Implementation Techniques

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
With numerous examples to enhance the text, practitioners, researchers, and students in engineering and computer science will find Implementation Techniques a uniquely comprehensive and powerful reference source

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.