title: An evolutionary approach toward dynamic self-generated fuzzy inference systems.
abstract: An evolutionary approach toward automatic generation of fuzzy inference systems (FISs), termed evolutionary dynamic self-generated fuzzy inference systems (EDSGFISs), is proposed in this paper. The structure and parameters of an FIS are generated through reinforcement learning, whereas an action set for training the consequents of the FIS is evolved via genetic algorithms (GAs). The proposed EDSGFIS algorithm can automatically create, delete, and adjust fuzzy rules according to the performance of the entire system, as well as evaluation of individual fuzzy rules. Simulation studies on a wall-following task by a mobile robot show that the proposed EDSGFIS approach is superior to other related methods.
authors.affiliation: School of Electrical and Electronic Engineering, Singapore Polytechnic, Singapore 139651. zhouyi@sp.edu.sg
authors.forename: Yi
authors.initials: Y
authors.lastname: Zhou
authors.forename: Meng Joo
authors.initials: MJ
authors.lastname: Er
mesh_terms: D000465:Algorithms; D005075:Biological Evolution; D003198:Computer Simulation; D005246:Feedback; D017143:Fuzzy Logic; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D011382:Programming, Linear; D013598:Systems Theory