigms of artificial neural networks have strongly contributed to the discovery, understanding and utilization of potential functional similarities between human and artificial information processing systems. Intense research interest persists and the area continues to develop. Artificial neural systems or neural networks are physically cellular systems which can acquire, store and utilize experimental knowledge. This book focuses on the foundations of such networks. The fundamentals of artificial neural systems theory, algorithms for information acquisition and retrieval examples of applications, and implementations issues are also included. Jacek M. Zurada received his MS and Ph.D. degrees (with distinction) in electrical engineering from the Technical University of Gdansk, Poland. Since 1989 he has been a Professor with the Electrical and Computer Engineering Department at the University of Louisville, Kentucky. He was Department Chair from 2004 to 2006. He has published over 350 journal and conference papers in the areas of neural networks, computational intelligence, data mining,

KEY FEATURES CONTENTS

• The book uses mathematical exposition at 1.Artificial Neural Systems: Preliminari

the depth, essential for artificial neural

systems implementation and simulation

* Unified and pedagogical approaches have been 2.Fundamental Concepts and Models of

used for better understanding of the complex Artificial Neural System

subject by the readers

* Author presents an integrated perspective to 3.Single-Layer Perceptron Classifiers

blend interdisciplinary aspects of this discipline and

also link the approaches and terminologies

among them

*The end-of-chapter problems focus on enhancing 4.Multilayer Feedforward Networks

the understanding of principles 5.Single-Layer Feedback Networks

6.Associative Memories

7.Matching and Self-Organizing Networks

8.Applications of Neural Algorithms and Systems

9.9. Neural Networks Implementation