The chaotic forecasting of power load is based on reconstructing phase space of the power load series.
电力负荷的混沌预测是建立在重构电力负荷序列的相空间基础之上的。
Based on the chaotic theory of reconstructing phase space, an improved local average nonlinear noise reduction method is presented.
基于混沌序列重构相空间理论，提出一种改进的局部平均非线性去噪方法。
The structure of the network is defined by integrating the theory of reconstructing phase space, which makes the efficient prediction information be contained in the network.
结合相空间重构理论确定网络结构，使网络能够包含有效的预测信息。
Based on nonlinear prediction ideas of reconstructing phase space, this paper presents a time delay BP neural network model, whose generalization is improved utilizing Bayes' regularization.
基于相空间重构的非线性预报思想，建立一个时滞的BP神经网络模型，采用贝叶斯正则化方法提高BP网络的泛化能力。
Based on nonlinear prediction ideas of reconstructing phase space, this paper presents a time delay BP neural network model, whose generalization is improved utilizing Bayes' regularization.
基于相空间重构的非线性预报思想，建立一个时滞的BP神经网络模型，采用贝叶斯正则化方法提高BP网络的泛化能力。
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