Implementation Techniques

Other | February 1, 1998

byLeondes, Cornelius T., Cornelius T. Leondes

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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.
  • 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

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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 comprehen...

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 adju...

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Format:OtherDimensions:401 pages, 1 × 1 × 1 inPublished:February 1, 1998Publisher:Academic PressLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:0080551823

ISBN - 13:9780080551821

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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.