[1]LIU Fucai,DOU Jinmei,WANG Shuen.Robust fuzzy prediction of the chaotic time series based on the MFOA and LW[J].CAAI Transactions on Intelligent Systems,2014,9(4):425-431.[doi:10.3969/j.issn.1673-4785.201305083]
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Robust fuzzy prediction of the chaotic time series based on the MFOA and LW

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 9 Number of periods: 2014 4 Page number: 425-431 Column: 学术论文—智能系统 Public date: 2014-08-25
Title:
Robust fuzzy prediction of the chaotic time series based on the MFOA and LW
Author(s):
LIU Fucai;  DOU Jinmei;  WANG Shu’en
Key Laboratory of Industrial Computer Control Engineering of Hebei Province, Yanshan University, Qinhuangdao 066004, China
Keywords:
modified fruit fly optimization algorithm; least Wilcoxon method; outliers; Mackey-Glass chaotic time series; T-S fuzzy model; fuzzy prediction
CLC:
TP15
DOI:
10.3969/j.issn.1673-4785.201305083
Abstract:
Focusing on the prediction of the chaotic time series containing outliers, a hybrid learning method based on the modified fruit fly optimization algorithm(MFOA) and the least Wilcoxon (LW)method is proposed in order to train the T-S fuzzy model. The purpose of this is to improve the accuracy and robustness of fuzzy modeling for nonlinear systems. Firstly, the MFOA is used to optimize the antecedent parameters of the Gaussian membership function with the advantages of ease of transformation of such a concept into program code and a high convergence speed, which can improve the identification accuracy and convergence speed in fuzzy modeling. Secondly, the least Wilcoxon method is applied to identify the consequential parameters of the model. When the outliers occur in the training data, the strong robustness of the LW with the outliers is effective for improving the sensitivity of the traditional least mean square method. Finally, a simulation experiment is conducted on the prediction of the Mackey-Glass chaotic time series, and the comparisons of the prediction results by different optimization methods are done to verify the superiority of the modified fruit fly optimization algorithm and in the case of outliers existing, the simulation results show the effectiveness and robustness of this proposed method.
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