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Proceedings Paper

Stochastic algorithm for evaluation of energy function of Hopfield neural networks
Author(s): Ercan Sen; Abhijit S. Pandya; Sam Hsu
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Paper Abstract

In this paper, we describe a stochastic algorithm which can be used for evaluation of energy function of Hopfield neural networks designed for solving optimization problems. To demonstrate this capability we used the Hopfield neural network described by T. P. Troudet to optimize the operation of a crossbar packet switch. The evaluation of energy function of a Hopfield neural network requires determination of synaptic weights from the energy function which represents the optimization problem under consideration. This process involves several iterations of adjusting the coefficients of the energy function and calculating the synaptic weights from the coefficients of the energy function. The proposed stochastic algorithm allows the designer of a Hopfield network to directly evaluate the energy function and adjust the coefficients without going through the step to determine the synaptic weights. In this paper, we also present the simulation results for evaluation of the energy function described by T. P. Troudet using the stochastic algorithm.

Paper Details

Date Published: 4 April 1997
PDF: 8 pages
Proc. SPIE 3077, Applications and Science of Artificial Neural Networks III, (4 April 1997); doi: 10.1117/12.271505
Show Author Affiliations
Ercan Sen, Siemens Stromberg-Carlson, Inc. (United States)
Abhijit S. Pandya, Florida Atlantic Univ. (United States)
Sam Hsu, Florida Atlantic Univ. (United States)


Published in SPIE Proceedings Vol. 3077:
Applications and Science of Artificial Neural Networks III
Steven K. Rogers, Editor(s)

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