Publicação: A neural network approach for robust nonlinear parameter estimation in presence of unknown-but-bounded errors
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Elsevier B.V.
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Trabalho apresentado em evento
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Acesso aberto
Resumo
Systems based on artificial neural networks have high computational rates due to the use of a massive number of simple processing elements and the high degree of connectivity between these elements. This paper presents a novel approach to solve robust parameter estimation problem for nonlinear model 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. Copyright (C) 2000 IFAC.
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parameter identification, neural networks, robust estimation, artificial intelligence, estimation algorithms
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Inglês
Como citar
Control Applications of Optimization 2000, Vols 1 and 2. Kidlington: Pergamon-Elsevier B.V., p. 317-322, 2000.
