Recurrent neural dynamic models for equilibrium and eigenvalue problems

Recurrent neural dynamic models for equilibrium and eigenvalue problems

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Article ID: iaor2004706
Country: Netherlands
Volume: 35
Issue: 1/2
Start Page Number: 229
End Page Number: 240
Publication Date: Jan 2002
Journal: Mathematical and Computer Modelling
Authors: ,
Keywords: matrices
Abstract:

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 composed 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.

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