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Recurrent neural dynamic models for equilibrium and eigenvalue problems

✍ Scribed by S. Rajasekaran; G.A. Vijayalakshmi Pai

Publisher
Elsevier Science
Year
2002
Tongue
English
Weight
702 KB
Volume
35
Category
Article
ISSN
0895-7177

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✦ Synopsis

Neural networks (NN) have been used in a number of interesting applications. In this paper, two neural dynamic models which belong to the class of recurrent neural networks (RNN) have been formulated for the solution of equilibrium and eigenvalue problems. The RNN is comprised of two layers, namely, variable layer and constraint layer, which correspond to the number of design variables in the problem. In addition, the recurrent connections and feed forward connections are used to represent the incremental values in the design parameters. The stability of the neural dynamic model for the equilibrium problem has been guaranteed using Lyapunov's function. Illustrative examples and results of the computer simulation of the neural dynamic model have also been presented. Elsevier Science Ltd. All rights reserved.

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