A barrier method for constrained nonlinear optimization using a modified Hopfield network
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2001-01-01
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Resumo
The ability of neural networks to realize some complex nonlinear function makes them attractive for system identification. This paper describes a novel barrier method using artificial neural networks to solve robust parameter estimation problems for nonlinear model with unknown-but-bounded errors and uncertainties. This problem can be represented by a typical constrained optimization problem. More specifically, a modified Hopfield network is developed and its internal parameters are computed using the valid-subspace technique. These parameters guarantee the network convergence to the equilibrium points. A solution for the robust estimation problem with unknown-but-bounded error corresponds to an equilibrium point of the network. Simulation results are presented as an illustration of the proposed approach.
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Computer simulation, Errors, Mathematical models, Optimization, Parameter estimation, Barrier method, Constrained nonlinear optimization, Equilibrium point, Modified Hopfield network, Nonlinear model, Unknown but bounded errors, Valid subspace technique, Neural networks
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Proceedings of the International Joint Conference on Neural Networks, v. 3, p. 1744-1749.