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Nonlinear Correction of Sensor Based on Genetic Algorithm and Support Vector Machine

Author: WangHua
Tutor: LiuTao
School: Lanzhou University of Technology
Course: Measuring Technology and Instruments
Keywords: Sensor Nonlinear correction Support Vector Machine Genetic Algorithms Matlab
CLC: TP212
Type: Master's thesis
Year: 2011
Downloads: 82
Quote: 0
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Abstract


The sensors are important components in the test system , its performance and the reliability of the output signal plays a vital role in the quality of the test system as a whole . In practical applications , the sensor is susceptible to a number of environmental factors , such as temperature , magnetic field , noise , power supply fluctuations , etc. , thus reducing the measurement accuracy of the entire system , resulting in the problem of poor stability of the system . Therefore, in order to improve the performance of the sensor , thereby increasing the accuracy of the overall test system and expand the measuring range , to nonlinear correction of the sensor has a very important significance . Difficult to determine the topic for several methods available in the sensor nonlinear correction lack of support vector machine parameters , combined with the global search ability of genetic algorithms , this paper proposes a method of combining a genetic algorithm and support vector machine the establishment of a the sensor nonlinearity correction support vector machine model , and describes the genetic algorithm implementation process parameter optimization of support vector machine . Nonlinear correction in the implementation process , the application the Matlab language preparation training program CYJ-101 pressure sensor and verify the feasibility of genetic support vector machine method . Meanwhile, the comparative analysis with support vector machine method and BP neural network correction results verify the superiority of the method . Experimental results show that : the correction and support vector machine , genetic support vector machine better selection of support vector machine and kernel function parameters to achieve the optimal mix of parameters ; BP neural network method makes sensor the relative volatility of 22.2% of the initial reduced to 1.12% , while the genetic support vector machine method so that it is reduced to 0.04% , significantly improve the performance of the sensor , and achieved good results . Part of the hardware and software of the system design pressure sensor , and genetic support vector machine method for the calibration and compensation of the sensor nonlinear software part of the method to be realized in practical applications .

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CLC: > Industrial Technology > Automation technology,computer technology > Automation technology and equipment > Automation components,parts > Transmitter ( converter),the sensor
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