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Volume 22 Issue 3

May  2005

Article Contents
Article >  ADVANCES IN ATMOSPHERIC SCIENCES  > 2005 >  22(3): 428-435

A Short-Term Climate Prediction Model Based on a Modular Fuzzy Neural Network

  • 1.

    Guangxi Research Institute of Meteorological Disasters Mitigation, Nanning 530022,Department of Computer Science, East China Normal University, Shanghai 200062,Guangxi Research Institute of Meteorological Disasters Mitigation, Nanning 530022


doi: 10.1007/BF02918756

  • In terms of the modular fuzzy neural network (MFNN) combining fuzzy c-mean (FCM) cluster and single-layer neural network, a short-term climate prediction model is developed. It is found from modeling results that the MFNN model for short-term climate prediction has advantages of simple structure, no hidden layer and stable network parameters because of the assembling of sound functions of the selfadaptive learning, association and fuzzy information processing of fuzzy mathematics and neural network methods. The case computational results of Guangxi flood season (JJA) rainfall show that the mean absolute error (MAE) and mean relative error (MRE) of the prediction during 1998-2002 are 68.8 mm and 9.78％, and in comparison with the regression method, under the conditions of the same predictors and period they are 97.8 mm and 12.28％ respectively. Furthermore, it is also found from the stability analysis of the modular model that the change of the prediction results of independent samples with training times in the stably convergent interval of the model is less than 1.3 mm. The obvious oscillation phenomenon of prediction results with training times, such as in the common back-propagation neural network (BPNN)model, does not occur, indicating a better practical application potential of the MFNN model.
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    • Manuscript History

    Manuscript received: 10 May 2005
    Manuscript revised: 10 May 2005
    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

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    A Short-Term Climate Prediction Model Based on a Modular Fuzzy Neural Network

    • 1. Guangxi Research Institute of Meteorological Disasters Mitigation, Nanning 530022,Department of Computer Science, East China Normal University, Shanghai 200062,Guangxi Research Institute of Meteorological Disasters Mitigation, Nanning 530022
    • Manuscript received: 2005-05-10
    • Manuscript revised: 2005-05-10

    Abstract: In terms of the modular fuzzy neural network (MFNN) combining fuzzy c-mean (FCM) cluster and single-layer neural network, a short-term climate prediction model is developed. It is found from modeling results that the MFNN model for short-term climate prediction has advantages of simple structure, no hidden layer and stable network parameters because of the assembling of sound functions of the selfadaptive learning, association and fuzzy information processing of fuzzy mathematics and neural network methods. The case computational results of Guangxi flood season (JJA) rainfall show that the mean absolute error (MAE) and mean relative error (MRE) of the prediction during 1998-2002 are 68.8 mm and 9.78％, and in comparison with the regression method, under the conditions of the same predictors and period they are 97.8 mm and 12.28％ respectively. Furthermore, it is also found from the stability analysis of the modular model that the change of the prediction results of independent samples with training times in the stably convergent interval of the model is less than 1.3 mm. The obvious oscillation phenomenon of prediction results with training times, such as in the common back-propagation neural network (BPNN)model, does not occur, indicating a better practical application potential of the MFNN model.

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