Publicação: A barrier method for constrained nonlinear optimization using a modified Hopfield network
Carregando...
Data
Orientador
Coorientador
Pós-graduação
Curso de graduação
Título da Revista
ISSN da Revista
Título de Volume
Editor
Tipo
Trabalho apresentado em evento
Direito de acesso
Acesso aberto
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.
Descrição
Palavras-chave
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
Idioma
Inglês
Como citar
Proceedings of the International Joint Conference on Neural Networks, v. 3, p. 1744-1749.
