Self-learning fuzzy controller with neural plant estimator for snack food frying

Fuzzy logic-based control has emerged as a promising approach for complex and/or ill-defined process control. In this paper, a self-learning fuzzy controller with neural plant estimator is designed for the snack food frying control and the specific objectives are as follows: 1) to find the control variables affecting on product quality based on the statistical results of experimental data; 2) to employ the neural estimator for the prediction of real plant output related to time lag; 3) to construct the adaptive-network-based fuzzy inference system for the fuzzy inference rule extraction and the membership function tuning; and 4) to evaluate the designed controller performance by simulation