A barrier method for constrained nonlinear optimization using a modified Hopfield network

• Autor(a) principal: Silva, I. N. da
• Data de Publicação: 2001
• Outros Autores: Ulson, Jose Alfredo Covolan [UNESP], Souza, A. N. de
• Tipo de documento: Artigo de conferência
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.1109/IJCNN.2001.938425; http://hdl.handle.net/11449/66422
• topic: Computer simulation; Errors; Mathematical models; Optimization; Parameter estimation; Barrier method; Constrained nonlinear optimization; Equilibrium point; Modified Hopfield network; Nonlinear model; Unknown but bounded errors; Valid subspace technique; Neural networks

The ability of neural networks to realize some complex nonlinear function makes them attractive for system identification. This paper describes a novel barrier method using artificial neural networks to solve robust parameter estimation problems for nonlinear model with unknown-but-bounded errors and uncertainties. This problem can be represented by a typical constrained optimization problem. 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.