Learning of a backpropagation neural network to tune a fuzzy control of a thermal system

Abstract

This regular paper describes three important aspects: design, simulation and implementation of neuro-fuzzy control applied to the temperature variable of a thermal system with a range of 25/spl deg/C to 75/spl deg/C and resolution of 0.01%. In the design were found the membership functions and the fuzzy rules base already optimized of the fuzzy controller by means of a backpropagation neural network trained to 120 learning cycles. The simulation presents basic tables of the fuzzy controller obtained by the neural network. The implementation of the program of the fuzzy controller was effected using a 486 PC with conversion card A/D and of 8-bit port output.