Publication: A neural network approach for robust nonlinear parameter estimation in presence of unknown-but-bounded errors
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Coadvisor
Graduate program
Undergraduate course
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Volume Title
Publisher
Elsevier B.V.
Type
Work presented at event
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Acesso aberto
Abstract
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.
Description
Keywords
parameter identification, neural networks, robust estimation, artificial intelligence, estimation algorithms
Language
English
Citation
Control Applications of Optimization 2000, Vols 1 and 2. Kidlington: Pergamon-Elsevier B.V., p. 317-322, 2000.
