Tuning an Adaptive Neural Network Fuzzy Inference Controller Using Evolutionary Learning

摘要

The control of nonlinear systems continues to be a challenging problem, particularly when the plant to be controlled changes or the environment is uncertain. A nonparametric approach which has gained success is to employ a neural network to learn about the unknown plant and fuzzy inference to compensate for the uncertainty (ANFIS control). Inherent in the design of such controllers is the need to tune the weights of the ANFIS controller. This paper presents an evolutionary learning approach whereby the ANFIS parameters are tuned at each generation. Results show that this approach is a feasible method for tuning nonparametric controllers such as the ANFIS architecture.