Stability and convergence analysis of a neural model applied in nonlinear systems optimization

Authors:
Ivan Nunes da Silva
Affiliations:
State University of São Paulo, Department of Electrical Engineering, Bauru, SP, Brazil
Venue:
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
Year:
2003

Citing 3
Cited 0

Nonlinear systems analysis (2nd ed.)

Nonlinear systems analysis (2nd ed.)
Original Contribution: An analytical framework for optimizing neural networks

Neural Networks
A high-performance neural network for solving linear and quadratic programming problems

IEEE Transactions on Neural Networks

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Abstract

A neural model for solving nonlinear optimization problems is presented in this paper. More specifically, a modified Hopfield network is developed and its internal parameters are computed using the valid-subspace technique. These parameters guarantee the convergence of the network to the equilibrium points that represent an optimal feasible solution. The network is shown to be completely stable and globally convergent to the solutions of nonlinear optimization problems. A study of the modified Hopfield model is also developed to analyze its stability and convergence. Simulation results are presented to validate the developed methodology.