| Nonlinear Time-Varying Systems Identification Using Basis Sequence Expansions Combined with Neural Networks |
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| 作者姓名： | 顾成奎 王正欧 孙雅明 |
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| 作者单位： | [1]InstituteofSystemsEngineering,TianjinUniversity,Tianjin300072,China [2]SchholofElectricalandAutomationEngineering,Tianjin300072,China |
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| 基金项目： | SupportedbyNationalNaturalScienceFoundationofChina(No . 60 2 0 4 0 1 2 ) |
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| 摘 要： | A new method for identifying nonlinear time-vaying systems with unknown structure is presented,The method extends the application ar5ea of basis sequence identification.The essential idea is to utilize the learning and nonlinear approximating ability of neural networks to model the non-linearity of the system,characterize time-varying dynamics of the system by the time-varying parametric vector of the network ,then the parametric vector of the network is approximated by a weighted sum of known basis sequences,Because of black-box modeling ability of neural networks,the presented method can identify noninear time-varying systems with unknown structure,In order to improver the real-time capability of the algorithm ,the neural network is trained by a simple fast learning algorthm based on local least squares presented by the authors,The effectiveness and the perfomence of the method are demonstrate3d by some simulation results.
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| 关 键 词： | 非线性时变系统 神经网络 系统识别 快速学习算法 权重 基序扩展 |
Nonlinear Time-Varying Systems Identification Using Basis Sequence Expansions Combined with Neural Networks |
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| GU Cheng kui ,WANG Zheng ou ,SUN Ya ming.Nonlinear Time-Varying Systems Identification Using Basis Sequence Expansions Combined with Neural Networks[J].Transactions of Tianjin University,2003,9(1):71-74. |
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| Authors: | GU Cheng kui WANG Zheng ou SUN Ya ming |
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| Institution: | 1. Institute of Systems Engineering, Tianjin University,Tianjin 300072,China
2. School of Electrical and Automation Engineering , Tianjin University,Tianjin 300072,China |
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| Abstract: | A new method for identifying nonlinear time varying systems with unknown structure is presented. The method extends the application area of basis sequence identification. The essential idea is to utilize the learning and nonlinear approximating ability of neural networks to model the non linearity of the system, characterize time varying dynamics of the system by the time varying parametric vector of the network, then the parametric vector of the network is approximated by a weighted sum of known basis sequences. Because of black box modeling ability of neural networks, the presented method can identify nonlinear time varying systems with unknown structure. In order to improve the real time capability of the algorithm, the neural network is trained by a simple fast learning algorithm based on local least squares presented by the authors. The effectiveness and the performance of the method are demonstrated by some simulation results. |
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| Keywords: | nonlinear time varying systems identification basis sequence expansions neural networks |
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