Título: Determinação Automática Da Estratégia De Controle Através De Sistema Híbrido Neuro-Fuzzy-Genético
Autores: Amaral, J. F. M.; Vellasco, M. M.; Tanscheit, R.; Pacheco, M. A. C.
Resumo: This work deals with the design of control systems based on hybrid techniques of computational intelligence. Initially a neuro-fuzzy system is employed in the control of several plants. Neuro-fuzzy systems implement a fuzzy inference system through a parallel distributed architecture, so that learning paradigms common to artificial neural networks can be of use in this hybrid architecture. The neuro-fuzzy system used in this work is the NEFCON model, which is capable of learning and optmizing on line the rule-base of a Mamdani-type fuzzy controller. The algorithm is based on reinforcement learning which uses a fuzzy measure for the error. Its performances in the control of linear plants of diverse complexity and also of a nonlinear one have been evaluated. Results are compared to those obtained through conventional techniques. The main focus of this work is on a new Neuro-Fuzzy-Genetic System which makes use of genetic algorithms in the optimization stage of the NEFCON algorithm. The satisfactory results obtained with the two more complex plants show the potential of this hybrid model in the design of control systems.
Código DOI: 10.21528/CBRN2001-061
Artigo em pdf: 5cbrn_061.pdf
Arquivo BibTex: 5cbrn_061.bib