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首页> 外文期刊>IEEE Transactions on Automatic Control >Identification of multivariable stochastic linear systems viapolyspectral analysis given noisy input-output time-domain data
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Identification of multivariable stochastic linear systems viapolyspectral analysis given noisy input-output time-domain data

机译：输入-输出时域数据嘈杂，通过多光谱分析识别多变量随机线性系统

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摘要

The paper considers the problem of identification of unknown parameters of multivariable, linear “errors-invariables” models. Attention is focused on frequency-domain approaches where the integrated polyspectrum (bispectrum or trispectrum) of the input and the integrated cross-polyspectrum, respectively, of the given time-domain input-output data are exploited. Two new classes of parametric frequency-domain approaches are proposed and analyzed. An integrated polyspectrum-based persistence of excitation condition on system input is defined. Both classes of the parameter estimators are shown to be strongly consistent in any measurement noise sequences with vanishing bispectra when integrated bispectrum-based approaches are used. The proposed parameter estimators are shown to be strongly consistent in Gaussian measurement noise when integrated trispectrum-based approaches are used. The input to the system need not be a linear process but must have nonvanishing bispectrum or trispectrum
机译：本文考虑了多变量线性“错误不变”模型的未知参数识别问题。注意集中在频域方法上，其中利用了给定时域输入-输出数据的输入的综合多谱（双谱或三谱）和综合的交叉多谱。提出并分析了两类新的参数化频域方法。定义了基于多光谱的基于激励的系统输入持续性持久性。当使用基于双谱的集成方法时，两类参数估计器在双谱消失的任何测量噪声序列中都显示出高度一致。当使用基于综合三光谱的方法时，建议的参数估计器在高斯测量噪声中显示出高度一致。系统的输入不必是线性过程，而必须具有不变的双光谱或三光谱

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