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Sensors 2017, 17(4), 894; 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

1
Department of Mechanical and Electrical Engineering, School of Aerospace Engineering, Xiamen University, Xiamen 361005, China
2
Department of Mechatronics Engineering, School of Mechanical & Automotive Engineering, South China University of Technology, Guangzhou 510640, China
3
Fujian Wide Plus Precision Instruments Co. Ltd., Fuzhou 350015, China
*
Author to whom correspondence should be addressed.
Academic Editor: Guillermo Villanueva
Received: 7 March 2017 / Revised: 7 April 2017 / Accepted: 12 April 2017 / Published: 19 April 2017
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [6113 KB, uploaded 19 April 2017]   |  

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. View Full-Text
Keywords: piezo-resistive pressure sensor; temperature compensation; static pressure effect; KELM; CSA; simplex piezo-resistive pressure sensor; temperature compensation; static pressure effect; KELM; CSA; simplex
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MDPI and ACS Style

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 2017, 17, 894.

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