Implementation Techniques

Implementation Techniques

1st Edition - November 13, 1997

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  • Editor: Cornelius Leondes
  • Hardcover ISBN: 9780124438637
  • eBook ISBN: 9780080551821

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Description

This volume covers practical and effective implementation techniques, including recurrent methods, Boltzmann machines, constructive learning with methods for the reduction of complexity in neural network systems, modular systems, associative memory, neural network design based on the concept of the Inductive Logic Unit, and a comprehensive treatment of implementations in the area of data classification. Numerous examples enhance the text. Practitioners, researchers,and students in engineering and computer science will find Implementation Techniques a comprehensive and powerful reference.

Key Features

  • Recurrent methods
  • Boltzmann machines
  • Constructive learning with methods for the reduction of complexity in neural network systems
  • Modular systems
  • Associative memory
  • Neural network design based on the concept of the Inductive Logic Unit
  • Data classification
  • Integrated neuron model systems that function as programmable rational approximators

Readership

Practitioners, research workers, academicians, and students in mechanical, electrical, industrial, manufacturing, and production engineering, as well as computer science and engineering

Table of Contents

  • Bianchini, Frasconi, Gori, and Maggini, Optimal Learning in Artificial Neural Networks: A Theoretical View. Kanjilal, Orthogonal Transformation Techniques in the Optimization of Feedforward Neural Network Systems. Museli, Sequential Constructive Techniques. Yu, Xu, and Wang, Fast Backpropagation Training Using Optimal Learning Rate and Momentum. Angulo and Torras, Learning of Nonstationary Processes. Schaller, Constraint Satisfaction Problems. Yang and Chen, Dominant Neuron Techniques. Lin, Chiang, and Kim, CMAC-based Techniques for Adaptive Learning Control. Deco, Information Dynamics and Neural Techniques for Data Analysis. Gorinevsky, Radial Basis Function Network Approximation and Learning in Task-Dependent Feedforward Control of Nonlinear Dynamical Systems.

Product details

  • No. of pages: 401
  • Language: English
  • Copyright: © Academic Press 1997
  • Published: November 13, 1997
  • Imprint: Academic Press
  • Hardcover ISBN: 9780124438637
  • eBook ISBN: 9780080551821

About the Series Editor

Cornelius Leondes

Cornelius T. Leondes received his B.S., M.S., and Ph.D. from the University of Pennsylvania and has held numerous positions in industrial and academic institutions. He is currently a Professor Emeritus at the University of California, Los Angeles. He has also served as the Boeing Professor at the University of Washington and as an adjunct professor at the University of California, San Diego. He is the author, editor, or co-author of more than 100 textbooks and handbooks and has published more than 200 technical papers. In addition, he has been a Guggenheim Fellow, Fulbright Research Scholar, IEEE Fellow, and a recipient of IEEE's Baker Prize Award and Barry Carlton Award.

Affiliations and Expertise

University of California, Los Angeles, U.S.A.

About the Author

Cornelius Leondes

Cornelius T. Leondes received his B.S., M.S., and Ph.D. from the University of Pennsylvania and has held numerous positions in industrial and academic institutions. He is currently a Professor Emeritus at the University of California, Los Angeles. He has also served as the Boeing Professor at the University of Washington and as an adjunct professor at the University of California, San Diego. He is the author, editor, or co-author of more than 100 textbooks and handbooks and has published more than 200 technical papers. In addition, he has been a Guggenheim Fellow, Fulbright Research Scholar, IEEE Fellow, and a recipient of IEEE's Baker Prize Award and Barry Carlton Award.

Affiliations and Expertise

University of California, Los Angeles, U.S.A.

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