Silva, I. N. daUlson, Jose Alfredo Covolan [UNESP]Souza, A. N. de2014-05-272014-05-272001-01-01Proceedings of the International Joint Conference on Neural Networks, v. 3, p. 1744-1749.http://hdl.handle.net/11449/66422The 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.1744-1749engComputer simulationErrorsMathematical modelsOptimizationParameter estimationBarrier methodConstrained nonlinear optimizationEquilibrium pointModified Hopfield networkNonlinear modelUnknown but bounded errorsValid subspace techniqueNeural networksTrabalho apresentado em evento10.1109/IJCNN.2001.938425WOS:000172784800310Acesso aberto2-s2.0-003486295245170571214622588212775960494686