J. Semicond. > Volume 36 > Issue 9 > Article Number: 095004

A novel pressure sensor calibration system based on a neural network

Xiaojun Peng 1, 2, , , Kuntao Yang 1, and Xiuhua Yuan 1,

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Abstract: According to the specific input-output characteristics of a pressure sensor, a novel calibration algorithm is presented and a calibration system is developed to correct the nonlinear error caused by temperature. In contrast to the routine BP and RBF, curve fitting based on RBF is first used to get the slope and intercept, and then the voltage-pressure curve is described. Test results show that the algorithm features fast convergence speed, strong robustness and minimum SSE (sum of squares for error). It is proven by practical applications that this calibration system works well and the measurement precision is better than the design demands. Furthermore, this calibration system has a good real-time capability.

Key words: nonlinear error correctioncomprehensive compensationcurve fittingneural networkhigh precision

Abstract: According to the specific input-output characteristics of a pressure sensor, a novel calibration algorithm is presented and a calibration system is developed to correct the nonlinear error caused by temperature. In contrast to the routine BP and RBF, curve fitting based on RBF is first used to get the slope and intercept, and then the voltage-pressure curve is described. Test results show that the algorithm features fast convergence speed, strong robustness and minimum SSE (sum of squares for error). It is proven by practical applications that this calibration system works well and the measurement precision is better than the design demands. Furthermore, this calibration system has a good real-time capability.

Key words: nonlinear error correctioncomprehensive compensationcurve fittingneural networkhigh precision



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Hou Liqun, Tong Weiguo, He Tongxiang. The static errors comprehensive correcting method of sensors based on radial basis function neural network[J]. Journal of Transcluction Technology, 2004, 12(4): 643.

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Peng Xiaojun, Feng Xuechao. Study on a measurement system of ocean depth[J]. Piezoelectrics & Acoustooptics, 2013, 35(2): 293.

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[1]

Shao Jianjian, Li Weitao, Sun Cao. A digital background calibration algorithm of a pipeline ADC based on output code calculation[J]. Journal of Semiconductors, 2012, 33(11): 115010.

[2]

Ploemen I H J, Molengraft M J G. Hybrid modeling for mechanical systems: methodologies and applications[J]. ASME Journal of Dynamic Systems, Measurement and Control, 1999, 121(2): 270.

[3]

Jing Xin, Zhuang Yiqi, Tang Hualian. A power-efficient 12-bit analog-to-digital converter with a novel constant-resistance CMOS input sampling switch[J]. Journal of Semiconductors, 2014, 35(2): 025002.

[4]

Xie Shilin, Chen Shenglai, Zhang Xinong. Neural network hybrid modeling method for transducer calibration[J]. J Mechanical Eng, 2010, 46(22): 6.

[5]

Green D L, Everhart J L, Rhode M N. Development and characterization of a low-pressure calibration system for hypersonic wind tunnels[J]. AIAA Paper, 2004.

[6]

Pasquale A, Pasquale D, Domenico G. ANN-based error reduction for experimentally modeled sensors[J]. IEEE Trans Instru & Mea, 2002, 51(1): 23.

[7]

Tian Sheping, Zhao Yang, Wei Hongyu. Nonlinear compensation of sensors based on BP neural network[J]. Journal of Test and Measurement Technology, 2007, 21(1): 86.

[8]

Hou Liqun, Tong Weiguo, He Tongxiang. The static errors comprehensive correcting method of sensors based on radial basis function neural network[J]. Journal of Transcluction Technology, 2004, 12(4): 643.

[9]

Peng Xiaojun, Feng Xuechao. Study on a measurement system of ocean depth[J]. Piezoelectrics & Acoustooptics, 2013, 35(2): 293.

[10]

Yang Yang, Zhao Xianli, Zhong Shun'an. Digital post-calibration of a 5-bit 1[J]. , 2012, 33(2): 025011-025011.

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X J Peng, K T Yang, X H Yuan. A novel pressure sensor calibration system based on a neural network[J]. J. Semicond., 2015, 36(9): 095004. doi: 10.1088/1674-4926/36/9/095004.

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Manuscript received: 02 February 2015 Manuscript revised: Online: Published: 01 September 2015

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