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NONPARAMETRIC APPROACH TO IDENTIFYING NARX SYSTEMS

Journal of Systems Science & Complexity 2010年第1期23卷 3-21页

作者： Qijiang SONG·Han-Pu CHEN Key Laboratory of Systems and Control,Institute of Systems Science,Academy of Mathematics and Systems Science,Chinese Academy of Sciences,Beijing 100190,China Key Laboratory of Systems and Control Institute of Systems Science Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China

This paper considers identification of the nonlinear autoregression with exogenous inputs(NARX system).The growth rate of the nonlinear function is required be not faster than linear withslope less than *** value of f(·) at any fixed point is recursively estimated by the stochasticapproximation (SA) algorithm with the help of kernel *** consistency of the estimatesis established under reasonable conditions,which,in particular,imply stability of the *** simulation is consistent with the theoretical analysis.

关键词： α-mixing geometrically ergodic Markov chains NARX nonparametric recursive estimate stochastic approximation strongly consistent.

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CFAR assessment of covariance matrix estimators for non-Gaussian clutter

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Science China(Information Sciences) 2010年第11期53卷 2343-2351页

作者： HE You, JIAN Tao, SU Feng, QU ChangWen & PING DianFa Research Institute of Information Fusion, Naval Aeronautical and Astronautical University, Yantai 264001, China Research Institute of Information Fusion Naval Aeronautical and Astronautical University Yantai China

In the non-Gaussian clutter modeled as independent and identically distributed spherically invariant random vectors, three estimators of sample covariance matrix (SCM), normalized sample covariance matrix (NSCM) and the corresponding recursive estimator (NSCM-RE) are analyzed. Based on the uniform theorem, three corresponding adaptive normalized matched filters (ANMF) are evaluated from the standpoint of constant false alarm rate (CFAR) property. The theoretical results demonstrate that the SCM-ANMF is only CFAR to the normalized clutter covariance matrix (NCCM); the NSCM-ANMF is only CFAR to the clutter power level; and the NSCM-RE-ANMF with finite number of iterations is still not CFAR to the NCCM. To ensure CFAR property of ANMF, an adaptive estimator (AE) is devised. Moreover, with AE as the initialization matrix for the iterations, the AE-RE is proposed. With finite number of iterations, the corresponding AE-REANMF guarantees CFAR property to both of the NCCM and the clutter power level. Finally, the performance assessment conducted by Monte Carlo simulation confirms the effectiveness of the proposed detectors.

关键词： non-Gaussian background covariance matrix estimate normalized matched filter constant false alarm rate recursive estimate

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recursive Identification of Wiener Systems with General Inputs

Recursive Identification of Wiener Systems with General Inpu...

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第二十七届中国控制会议

作者： Fang Haitao,Jiang Yueping Key Laboratory of Systems and Control,Chinese Academy of Sciences,Beijing 100190,P.R.China

The paper concerns the identification of Wiener system consisting of a linear subsystem followed by a static *** on integration by parts and Fourier transform,we give two recursive algorithms for estimating the coefficients of the linear subsystem with random inputs with possibly non-Gaussian *** algorithm proposed in the paper includes the one given in Hu and Chen[2005]as a special case for the single-input single-output（SISO） systems,while for the multi-input multi-output（MIMO） systems it works in the case where there is no priori structural assumption made on *** numerical examples are given to justify the theoretical analysis.

关键词： Wiener system Fourier methods recursive estimate Single-input single-output Multi-input multi-output

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recursive Identification of Wiener Systems with Nonparametric Nonlinearity

Recursive Identification of Wiener Systems with Nonparametri...

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第二十四届中国控制会议

作者： Xiaoli Hu, Hanfu Chen Key Laboratory of Systems and Control, Institute of Systems Science, AMSS, Chinese Academy of Sciences, Beijing 100080, P.R. China

＜正＞ The paper concerns identification of the Wiener system consisting of a linear subsystem followed by a nonlinear memoryless block f（?） with no structure assumption, in particular, with no invertibility assumption. recursive estimates are given for coefficients of the linear subsystem and for f（v） at any fixed v. All estimates given in the paper converge to the true values with probability one. A numerical example is given to justify the theoretical results.

关键词： Wiener system nonparametric nonlinearity recursive estimate strong consistency stochastic approximation

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recursive Identification for Hammerstein System with ARX Subsystem

Recursive Identification for Hammerstein System with ARX Sub...

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第25届中国控制会议

作者： Zhao Wenxiao, Chen Hanfu Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100080

＜正＞Identification is considered for the Hammerstein system consisting of a static nonlinear block f（·） followed by an ARX subsystem, when the system output is observed with noise. No assumption is made on the structure of f（·）. recursive estimates are given for coefficients of the ARX subsystem and for the value of f（u） at any u. All estimates are convergent to the true values with probability one. Numerical examples are provided justifying the theoretical analysis.

关键词： Hammerstein system ARX nonparametric nonlinearity recursive estimate strong consistency stochastic approximation.

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