| Maximum Likelihood Estimation of the Identification Parameters and Its Correction |
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| 作者姓名： | An Kai Ma Jiaguang & Fu Chengyu Institute of Optics and Electronics Chinese Academy of Sciences Chengdu P. R. China |
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| 作者单位： | An Kai，Ma Jiaguang & Fu Chengyu Institute of Optics and Electronics，Chinese Academy of Sciences，Chengdu 610041，P. R. China |
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| 摘 要： | By taking the subsequence out of the input-output sequence of a system polluted by white noise, an independent observation sequence and its probability density are obtained and then a maximum likelihood estimation of the identification parameters is given. In order to decrease the asymptotic error, a corrector of maximum likelihood (CML) estimation with its recursive algorithm is given. It has been proved that the corrector has smaller asymptotic error than the least square methods. A simulation example shows that the corrector of maximum likelihood estimation is of higher approximating precision to the true parameters than the least square methods.
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Maximum Likelihood Estimation of the Identification Parameters and Its Correction |
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| An Kai,Ma Jiaguang & Fu Chengyu Institute of Optics and Electronics,Chinese Academy of Sciences,Chengdu ,P. R. China.Maximum Likelihood Estimation of the Identification Parameters and Its Correction[J].Journal of Systems Engineering and Electronics,2002,13(4). |
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| Authors: | AN Kai Ma Jiaguang Fu Chengyu |
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| Abstract: | By taking the subsequence out of the input-output sequence of a system polluted by white noise, an independent observation sequence and its probability density are obtained and then a maximum likelihood estimation of the identification parameters is given. In order to decrease the asymptotic error, a corrector of maximum likelihood (CML) estimation with its recursive algorithm is given. It has been proved that the corrector has smaller asymptotic error than the least square methods. A simulation example shows that the corrector of maximum likelihood estimation is of higher approximating precision to the true parameters than the least square methods. |
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| Keywords: | Probability density Noise Least square methods Corrector of maximum likelihood estimation |
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