@inproceedings{7bee040b7a4e4940a7cd6ff4a5062101,
title = "Compensation of FOG temperature drift based on LS-SVM modeling",
abstract = "A nonlinear prediction model based on least-square support vector machine (LS-SVM) is proposed for the fiber optic gyro (FOG) temperature drift. LS-SVM is an intelligent learning machine, and is good at solving nonlinear, small samples learning problem. In the proposed LS-SVM model, the environment temperature, temperature change rate and the temperature gradient was set to be three inputs, the FOG bias drift is the expectation output. A simulated annealing algorithm (SA) is introduced to determine two important parameters in the LS-SVM model. SA is a universal random search algorithm; it provides the LS-SVM model a best prediction accuracy. Two groups of simulation with different temperature rate were carried out to evaluate the proposed algorithm. The results proved that the proposed LS-SVM model is more efficient and accuracy than the traditional BP neural network in reducing the FOG temperature drift.",
keywords = "Fiber Optic Gyro (FOG), Least-Square Support Vector Machine (LS-SVM), Simulated Annealing (SA) Algorithm, Temperature Drift",
author = "Nan Li and Jiabin Chen and Yan Yuan and Yongqiang Han and Xiaochun Tian",
note = "Publisher Copyright: {\textcopyright} 2016 TCCT.; 35th Chinese Control Conference, CCC 2016 ; Conference date: 27-07-2016 Through 29-07-2016",
year = "2016",
month = aug,
day = "26",
doi = "10.1109/ChiCC.2016.7554214",
language = "English",
series = "Chinese Control Conference, CCC",
publisher = "IEEE Computer Society",
pages = "5515--5518",
editor = "Jie Chen and Qianchuan Zhao and Jie Chen",
booktitle = "Proceedings of the 35th Chinese Control Conference, CCC 2016",
address = "United States",
}