Overview

Neural network theory can be learned with relative ease, yet learning to apply the technology successfully can be a slow, trial-and-error process. In this study, the connectionist model is taught using numerous examples that show how people have built neural network applications. Examples are provided in enough detail for readers to assimilate the information and use the accumulated experience of others to create their own applications. In addition, applications are deliberately restricted to those that can be easily understood and recreated by the novice practitioner. The book also describes alternative approaches to the same application to allow the reader to compare and contrast.

Full Product Details

Author:   David M. Skapura
Publisher:   Pearson Education (US)
Imprint:   Addison Wesley
Dimensions:   Width: 24.10cm , Height: 2.00cm , Length: 16.70cm
Weight:   0.458kg
ISBN:  

9780201539219

ISBN 10:   0201539217
Pages:   304
Publication Date:   11 March 1996
Audience:   College/higher education ,  Tertiary & Higher Education
Format:   Hardback
Publisher's Status:   Out of Print
Availability:   In Print  
Limited stock is available. It will be ordered for you and shipped pending supplier's limited stock.

Table of Contents

Foundations. Motivation. Neural-Network Fundamentals. Single Neuron Computations. Network Computations. Network Simulation. Foundations Summary. Suggested Readings. Bibliography. Paradigms. The Backpropagation Network. The Counterpropagation Network. Adaptive Resonance Theory. The Multidirectional Associative Memory. The Hopfield Memory. Network-Learning Summary. Suggested Readings. Bibliography. Application Design. Developing a Data Representation. Pattern Representation Methods. Exemplar Analysis. Training and Performance Evaluation. A Practical Example. Application-Design Summary. Suggested Readings. Bibliography. Associative Memories. Associative-Memory Definitions. Character Recognition. State-Space Search. Image Interpolation. Diagnostic Aids. Associative-Memory Summary. Suggested Readings. Bibliography. Business and Financial Applications. Financial Modeling. Market Prediction. Bond Rating. Predicting Commodity Futures. Financial-Applications Summary. Suggested Readings. Bibliography. Pattern Classification. NETtalk. Radar-Signature Classifier. Prostate-Cancer Detection. Pattern-Classification Summary. Suggested Readings. Bibliography. Image Processing. Image-Processing Networks. Gender Recognition from Facial Images. Imagery Feature Discovery. Aircraft Tracking in Video Imagery. Image-Processing Summary. Suggested Readings. Bibliography. Process Control and Robotics. Control Theory. Cart/Pole Balancer. Bipedal-Locomotion Control. Robotic Manipulator Control. Control-Application Summary. Suggested Readings. Bibliography. Fuzzy Neural Systems. Fuzzy Logic. Implementation of a Fuzzy Network. Fuzzy Neural Inference. Fuzzy Control of BPN Learning. Fuzzy Neural-System Summary. Suggested Readings. Bibliography. Answers to Selected Exercises. Index. 0201539217T04062001

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Author Information

About David M. Skapura David M. Skapura is the coauthor, with James A. Freeman, of Neural Networks: Algorithms, Applications, and Programming Techniques. He is currently employed by Brightware Corporation (a spin-off of Inference Corporation), where he works as an applications consultant, developing customized knowledge-based systems and applications. He is also the founder and president of Scient Computing, a small Houston consulting firm specializing in neural-networking applications and research. Previously at Loral Space Information Systems, Skapura investigated the applicability of neural networks to NASA's advanced automation requirements. He is an adjunct professor at the University of Houston at Clear Lake. 0201539217AB04062001

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