Trabalho apresentado em evento
A neural system to robust Nonlinear optimization subject to disjoint and constrained sets
Fecha
2001-01-01Registro en:
World Multiconference on Systemics, Cybernetics and Informatics, Vol 1, Proceedings. Orlando: Int Inst Informatics & Systemics, p. 7-12, 2001.
WOS:000175785900002
8212775960494686
5589838844298232
4517057121462258
0000-0001-8510-8245
Autor
Universidade Estadual Paulista (Unesp)
Resumen
The ability of neural networks to realize some complex nonlinear function makes them attractive for system identification. This paper describes a novel method using artificial neural networks to solve robust parameter estimation problems for nonlinear models with unknown-but-bounded errors and uncertainties. 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.