Title

用微粒群算法與神經網絡實現傳感器誤差補償

Translated Titles

Sensor Nonlinear Error Compensation Evolved by Neural Network and Particle Swarm Algorithm

Authors

孫健(Jian Sun)

Key Words

電子技術 ; 測量 ; 誤差 ; 微粒群 ; 神經網絡 ; electronic technology ; measurement ; error ; particle swarm optimization ; neural network

PublicationName

電子元件與材料

Volume or Term/Year and Month of Publication

24卷12期(2005 / 12 / 05)

Page #

17 - 19

Content Language

簡體中文

Chinese Abstract

In order to reduce the measurement system error from the sensor nonlinear response, a sensor error compensation unit was designed by the particle swarm optimization algorithm and BP (Back-propagation) neural network. Using this method, nonlinear characteristics of sensor can be converted into a non-distortion linear model which is consistent with the actual physical process, and the nonlinear error of system can be reduced very much. In the inductance micro measured the meter moves in the measurement system experiment, after uses the PSO (Particle swarm optimization) algorithm training, network BP(Back-Propagation) algorithm convergence rate very quick, also the precision is high, after undergoes 120 times of studies, erroneous square E<0.001.

English Abstract

In order to reduce the measurement system error from the sensor nonlinear response, a sensor error compensation unit was designed by the particle swarm optimization algorithm and BP (Back-propagation) neural network. Using this method, nonlinear characteristics of sensor can be converted into a non-distortion linear model which is consistent with the actual physical process, and the nonlinear error of system can be reduced very much. In the inductance micro measured the meter moves in the measurement system experiment, after uses the PSO (Particle swarm optimization) algorithm training, network BP(Back-Propagation) algorithm convergence rate very quick, also the precision is high, after undergoes 120 times of studies, erroneous square E<0.001.

Topic Category 工程學 > 工程學總論
工程學 > 電機工程