Neural networks and simulation: Modeling for applications

Author:

Padgett Mary Lou1,Roppel Thaddeus A.1

Affiliation:

1. Electrical Engineering Dept. Auburn University Auburn, Alabama 36849

Abstract

Artificial neural networks simulate biologi cal processes in an intriguing manner. Ideas gleaned from the study of neurophysiology and animal behavior have become realizable in recent years. The advent of computers capable of rapidly executing massively parallel and distributed processes has allowed ideas from diverse fields to be merged and tested. The resulting neural networks, simulated in software and/or hardware, provide an adaptable, robust modeling tool useful to simulationists in all disciplines.

Publisher

SAGE Publications

Subject

Computer Graphics and Computer-Aided Design,Modeling and Simulation,Software

Reference20 articles.

1. Akers, Lex A., Ferry, David K. and Grondin, Robert O. 1990. "Synthetic Neural Systems in VLSI," An Introduction to Neural and Electronic Networks, Ed. Zornetzer, S. F., Davis, J. L. and Lau, C. Academic Press, Inc., San Diego, CA, pp. 275-292.

2. Performance and generalization of the classification figure of merit criterion function

3. Bowker, A.H. and Lieberman, G.J. 1972. Engineering Statistics. Prentice-Hall, Inc., Englewood Cliffs, N.J. p. 134-153.

4. Conover, W.J. 1980. Practical Nonparametric Statistics. 2nd Edition. John Wiley & Sons, NY. 493 p.

5. Detection of spurious states of neural networks

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