Research On Self-Adaptive Fuzzy Controller Based On Identification Using Neural Networks

In recent years special attention has been paid on the combination of neural network and fuzzy logic especially in academe and engineering. Neural network is good at pattern recognition and self-adaptive response according to changing environment while fuzzy inference systems have advantage at reasoning and decision-making. Combining them, we can solve actual problem effectively.It has proved that proper scaling factors can improve the performance of fuzzy logic controller. But how to find proper scaling factors? Many experts and scholars have discussed this question and have given many schemes. However none of them can solve the problem perfectly.How to control a nonlinear system using BP algorithm for identification and self-adaptive fuzzy control is discussed in this paper. We give a banausic parallel scheme after analyzed the requirements of the actual situation of the industry project. After discussed how the scaling factors work on the performance of the controlled process, we bring forward a self-tuning scheme for fuzzy logic controllers, Here, the output scaling factor k3 is adjusted by a neural network. The simulation results show that the proposed scheme has a improved performance over its conventional counterpart.