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

Bi Yun-Long; Lai  Feng-Chang; Liu Peng

doi:10.3724/SP.J.1146.2007.00158

Volume 30 Issue 9

Jan. 2011

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Bi Yun-Long, Lai Feng-Chang, Liu Peng. A System Identification Algorithm by Minimizing the Zero-Minimum Target Function[J]. Journal of Electronics & Information Technology, 2008, 30(9): 2138-2142. doi: 10.3724/SP.J.1146.2007.00158

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Bi Yun-Long, Lai Feng-Chang, Liu Peng. A System Identification Algorithm by Minimizing the Zero-Minimum Target Function[J]. Journal of Electronics & Information Technology, 2008, 30(9): 2138-2142. doi: 10.3724/SP.J.1146.2007.00158

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

doi: 10.3724/SP.J.1146.2007.00158

Received Date: 2007-01-25
Rev Recd Date: 2007-07-23
Publish Date: 2008-09-19

Abstract

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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