| 一种基于动态人工神经网络的Wiener模型辨识 |
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| 引用本文： | 李世华,吴福保,李 奇. 一种基于动态人工神经网络的Wiener模型辨识[J]. 控制理论与应用, 2000, 17(1): 92-95 |
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| 作者姓名： | 李世华 吴福保 李 奇 |
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| 作者单位： | 1. 东南大学自动控制系·南京,210096
2. 东南大学自动化研究所·南京,210096 |
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| 摘 要： | 提出了一种新的辨识模型对Ｗｉｅｎｅｒ模型进行辨识，该模型 线性动态神经元串联一静态网络模型组成，利用线性动态神经元对Ｗｉｅｎｅｒ模型的线性动态部分建模，利用静态ＢＰ网络逼近模型的静态非线性部分，并且给出了统一的ＢＰ辨识算法，仿真结果表明了该方法的有效性。
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| 关 键 词： | 人工神经网络 系统辨识 Wiener模型 |
| 收稿时间： | 1998-05-04 |
| 修稿时间： | 1998-05-04 |
Identification of Wiener Model Using Dynamic Artificial Neural Networks |
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| LI Shi-hu,WU Fu-bao and LI Qi. Identification of Wiener Model Using Dynamic Artificial Neural Networks[J]. Control Theory & Applications, 2000, 17(1): 92-95 |
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| Authors: | LI Shi-hu WU Fu-bao LI Qi |
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| Affiliation: | Department of Automatic Control,Southeast University, Nanjing,210096,P.R.China;Research Institute of Automation,Southeast University, Nanjing,210096,P.R.China;Research Institute of Automation,Southeast University, Nanjing,210096,P.R.China |
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| Abstract: | The problem ofidentification of a Wiener model is studied.The proposed identification method uses adynamic neural network (DANN) which consists of a linear dynamic neuron (LDN) in cascadewith a static BP neural network (SBP).A unified back-propagation algorithm is proposed toestimate the weights and the biases of the LDN and the SBP simultaneously.Numericalexamples are provided to show the efficiency of the proposed method. |
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| Keywords: | neural networks nonlinear systems identification Wiener model |
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