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

• Main Author: da Silva, I. N.
• Publication Date: 2001
• Other Authors: de Souza, A. N., Bordon, M. E., Ulson, Jose Alfredo Covolan [UNESP]
• Format: Conference object
• Language: eng
• Source: Repositório Institucional da UNESP
• Download full: http://dl.acm.org/citation.cfm?id=704386; http://hdl.handle.net/11449/8905
• topic: neural networks; robust estimation; parameter identification; estimation algorithms

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.