A Prelude to Neural Networks: Adaptive and Learning Systems

Author:   Jerry M. Mendel
Publisher:   Pearson Education (US)
Edition:   1st New edition
ISBN:  

9780131474482


Pages:   464
Publication Date:   01 February 1994
Format:   Hardback
Availability:   Out of stock   Availability explained


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A Prelude to Neural Networks: Adaptive and Learning Systems


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Overview

"Now recognized as a classic that was ahead of its time, this volume has been republished to make available a rich source of background materials for the field of neural networks. It serves as a bridge between today's ""second wave"" of neural networks and the ""first wave"" that occurred in the 1960s -- and collects in one place the vast amount of material of great interest to ""second wavers."" Explores the seminal thinking in the development of such areas as pattern recognition as the foundation for both supervised and unsupervised feedforward neural networks; optimization; gradient optimization algorithms within the contexts of general stochastic approximation theory and equation error system identification; and reinforcement learning control systems -- with regard to short-term and long-term memory, goals and sub-goals, and stochastic automata. The Preface explains the content within the context of today's advancements in neural networks."

Full Product Details

Author:   Jerry M. Mendel
Publisher:   Pearson Education (US)
Imprint:   Prentice Hall
Edition:   1st New edition
Dimensions:   Width: 15.10cm , Height: 2.30cm , Length: 22.60cm
Weight:   0.600kg
ISBN:  

9780131474482


ISBN 10:   0131474480
Pages:   464
Publication Date:   01 February 1994
Audience:   College/higher education ,  Tertiary & Higher Education
Format:   Hardback
Publisher's Status:   Out of Print
Availability:   Out of stock   Availability explained

Table of Contents

1. Elements of Pattern Recognition. 2. Statistical Pattern Recognition. 3. Algorithms for Pattern Recognition. 4. Applications of Pattern Recognition Technology. 5. Synthesis of Quasi-Optimal Switching Surfaces by Means of Training Techniques. 6. Gradient Identification for Linear Systems.

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