標題: A self-adaptive artificial bee colony algorithm with local search for TSK-type neuro-fuzzy system training
作者: Chou, Kuang-Pen
Lin, Chin-Teng
Lin, Wen-Chieh
資訊工程學系
Department of Computer Science
關鍵字: Evolutionary algorithm (EA);Artificial bee colony (ABC)optimization;Neuro-fuzzy system (NFS)
公開日期: 1-Jan-2019
摘要: In this paper, we introduce a self-adaptive artificial bee colony (ABC) algorithm for learning the parameters of a Takagi-Sugeno-Kang-type (TSK-type) neuro-fuzzy system (NFS). The proposed NFS learns fuzzy rules for the premise part of the fuzzy system using an adaptive clustering method according to the input-output data at hand for establishing the network structure. All the free parameters in the NFS, including the premise and the following TSK-type consequent parameters, are optimized by the modified ABC (MABC) algorithm. Experiments involve two parts, including numerical optimization problems and dynamic system identification problems. In the first part of investigations, the proposed MABC compares to the standard ABC on mathematical optimization problems. In the remaining experiments, the performance of the proposed method is verified with other metaheuristic methods, including differential evolution (DE), genetic algorithm (GA), particle swarm optimization (PSO) and standard ABC, to evaluate the effectiveness and feasibility of the system. The simulation results show that the proposed method provides better approximation results than those obtained by competitors methods.
URI: http://hdl.handle.net/11536/153679
ISBN: 978-1-7281-2153-6
期刊: 2019 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC)
起始頁: 1502
結束頁: 1509
Appears in Collections:Conferences Paper