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《Journal of Electronic Measurement and Instrument》 2011-01
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Nonlinear correction of sensor using genetic algorithm and support vector machine

Liu Tao Wang Hua(College of Mechanical and Electrical Engineering,Lanzhou University of Technology,Lanzhou 730050,China)  
In order to overcome the disadvantages of neural networks more commonly used in nonlinear correction of sensors and the problems that the parameters of Support Vector Machine are difficult to determine,a method based on Genetic Algorithm and Support Vector Machine is presented.The nonlinear correction principle of this method is explained.The realization process of Genetic Algorithm optimizing the parameters of Support Vector Machine is introduced.A pressure sensor is adjusted with BP neural network and the method of Genetic Algorithm and Support Vector Machine,respectively.The experiment results show that BP neural network reduces the maximum relative fluctuation from the initial 22.2% to 1.12%.Moreover,the method of Genetic Algorithm and Support Vector Machine reduces the maximum relative fluctuation to 0.04%,therefore it evidently improves the performance of the sensor and achieves better result.
【Fund】: 甘肃省高等学校基本科研业务费资助项目
【CateGory Index】: TP212
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