| 基于高阶神经网络扩展卡尔曼滤波器算法的非线性动态系统辨识 |
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| 引用本文： | 刘春梅,沈毅,胡恒章,葛升民. 基于高阶神经网络扩展卡尔曼滤波器算法的非线性动态系统辨识[J]. 哈尔滨工业大学学报, 2000, 0(2) |
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| 作者姓名： | 刘春梅 沈毅 胡恒章 葛升民 |
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| 作者单位： | 哈尔滨工业大学控制工程系!黑龙江哈尔滨150001 |
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| 摘 要： | 针对非线性动态系统辨识 ,采用高阶神经网络和径向基函数网络相结合的方法 ,神经网络的连接权值可作为系统的未知参数 ,用扩展卡尔曼滤波器 (EKF)算法来估计 ,确保了该方法的快速收敛 .具体模型的仿真结果表明该方法能快速收敛 ,并能方便的用于在线辨识 .
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| 关 键 词： | 高阶神经网络 扩展卡尔曼滤波器 非线性挠性结构 动态系统辨识 径向基函数网络 |
Identification of nonlinear dynamic system based on high-order neural networks and extended kalman filter algorithm |
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| LIU Chun mei,SHEN Yi,HU Heng zhang,GE Sheng min. Identification of nonlinear dynamic system based on high-order neural networks and extended kalman filter algorithm[J]. Journal of Harbin Institute of Technology, 2000, 0(2) |
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| Authors: | LIU Chun mei SHEN Yi HU Heng zhang GE Sheng min |
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| Abstract: | High order neural networks and radial basis function networks are combined to identify nonlinear dynamic system. The linkweights of neural networks can be taken as the unknown parameters of system and can be estimated by EKF algorithm so that the fast convergence is assured. The simulations of specific model imply that this method is of fast convergence and can be used in on-line identification expediently. |
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| Keywords: | high order neural networks extended Kalman filter nonlinear flexible structure identification of dynamic system radial basis function networks |
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