Automatic generation of Fuzzy Inference Systems using Incremental-Topological-Preserving-Map-based Fuzzy Q-Learning

This paper represents a new approach for automatically generating fuzzy inference system (FIS) using incremental topological preserving map fuzzy Q-learning (ITPM-FQL). The ITPM-FQL can create and tune the fuzzy rules automatically without any priori knowledge. The online self organizing ITPM approach is used to achieve automatic structure identification while the fuzzy Q-learning approach is used for parameter identification to deal with continuous states and actions. Compared with the first authorpsilas previous works in dynamic fuzzy Q-learning (DFQL), this proposed approach is able to achieve fewer numbers of fuzzy rules. Similar to the DFQL, epsiv-completeness criterion is used to generate fuzzy rules but the convergence capability of the ITPM is added to provide flexible fuzzy clustering. Experimental results and comparative studies with conventional fuzzy Q-learning (FQL), continuous-action Q-learning (CAQL), DFQL and its related developments, dynamic self generated fuzzy Q-learning (DSGFQL) and enhanced dynamic self generated fuzzy Q-learning (EDSGFQL), in wall-following task of a mobile robot are presented to demonstrate the superiority of the proposed approach.