da Silva, I. N.de Souza, A. N.Bordon, M. E.Ulson, Jose Alfredo Covolan [UNESP]2014-05-202014-05-202001-01-01World Multiconference on Systemics, Cybernetics and Informatics, Vol 1, Proceedings. Orlando: Int Inst Informatics & Systemics, p. 7-12, 2001.http://hdl.handle.net/11449/8905The 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.7-12engneural networksrobust estimationparameter identificationestimation algorithmsTrabalho apresentado em eventoWOS:000175785900002Acesso aberto8212775960494686558983884429823245170571214622580000-0001-8510-8245