A Hybrid Self-learning Approach for Generating Fuzzy Inference Systems

Abstract

In this paper, a novel hybrid self-learning approach termed Enhanced Dynamic Self-Generated Fuzzy Q-Learning (EDSGFQL) for automatically generating a Fuzzy Inference System (FIS) is presented. In the EDSGFQL approach, the structure of an FIS is generated via Reinforcement Learning (RL) while the centers of Membership Functions (MFs) are updated by an extended Self Organizing Map (SOM) algorithm. The proposed EDSGFQL methodology can automatically create, delete and adjust fuzzy rules without any priori knowledge. In the EDSGFQL approach, fuzzy rules of an FIS are regarded as agents of the entire system and all of the rules are recruited, adjusted and terminated according to their contributions and participation. At the mean time, the centers of MFs are adjusted to move to the real centers in the sense of feature representation by the extended SOM approach. Comparative studies on a wall-following task by a mobile robot have been done for the proposed EDSGFQL approach and other current methodologies and the demonstration results show that the proposed EDSGFQL approach is superior.