Recursive Identification of MIMO Wiener Systems

Mu B.-Q.; Chen H.-F.

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Recursive Identification of MIMO Wiener Systems

机译：MIMO Wiener系统的递归识别

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摘要

Stochastic approximation (SA) algorithms are proposed to identify a multi-input and multi-output (MIMO) Wiener system, in which the system input is taken to be a sequence of independent and identically distributed (i.i.d.) Gaussian random vectors $u_{k}in{cal N}(0,I)$. The algorithm for identifying the nonlinear part is designed with multi-variable kernel functions. Under suitable conditions, we show that the estimates of the coefficients of the linear subsystem and of the values of the nonlinear function converge to the respective true values with probability one.

机译：提出了随机逼近（SA）算法来识别多输入多输出（MIMO）Wiener系统，其中系统输入被视为一系列独立且均匀分布的（iid）高斯随机矢量$ u_ {k } in {cal N}（0，I）$。利用多变量核函数设计了用于识别非线性部分的算法。在适当的条件下，我们表明线性子系统的系数和非线性函数的值的估计以概率1收敛到各自的真实值。

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《Automatic Control, IEEE Transactions on》 |2013年第3期|p.802-808|共7页
作者
Mu B.-Q.; Chen H.-F.;
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作者单位

B. Mu is with the Key Laboratory of Systems and Control, Institute of Systems Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, P. R. China.(email:bqmu@amss.ac.cn);

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正文语种 eng
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Approximation algorithms; Approximation methods; Convergence; Estimation; Kernel; MIMO; Vectors; formula formulatype="inline"tex Notation="TeX"$alpha$/tex /formula-mixing; MIMO Wiener system; stochastic approximation; strong consistency;

机译：近似算法;近似方法;收敛;估计;核心;MIMO;向量;formula Formulatype =“ inline” tex Notation =“ TeX” $ alpha $ / tex / formula-混合;MIMO Wiener系统;随机近似一致性强;

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Recursive Identification of MIMO Wiener Systems

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