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

Chou, KP; Lin, CT; Lin, WC

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

Chou, KP Lin, CT

Lin, WC

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Publication Type:: Conference Proceeding
Citation:: 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings, 2019, pp. 1502 - 1509
Issue Date:: 2019-06-01

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Field	Value	Language
dc.contributor.author	Chou, KP	en_US
dc.contributor.author	Lin, CT https://orcid.org/0000-0001-8371-8197	en_US
dc.contributor.author	Lin, WC	en_US
dc.date.available	2021-08-12T19:01:02Z
dc.date.issued	2019-06-01	en_US
dc.identifier.citation	2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings, 2019, pp. 1502 - 1509	en_US
dc.identifier.isbn	9781728121536	en_US
dc.identifier.uri	http://hdl.handle.net/10453/136593
dc.description.abstract	© 2019 IEEE. 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.	en_US
dc.relation.ispartof	2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings	en_US
dc.relation.isbasedon	10.1109/CEC.2019.8790334	en_US
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.title	A self-adaptive artificial bee colony algorithm with local search for TSK-type neuro-fuzzy system training	en_US
dc.type	Conference Proceeding
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	open_access	*
pubs.publication-status	Published	en_US

Abstract:

© 2019 IEEE. 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.

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http://hdl.handle.net/10453/136593