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The paper proposes a novel dynamic weight neural network ensemble model (DW-NNE). The Bagging algorithm generates certain neural network individuals which then are selected by the K-means clustering algorithm. In order to solve the problem that K-value cannot be selected automatically in the K-means clustering algorithm when conducting the selection of individuals, the K-value optimization algorithm based on distance cost function is put forward to find the optimal K-values. In addition, for the integrated output problems, the paper proposes a dynamic weight model which is based on fuzzy neural network with accordance to the ideas of dynamic weight. 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