A neural system to robust Nonlinear optimization subject to disjoint and constrained sets

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da Silva, I. N.
de Souza, A. N.
Bordon, M. E.
Ulson, Jose Alfredo Covolan [UNESP]

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Int Inst Informatics & Systemics


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.



neural networks, robust estimation, parameter identification, estimation algorithms

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World Multiconference on Systemics, Cybernetics and Informatics, Vol 1, Proceedings. Orlando: Int Inst Informatics & Systemics, p. 7-12, 2001.