Network Models for Control and Processing
Network Models for Control and Processing (Book)
A powerful research tool that unites the related work on both modeling control and processing in distributed networks.
Traditional publications in the field of network models have focussed [sic] on specific areas, whereas this collection successfully intersects many related fields.
Edition
A powerful research tool that unites the related work on both modeling control and processing in distributed networks.
Traditional publications in the field of network models have focussed [sic] on specific areas, whereas this collection successfully intersects many related fields. These include:
- control processes,
- modelling features and operations of biological neural networks and neurons,
- simulation of biological experimentation,
- representation of artificial neural networks (ANNs)
Advances covering a broad area in control, learning and neurobiological models - using mathematics and simulation - are reported through the international perspectives of the authors. Their work represents the most recent developments in this fast-moving field and directly engages with its central concerns.
Martin D. Fraser is Professor and Chair, Department of Computer Science at Georgia State University. He was Statistical Analysis Manager at American Greetings Corp. and Chief of Software Acceptance Testing at the Air Force Satellite Test Center in Sunnyvale, California. He received a PhD in Mathematics, major Statistics, from St. Louis University and his research interests include software engineering, artificial neural networks, and simulation.
Contents
Editor's Preface iii
1 Learning in Time Varying Environments p.1
Anthony Kuh and Thomas Petsche
2 Cortical Inhibition as Explained by the Competitive Distribution Hypothesis p.31
James A. Reggia, Granger G. Sutton III, C. Lynne D'Autrechy, Sungzoon Choo and Steve L. Armentrout
3 Implementation of Hartline Pools and Neural-Type Cells by VLSI Circuits p.63
Suan-Wei Tsay and Robert W. Newcomb
4 Self-Organizing Parallel Distributed Neural Network Models p.83
A. Garliauskas and A. Malickas
5 Behavioural Simulation of Neural Networks: An Approach Based upon SPICE P.109
Dario D'Amore and Vincenzo Piuri
6 Induction and Polynomial Networks p.143
John F. Elder IV and Donald E. Brown