Self-Generation of Fuzzy Inference Systems by Enhanced Dynamic Self-Generated Fuzzy Q-Learning

In this paper, a novel approach termed enhanced dynamic self-generated fuzzy Q-learning (EDSGFQL) for automatically generating a fuzzy inference system (FIS) is presented. In this temporal difference (TD)-based EDSGFQL approach, the structure and preconditioning parts of an FIS are generated by a hybrid reinforcement learning (RL) and unsupervised learning (UL) approach. In the EDSGFQL approach, the preconditioning parts of an FIS are estimated and generated dynamically via evaluations on the TD error and firing strength. An extended self organizing map (SOM) algorithm is used for updating the centers of membership functions (MFs). The consequent parts of the FIS are trained through the FQL. The proposed EDSGFQL methodology can automatically create, delete and adjust fuzzy rules without any priori knowledge, which is superior to many existing methodologies. Simulation studies on a wall-following task by a mobile robot show that the proposed EDSGFQL approach is superior