Recurrent neural dynamic models for equilibrium and eigenvalue problems

  • Authors:

  • S. Rajasekaran;G. A. Vijayalakshmi Pai

  • Affiliations:

  • Department of Civil Engineering PSG College of Technology Coimbatore 641 004, India;Department of Computer Applications PSG College of Technology Coimbatore 641 004, India

  • Venue:
  • Mathematical and Computer Modelling: An International Journal
  • Year:
  • 2002

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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 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.