Uma abordagem neuro- nebulosa para otimização de sistema e indentificação robusta

AUTOR(ES)
DATA DE PUBLICAÇÃO

1997

RESUMO

The ability of artificial neural networks to solve complex and diversified problems make them attractive for application in many áreas of engineering and science. A neural network is basically composed of many simple processing elements with a high degree of connectivity among them. This thesis presents an architecture of artificial neural network to apply in optimization problems with constraints. More specifically, a topology based in the Hopfield networks is proposed to solve some optimization problems, including dynamic programming and combinatorial optimization, and also robust parametric identification problems with unknown-but-bounded disturbance. In this case, the network is used to calculate the parametric uncertainty intervals and the feasible membership set for model parameters. A rule-based fuzzy system has been developed in order to improve the efficiency and the network convergence to the equilibrium points. The internal parameters of the network, associated with the constraints imposed by a specific problem, are explicitally computed using a valid-subspace technique. Simulation results are provided to illustrate the performance of the proposed approach

ASSUNTO(S)

redes neurais (computação) inteligencia artificial identificação de sistemas otimização matematica

ACESSO AO ARTIGO

Documentos Relacionados