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A System Identification Algorithm by Minimizing the Zero-Minimum Target Function |
Bi Yun-long Lai Feng-chang Liu Peng |
Dept. of Microelectronic Science and Technology, Harbin Institute of Technology, Harbin 150001, China |
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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. It’s also shown that the algorithm has better stability.
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Received: 25 January 2007
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Corresponding Authors:
Bi Yun-long
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