Title :
A neuro-fuzzy-genetic system for automatic setting of control strategies
Author :
Amaral, J.F.M. ; Vellasco, M.M. ; Tanscheit, R. ; Pacheco, M.A.C.
Author_Institution :
DETEL, UERJ, Rio de Janeiro, Brazil
Abstract :
The article 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. The neuro-fuzzy system used here is the NEFCON model, which is capable of learning and optimizing online the rulebase of a Mamdani-type fuzzy controller. The algorithm is based on reinforcement learning that uses a fuzzy measure for the error. Its performances in the control of linear plants of diverse complexity and also of a nonlinear one are evaluated. Results are compared to those obtained through conventional techniques. The main focus of the work is on the development of a new neuro-fuzzy-genetic system, which makes use of genetic algorithms for rule base optimization. The satisfactory results obtained with the two more complex plants show the potential of this hybrid model in the design of control systems
Keywords :
control system analysis computing; fuzzy control; fuzzy neural nets; genetic algorithms; learning (artificial intelligence); neurocontrollers; Mamdani-type fuzzy controller; NEFCON model; automatic setting; computational intelligence; control systems design; fuzzy error measure; genetic algorithms; hybrid model; hybrid techniques; linear plants; neuro-fuzzy system; neuro-fuzzy-genetic system; reinforcement learning; rule base optimization; Automatic control; Computational intelligence; Control system synthesis; Control systems; Fuzzy control; Fuzzy neural networks; Fuzzy systems; Genetic algorithms; Learning; Performance evaluation;
Conference_Titel :
IFSA World Congress and 20th NAFIPS International Conference, 2001. Joint 9th
Conference_Location :
Vancouver, BC
Print_ISBN :
0-7803-7078-3
DOI :
10.1109/NAFIPS.2001.943780
