| 引用本文: | 吴德会.非线性动态系统的Wiener神经网络辨识法[J].控制理论与应用,2009,26(11):1192~1196.[点击复制] |
| WU De-hui.Identification method for nonlinear dynamic system using Wiener neural network[J].Control Theory and Technology,2009,26(11):1192~1196.[点击复制] |
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| 非线性动态系统的Wiener神经网络辨识法 |
| Identification method for nonlinear dynamic system using Wiener neural network |
| 摘要点击 2291 全文点击 1839 投稿时间：2008-05-29 修订日期：2009-01-05 |
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| DOI编号 10.7641/j.issn.1000-8152.2009.11.CCTA080545 |
| 2009,26(11):1192-1196 |
| 中文关键词 非线性动态系统 辨识 神经网络 Wiener模型 |
| 英文关键词 nonlinear dynamic system identification neural network Wiener model |
| 基金项目 国家自然科学基金资助项目(50705039); 中国博士后基金资助项目(20070420358). |
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| 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 novelWiener 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. |