Efficient differential evolution algorithm-based optimisation of fuzzy prediction model for time series forecasting

Han, MF; Lin, CT; Chang, JY

Efficient differential evolution algorithm-based optimisation of fuzzy prediction model for time series forecasting

Han, MF Lin, CT

Chang, JY

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Publication Type:: Journal Article
Citation:: International Journal of Intelligent Information and Database Systems, 2013, 7 (3), pp. 225 - 241
Issue Date:: 2013-05-20

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Field	Value	Language
dc.contributor.author	Han, MF	en_US
dc.contributor.author	Lin, CT https://orcid.org/0000-0001-8371-8197	en_US
dc.contributor.author	Chang, JY	en_US
dc.date.issued	2013-05-20	en_US
dc.identifier.citation	International Journal of Intelligent Information and Database Systems, 2013, 7 (3), pp. 225 - 241	en_US
dc.identifier.issn	1751-5858	en_US
dc.identifier.uri	http://hdl.handle.net/10453/116968
dc.description.abstract	This paper proposes a differential evolution algorithm with efficient mutation strategy (DEEMS) for fuzzy prediction model (FPM) optimisation. The proposed DEEMS uses a modified mutation operation which considers local information nearby each individual to trade-off between the exploration ability and the exploitation ability. In the FPM design, we adopt an entropy measure method to determine the number of rules. Initially, there is no rule in the FPM. Fuzzy rules are automatically generated by entropy measure. Subsequently, the DEEMS algorithm is performed to optimise all the free parameters. During evolution process, the scale factor and crossover rate in the DEEMS algorithm are adjusted by adaptive parameter tuning strategy for each generation. It is thus helpful to enhance the robustness of the DEEMS algorithm. In the simulation, the proposed FPM with DEEMS model (FPM-DEEMS) is applied to two real world problems. Results show that the proposed FPM-DEEMS model obtains better performance than other algorithms. Copyright © 2013 Inderscience Enterprises Ltd.	en_US
dc.relation.ispartof	International Journal of Intelligent Information and Database Systems	en_US
dc.relation.isbasedon	10.1504/IJIIDS.2013.053824	en_US
dc.title	Efficient differential evolution algorithm-based optimisation of fuzzy prediction model for time series forecasting	en_US
dc.type	Journal Article
utslib.citation.volume	3	en_US
utslib.citation.volume	7	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0804 Data Format	en_US
utslib.for	0806 Information Systems	en_US
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	closed_access
pubs.issue	3	en_US
pubs.publication-status	Published	en_US
pubs.volume	7	en_US

Abstract:

This paper proposes a differential evolution algorithm with efficient mutation strategy (DEEMS) for fuzzy prediction model (FPM) optimisation. The proposed DEEMS uses a modified mutation operation which considers local information nearby each individual to trade-off between the exploration ability and the exploitation ability. In the FPM design, we adopt an entropy measure method to determine the number of rules. Initially, there is no rule in the FPM. Fuzzy rules are automatically generated by entropy measure. Subsequently, the DEEMS algorithm is performed to optimise all the free parameters. During evolution process, the scale factor and crossover rate in the DEEMS algorithm are adjusted by adaptive parameter tuning strategy for each generation. It is thus helpful to enhance the robustness of the DEEMS algorithm. In the simulation, the proposed FPM with DEEMS model (FPM-DEEMS) is applied to two real world problems. Results show that the proposed FPM-DEEMS model obtains better performance than other algorithms. Copyright © 2013 Inderscience Enterprises Ltd.

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http://hdl.handle.net/10453/116968