Automatic generation of fuzzy inference systems via unsupervised learning

Authors:
Meng Joo Er;Yi Zhou
Affiliations:
School of Electrical and Electronic Engineering, Nanyang Technological University, S1, 50 Nanyang Ave, Singapore 639798, Singapore;School of Electrical and Electronic Engineering, Singapore Polytechnic, 500 Dover Road, Singapore 139651, Singapore
Venue:
Neural Networks
Year:
2008

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Abstract

In this paper, a novel approach termed Enhanced Dynamic Self-Generated Fuzzy Q-Learning (EDSGFQL) for automatically generating Fuzzy Inference Systems (FISs) is presented. In the EDSGFQL approach, structure identification and parameter estimations of FISs are achieved via Unsupervised Learning (UL) (including Reinforcement Learning (RL)). Instead of using Supervised Learning (SL), UL clustering methods are adopted for input space clustering when generating FISs. At the same time, structure and preconditioning parts of a FIS are generated in a RL manner in that fuzzy rules are adjusted and deleted according to reinforcement signals. The proposed EDSGFQL methodologies can automatically create, delete and adjust fuzzy rules dynamically. Simulation studies on wall-following and obstacle avoidance tasks by a mobile robot show that the proposed approach is superior in generating efficient FISs.