Auto fuzzy tuning having minimum structure by using genetic algorithm and delta rule

An auto tuning algorithm of fuzzy inference for Fuzzy neural networks using the genetic algorithm and the delta rule is presented. Some auto-tuning methods are proposed to reduce time-consuming operations by human experts. This tuning method brings the minimal and optimal structure of the fuzzy model. Two types of the fuzzy model are prepared, whose membership functions on the antecedent part consist of triangular and Gaussian type, respectively. The effectiveness of the proposed methods compared with the former methods is shown by simulation. The proposed method has the potential to be applied to robotic motion control, sensing and recognition problems