Based on the analogue of general uncertainty relation in information transmitting processes, by the introduction of multi-correlative coefficient R to manifest the complexity of the function and the hidden unit h to manifest the complexity of the network structure, a general uncertainty relation between the learning ability and the generalization ability suited to overfitting of BP neural network was revealed in the modeling of BP neural network. Tests of numerical simulation for 12 kinds of complicated function were carried out to determine the value distribution (I X 10(-5) similar to5 X 10(-4)) of overfitting parameter in the uncertainty relation. Based on the uncertainty relation, the judgement of overfitting in the training process of gived sample sets using BP network was given.
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机译：基于信息传递过程中一般不确定性关系的类比，通过引入多重相关系数R表示函数的复杂性和隐藏单元h表示网络结构的复杂性，学习之间的一般不确定性关系在BP神经网络的建模中揭示了适合BP神经网络过度拟合的能力和泛化能力。进行了12种复杂函数的数值模拟测试，以确定不确定性关系中过拟合参数的值分布（I X 10（-5）类似于5 X 10（-4）。基于不确定性关系，给出了使用BP网络对给定样本集训练过程中过度拟合的判断。
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