动态神经网络的模型检验

讨论了利用仅含一个隐层的前馈多层神经网络来辨识离散时间非线性动态系统时的模型检验问题。从估计理论出发，加上实际应用的考虑，提出了一种检验神经网络模型适用性的综合误差指标（ＳＥＩ）准则，并利用学习速度快、收敛性能好的递推预测误差（ＲＰＥ）学习算法，对两个例子进行了仿真计算。结果表明：综合误差指标准则可用来检验非线性动态系统的神经网络模型。与相关检验法相比，综合误差指标准则具有计算简单、使用方便等优点。最后，指出了今后的研究方向。

THE MODEL VALIDATION OF DYNAMIC NEURAL NETWORKS

This paper investigates the problem of the model validation in identifying discrete-time-nonlinear dynamic systems by using neural networks with a single hidden layer.Based on the estimation theory,a synthetic error-index(SEI)criterion for the neural network models has been developed.By using the powerful training algorithm of recursive prediction error (RPE),two simulated non-linear systems are studied,and the results show that the synthetic error-index criterion can be used to verify the dynamic neural network models.Furthermore,the proposed technique is much simple in calculation than that of the effective correlation tests.Finally,some problems required by further study are discussed.