Optimization of a fuzzy controller for fruit storage using neural networks and genetic algorithms

Abstract It is difficult to determine membership functions and control rules efficiently when applying fuzzy control to an unknown system. In this study, a new fuzzy control technique, which efficiently selects optimal membership functions and control rules by using neural networks and genetic algorithms, was proposed and then applied to the control of relative humidity in a fruit-storage house. The control input is the on-off of ventilation. The response of relative humidity, as affected by ventilation, is first identified using neural networks, and then optimal membership functions and control rules are sought through simulation of the identified model using genetic algorithms. The neural network works as a simulator for the search for an optimal value. The control aim here is to maintain the relative humidity in the storage house at the desired value through the on-off control of ventilation by the fuzzy control. Results show that this control technique allowed optimal membership functions and control rules to be successfully determined, and its control performance was superior to the conventional control.