A neural system to robust Nonlinear optimization subject to disjoint and constrained sets - da Silva, I. N.; de Souza, A. N.; Bordon, M. E.; Ulson, Jose Alfredo Covolan [UNESP] (Int Inst Informatics & Systemics, 2001)
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.
Keywords: neural networks; robust estimation; parameter identification; estimation algorithms