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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
