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Li J, Hu G, Zhou Y, et al. Study on Temperature and Synthetic Compensation of Piezo-Resistive Differential Pressure Sensors by Coupled Simulated Annealing and Simplex Optimized Kernel Extreme Learning Machine. Sensors (Basel). 2017;17(4)doi: 10.3390/s17040894.
Li, J., Hu, G., Zhou, Y., Zou, C., Peng, W., & Alam Sm, J. (2017). Study on Temperature and Synthetic Compensation of Piezo-Resistive Differential Pressure Sensors by Coupled Simulated Annealing and Simplex Optimized Kernel Extreme Learning Machine. Sensors (Basel, Switzerland), 17(4), . https://doi.org/10.3390/s17040894
Li, Ji, et al. "Study on Temperature and Synthetic Compensation of Piezo-Resistive Differential Pressure Sensors by Coupled Simulated Annealing and Simplex Optimized Kernel Extreme Learning Machine." Sensors (Basel, Switzerland) vol. 17,4 (2017). doi: https://doi.org/10.3390/s17040894
Li J, Hu G, Zhou Y, Zou C, Peng W, Alam Sm J. Study on Temperature and Synthetic Compensation of Piezo-Resistive Differential Pressure Sensors by Coupled Simulated Annealing and Simplex Optimized Kernel Extreme Learning Machine. Sensors (Basel). 2017 Apr 19;17(4). doi: 10.3390/s17040894. PMID: 28422080; PMCID: PMC5426544.
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Sensors (Basel). 2017 Apr 19;17(4). doi: 10.3390/s17040894.

Study on Temperature and Synthetic Compensation of Piezo-Resistive Differential Pressure Sensors by Coupled Simulated Annealing and Simplex Optimized Kernel Extreme Learning Machine.

Sensors (Basel, Switzerland)

Ji Li, Guoqing Hu, Yonghong Zhou, Chong Zou, Wei Peng, Jahangir Alam Sm

Affiliations

  1. Department of Mechanical and Electrical Engineering, School of Aerospace Engineering, Xiamen University, Xiamen 361005, China. [email protected].
  2. Department of Mechanical and Electrical Engineering, School of Aerospace Engineering, Xiamen University, Xiamen 361005, China. [email protected].
  3. Department of Mechatronics Engineering, School of Mechanical & Automotive Engineering, South China University of Technology, Guangzhou 510640, China. [email protected].
  4. Fujian Wide Plus Precision Instruments Co. Ltd., Fuzhou 350015, China. [email protected].
  5. Fujian Wide Plus Precision Instruments Co. Ltd., Fuzhou 350015, China. [email protected].
  6. Department of Mechatronics Engineering, School of Mechanical & Automotive Engineering, South China University of Technology, Guangzhou 510640, China. [email protected].
  7. Department of Mechatronics Engineering, School of Mechanical & Automotive Engineering, South China University of Technology, Guangzhou 510640, China. [email protected].

PMID: 28422080 PMCID: PMC5426544 DOI: 10.3390/s17040894

Abstract

As a high performance-cost ratio solution for differential pressure measurement, piezo-resistive differential pressure sensors are widely used in engineering processes. However, their performance is severely affected by the environmental temperature and the static pressure applied to them. In order to modify the non-linear measuring characteristics of the piezo-resistive differential pressure sensor, compensation actions should synthetically consider these two aspects. Advantages such as nonlinear approximation capability, highly desirable generalization ability and computational efficiency make the kernel extreme learning machine (KELM) a practical approach for this critical task. Since the KELM model is intrinsically sensitive to the regularization parameter and the kernel parameter, a searching scheme combining the coupled simulated annealing (CSA) algorithm and the Nelder-Mead simplex algorithm is adopted to find an optimal KLEM parameter set. A calibration experiment at different working pressure levels was conducted within the temperature range to assess the proposed method. In comparison with other compensation models such as the back-propagation neural network (BP), radius basis neural network (RBF), particle swarm optimization optimized support vector machine (PSO-SVM), particle swarm optimization optimized least squares support vector machine (PSO-LSSVM) and extreme learning machine (ELM), the compensation results show that the presented compensation algorithm exhibits a more satisfactory performance with respect to temperature compensation and synthetic compensation problems.

Keywords: CSA; KELM; piezo-resistive pressure sensor; simplex; static pressure effect; temperature compensation

References

  1. IEEE Trans Syst Man Cybern B Cybern. 2010 Apr;40(2):320-35 - PubMed
  2. Sensors (Basel). 2015 May 13;15(5):11189-207 - PubMed
  3. Proc IEEE Inst Electr Electron Eng. 2009;97(3):513-552 - PubMed
  4. Sensors (Basel). 2014 Oct 09;14(10):18711-27 - PubMed
  5. Appl Phys Lett. 2011 May 30;98(22):223103 - PubMed
  6. Sensors (Basel). 2016 Jul 22;16(7):null - PubMed
  7. Science. 1983 May 13;220(4598):671-80 - PubMed
  8. Comput Biol Med. 2016 Feb 1;69:213-25 - PubMed
  9. Opt Express. 2015 May 18;23(10):13320-32 - PubMed
  10. Sensors (Basel). 2016 Oct 14;16(10 ):null - PubMed

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