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

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          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.

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          Most cited references 36

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          Optimization by simulated annealing.

          There is a deep and useful connection between statistical mechanics (the behavior of systems with many degrees of freedom in thermal equilibrium at a finite temperature) and multivariate or combinatorial optimization (finding the minimum of a given function depending on many parameters). A detailed analogy with annealing in solids provides a framework for optimization of the properties of very large and complex systems. This connection to statistical mechanics exposes new information and provides an unfamiliar perspective on traditional optimization problems and methods.
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            LIBSVM

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              Least squares support vector machine classifiers

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                Author and article information

                Contributors
                Role: Academic Editor
                Journal
                Sensors (Basel)
                Sensors (Basel)
                sensors
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                19 April 2017
                April 2017
                : 17
                : 4
                Affiliations
                [1 ]Department of Mechanical and Electrical Engineering, School of Aerospace Engineering, Xiamen University, Xiamen 361005, China; 19920130154215@ 123456stu.xmu.edu.cn
                [2 ]Department of Mechatronics Engineering, School of Mechanical & Automotive Engineering, South China University of Technology, Guangzhou 510640, China; mepeng.wei@ 123456mail.scut.edu.cn (W.P.); mejahangir@ 123456scut.edu.cn (J.A.S.)
                [3 ]Fujian Wide Plus Precision Instruments Co. Ltd., Fuzhou 350015, China; zyh@ 123456wideplus.com (Y.Z.); c@ 123456wideplus.com (C.Z.)
                Author notes
                [* ]Correspondence: gqhu@ 123456scut.edu.cn ; Tel.: +86-138-2618-5229
                Article
                sensors-17-00894
                10.3390/s17040894
                5426544
                28422080
                © 2017 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

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