Automatic generation of hierarchical structure of fuzzy inference by genetic algorithm

This paper deals with an automatic generation algorithm of a hierarchical fuzzy inference using genetic algorithms and the delta rule. The fuzzy inference can be applied to various problems. However, the determination of the membership functions is a difficult problem because the determination depends on human experts. The auto-tuning methods of the fuzzy model have been proposed to develop the time-consuming operation used by human experts. The problem is that a number of the fuzzy rules in the multiple input and multiple output is needed in the case of simple fuzzy inference. Therefore, the authors propose an auto-tuning method of hierarchical fuzzy inference. The proposed method enables the fuzzy model to construct the optimal and the minimal structures. In this paper, the authors show the effectiveness of the proposed method by simulation of water tanks level control. This general system can be applied to robotic motion control, sensing and recognition problems