Transducer modeling and compensation in high-pressure dynamic calibration

Chikun Gong; Yongxin Li

doi:10.1117/12.664369

20 February 2006 Transducer modeling and compensation in high-pressure dynamic calibration

Chikun Gong, Yongxin Li

Proceedings Volume 6041, ICMIT 2005: Information Systems and Signal Processing; 60412A (2006) https://doi.org/10.1117/12.664369
Event: ICMIT 2005: Merchatronics, MEMS, and Smart Materials, 2005, Chongqing, China

Abstract

When the RBF neural network is used to establish and compensate the transducer model, the numbers of cluster need to be given in advance by using Kohonen algorithm, the RLS algorithm is complicated and the computational burden is much heavier by using it to regulate the output weights. In order to overcome the weakness, a new approach is proposed. The cluster center is decided by the subtractive clustering, and LMS algorithm is used to regulate the output weights. The noise elimination with correlative threshold plus wavelet packet transformation is used to improve the SNR. The study result shows that the network structure is simple and astringency is fast, the modeling and compensation by using the new algorithm is effective to correct the nonlinear dynamic character of transducer, and noise elimination with correlative threshold plus wavelet packet transformation is superior to conventional noise elimination methods.

Citation Download Citation

Chikun Gong and Yongxin Li "Transducer modeling and compensation in high-pressure dynamic calibration", Proc. SPIE 6041, ICMIT 2005: Information Systems and Signal Processing, 60412A (20 February 2006); https://doi.org/10.1117/12.664369

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