A self-adaptive artificial bee colony algorithm with local search for TSK-type neuro-fuzzy system training

Kuang Pen Chou, Chin Teng Lin, Wen Chieh Lin

研究成果: Conference contribution同行評審

3 引文 斯高帕斯(Scopus)

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.

原文English
主出版物標題2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings
發行者Institute of Electrical and Electronics Engineers Inc.
頁面1502-1509
頁數8
ISBN(電子)9781728121536
DOIs
出版狀態Published - 6月 2019
事件2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Wellington, New Zealand
持續時間: 10 6月 201913 6月 2019

出版系列

名字2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings

Conference

Conference2019 IEEE Congress on Evolutionary Computation, CEC 2019
國家/地區New Zealand
城市Wellington
期間10/06/1913/06/19

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