Article: Efficient differential evolution algorithm-based optimisation of fuzzy prediction model for time series forecasting Journal: International Journal of Intelligent Information and Database Systems (IJIIDS) 2013 Vol.7 No.3 pp.225 - 241 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. Inderscience Publishers - linking academia, business and industry through research

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

Authors: Ming-Feng Han; Chin-Teng Lin; Jyh-Yeong Chang

Addresses: Institute of Electrical Control Engineering, National Chiao Tung University, 1001 University Road, Hsinchu, 300, Taiwan ' Institute of Electrical Control Engineering, National Chiao Tung University, 1001 University Road, Hsinchu, 300, Taiwan ' Institute of Electrical Control Engineering, National Chiao Tung University, 1001 University Road, Hsinchu, 300, Taiwan

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.

Keywords: fuzzy modelling; differential evolution; neuro-fuzzy system optimisation; evolutionary algorithm; prediction models; time series forecasting; simulation; mutation strategy; entropy measure; neural networks; fuzzy logic.

DOI: 10.1504/IJIIDS.2013.053824

International Journal of Intelligent Information and Database Systems, 2013 Vol.7 No.3, pp.225 - 241

Received: 16 Mar 2012
Accepted: 23 Jul 2012
Published online: 31 Mar 2014 *

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

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