将经验模式分解和多层前向网络的交叉覆盖算法相结合，提出一种时间序列相似模式的匹配算法。

This paper proposes an effective time series matching method by combining the empirical mode decomposition (EMD) with the alternative covering algorithm.

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基于前向型神经网络理论的时间序列分析跳出了传统的建立主观模型的局限，通过时间序列的内在规律作出分析与预测。

Time series analysis based on neural networks theory cross through traditional frame of subjective model draw out prediction on the inner rules of linear time series data.

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此研究结果表明，在混沌加速BP算法的支持下，三层前向神经网络可用来快速处理混沌光学时间序列以进行相应的动力学重构。

The computer simulation result shows that, the three layers foreward neural network, if trained with the chaos speedup BP algorithm, is indeed a fine identifier with less training iteration…

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