A neural network approach for robust nonlinear parameter estimation in presence of unknown-but-bounded errors

• Autor(a) principal: da Silva, I. N.
• Data de Publicação: 2000
• Outros Autores: de Souza, A. N., Bordon, M. E.
• Tipo de documento: Artigo de conferência
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: https://getinfo.de/app/A-Neural-Network-Approach-for-Robust-Nonlinear/id/BLCP%3ACN039405763; http://hdl.handle.net/11449/8886
• topic: parameter identification; neural networks; robust estimation; artificial intelligence; estimation algorithms

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.