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Proceedings Paper

Transducer modeling and compensation in high-pressure dynamic calibration
Author(s): Chikun Gong; Yongxin Li
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Paper 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.

Paper Details

Date Published: 20 February 2006
PDF: 5 pages
Proc. SPIE 6041, ICMIT 2005: Information Systems and Signal Processing, 60412A (20 February 2006); doi: 10.1117/12.664369
Show Author Affiliations
Chikun Gong, Nanjing Univ. of Science and Technology (China)
Yongxin Li, Nanjing Univ. of Science and Technology (China)


Published in SPIE Proceedings Vol. 6041:
ICMIT 2005: Information Systems and Signal Processing
Yunlong Wei; Kil To Chong; Takayuki Takahashi, Editor(s)

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