Two-stage method for system identification based on asymptotic theory
Two-stage method for system identification based on asymptotic theory
复制标题
基于渐近理论的系统辨识两阶段法
DOI：
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发表时间：
2008
期刊：
影响因子：
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通讯作者：
Xu, Zuhua
中科院分区：
文献类型：
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作者：
Qian, Jixin;Zhao, Jun;Xu, Zuhua
提出了一种基于渐近理论的两阶段系统辨识方法，首先利用高阶ARX模型得到无偏估计及其频率方差，然后利用OE结构和MDL准则对各子模型进行降阶，将多变量模型结构选择转化为简单的SISO问题，通过频率方差实现模型验证，该方法解决了多变量系统模型阶次选择和模型验证的难题，利用多变量测试信号减少了被试时间和对系统运行的干扰，应用结果表明了该方法的有效性。
A two-stage method for system identification based on asymptotic theory was proposed.Firstly, an unbiased estimation and its frequency variance were obtained by the high-order ARX model.Then each sub model was reduced by the OE structure and MDL criterion.It translated multivariable model structure selection into a simple SISO problem and realized model validation through frequency variance, which resolved the difficult problem of model order selection and model validation for the multivariable system.The use of multivariable test signal reduced time for plant test and disturbance to operation.The application results were given to demonstrate the effectiveness of the identification method.