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Vol. 164, Issue 2, February 2014, pp. 265-271




Parameter Identification Method Based on Wavelet Analysis of Time Window

1, 2 HE Zhiyong, 1 HE Qinghua, 2 HE Shanghong

1 College of Mechanical and Electrical Engineering, Central South University, Changsha 410083, China
2 College of Automobile and Mechanism Engineering, Changsha University of Science and Technology, Changsha 410076, China

E-mail: hezhiyong73@163.com


Received: 12 November 2013 /Accepted: 28 January 2014 /Published: 28 February 2014

Digital Sensors and Sensor Sysstems


Abstract: The influence of the noise impact on system parameters estimation was analyzed in this paper. Direct identification algorithm of modulation least square of continuous Hammerstein which without measurement noise interference was introduced and the estimation parameter of Hammerstein model was obtained by parameter separation method. According to least-square characteristic of noise, the unbiased estimate parameters of continuous Hammerstein model with measurement noise interference was gained through modulation of generalized least squares algorithm. The unbiased and high accuracy estimation of system parameters was obtained by time window noise modulation auxiliary variable method and time window noise modulation of generalized least squares algorithm. By contrast checking an instance, when noise signal ratio NSR is 20 %, the error value dropped from 16.8002 % to 1.4114 %, when the noise signal ratio NSR is 40 %, the error value dropped from 19.5041 % to 2.2505 %. Research shows that this algorithm can adapt to large industrial system model identification by the results verified algorithm is effective and practical.


Keywords: Parameter identification, Hammerstein model, Noise, Noise reduction of time window.


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