零极值目标函数系统辨识算法

毕云龙; 来逢昌; 刘鹏

doi:10.3724/SP.J.1146.2007.00158

零极值目标函数系统辨识算法

doi: 10.3724/SP.J.1146.2007.00158

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出版历程
- 收稿日期: 2007-01-25
- 修回日期: 2007-07-23
- 刊出日期: 2008-09-19

A System Identification Algorithm by Minimizing the Zero-Minimum Target Function

摘要

摘要: 该文提出一种零极值目标函数最小化系统辨识算法。目标函数为系统均方误差与系统噪声方差之差的平方，其极小值为零。在系统辨识过程中采用滑动平均法在线估计系统均方误差、输入自相关矩阵以及输入与期望响应的互相关向量。推导出自适应滤波器权值向量的更新表达式。算法的步长能够根据统计量自适应地调整，使得在得到较小稳态误差的同时提高算法收敛速度。分析了算法的稳定性，得到了算法收敛的条件。对比实验结果表明，该算法具有更快的收敛速度，更小的稳态误差以及更好的稳定性。
- 系统辨识 /
- 自适应滤波 /
- 变步长LMS算法
Abstract: A system identification algorithm which is based on minimizing the zero-minimum target function is proposed. The target function presented in this paper is the square of the difference between the mean square error and the variance of the noise. And the minimum of the target function is zero. During the system identification process, the system mean square error, the correlation matrix of the tap inputs and the cross-correlation vector between the tap inputs of the adaptive filter and the desired response are estimated online by employing moving average method. Then, a recursive relation for updating the tap-weight vector is derived. The step size of the algorithm presented here could be adjusted adaptively according to the value of the statistics obtained online, which makes the algorithm be capable of accelerating the speed of convergence without sacrificing the steady state error. Further, the analysis of stability of the algorithm is provided, and then the convergent condition is obtained. The experimental results of the system identification setting demonstrate that the algorithm proposed here has faster convergence and smaller steady state error comparing with the algorithms mentioned in other literatures. Its also shown that the algorithm has better stability.
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[1] 邹谋炎. 反卷积和信号复原. 2001 年3 月第1 版, 北京: 国防工业出版社, 2004: 1-10.Zou Mou-yan. Deconvolution and Signal Recovery. Mar. 2001first Edition, Beijing: National Defence Industry Press, 2004:1-10. [2] Simon Haykin. Adaptive Filter Theory. Fourth Edition,Beijing: Publishing House of Electronics Industry. 2002:Chapter 2-Chapter 5. [3] 覃景繁, 欧阳景正. 一种新的变步长自适应滤波算法. 数据采集与处理, 1997, 12(3): 171-174.Qin Jing-fan and Ouyang Jing-zheng. A novel variable stepLMS adaptive filtering algorithm based on Sigmoid function.Journal of Data Acquisition Processing, 1997, 12(3):171-174. [4] 罗小东, 贾振红, 王强. 一种新的变步长LMS 自适应滤波算法. 电子学报, 2006, 34(6): 1123-1126.Luo Xiao-dong, Jia Zhen-hong, and Wang Qiang. A newvariable step size LMS adaptive filtering algorithm. ActaElectronica Sinica, 2006, 34(6): 1123-1126. [5] Sanubari J. A new variable step size method for the LMSadaptive filter[J].The 2004 IEEE Asia-Pacific Conference onCircuit and System, Tainan, Taiwan.2004, 1:501-504 [6] 吕振肃, 黄石. 一种改进的变步长ELMS 算法[J].电子与信息学报.2005, 27(10):1524-1526浏览 [7] Yu Xiao and Wang Qicai. An extended LMS algorithm inANC. ICNNSP95, Nanjing, China, 1995: 737-740. [8] Mathews V John and Xie Zhenhua. A stochastic gradientadaptive filter with gradient adaptive step size[J].IEEE Trans.on Signal Processing.1993, 41(6):2075-2087 [9] Kripasagar Venkat and Issa M S Panahi. SIMO blind systemidentification and order determination. 2006 IEEEInternational Conference on Acoustic, Speech and SignalProcessing, Toulouse, France, 2006, 3: 121-124. [10] Jernimo Arenas-Garca, Anbal R Figueiras-Vidal, and AliH Sayed. Mean-square performance of a convex combinationof two adaptive filters[J].IEEE Trans. on Signal Processing.2006, 54(3):1078-1090 [11] Nikolay D Gaubitch, Md Kamrul Hasan, and Patrick ANaylor. Generalized optimal step-size for blind multichannelLMS system identification[J].IEEE Signal Processing Letters.2006, 13(10):624-627 [12] Se Bin Im and Hyung Jin Choi. Iterative channel estimationwith moving average filter in OFDM packet transmissionsystem. 2006 Asia-Pacific Conference on Communication,Busan, 2006: 1-5. [13] Gorniewicz L, Marano S A, and Slosarski M. Fixed points ofcontractive multivalued maps[J].Proceedings of the AmericanMathematical Society.1996, 124(9):2675-2683

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