结合贝叶斯网络和神经网络，提出了一种建立数据驱动型的动态线性回归系统模型的方法。

A new method was represented to model dynamic linear regression system driven by data, in which a bayesian network was combined with the RBF neural network.

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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.

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