中文摘要：

提出了一种新的Wiener神经网络结构并将其应用于非线性动态系统辨识问题．首先，用Wiener模型对非线性动态系统进行描述，将其分解成线性动态子环节串接非线性静态增益的形式．其次，设计一种新型的神经网络结构，使网络权值对应于相应的Wiener模型参数；并推导了基于反向传播的网络权值调整方法．最后，通过网络迭代训练，可同时得到线性动态子环节和非线性静态增益的模型参数．通过一个Wiener模型的数值仿真来验证方法的有效性，仿真结果表明所提辨识方法切实可行．

英文摘要：

A novel Wiener neural network structure is presented and applied to nonlinear dynamic system identification. Firstly, the nonlinear dynamic system is described by a Wiener model which consists of a linear dynamic part in cascade with a nonlinear static gain. Secondly, a novel neural network structure is designed, the weights in which are corresponding with the parameters of the Wiener model. Thirdly, backward-propagation methods for the adjustment of weights in the network are discussed. Finally, parameters of the linear dynamic part and the nonlinear static gain in the Wiener model are determined simultaneously by iterative training. A numerical simulation of Wiener model is provided to validate the effectiveness. Simulation results show that the suggested identification schemes are practically feasible.