CPSO-LSSVM在自回归钟差预报中的应用

doi:10.13229/j.cnki.jdxbgxb201403036

Abstract

Abstract: A model of Auto Regressive (AR) algorithm is established to predict clock error. The model takes the advantage of strong abilities of non-linear operation and fault toleration of Least Square-Support Vector Machine (LS-SVM) to determine the parameters of AR. It also optimizes the LS-SVM parameters by Particle Swarm Optimization (PSO), which could search the best target fast. In order to overcome the shortcoming that PSO always plunges into local extremum and becomes premature, Chaos theory is introduced into positional initialization and local extremum condition, which improves particles' ergodicity and the ability of optimization, accordingly the algorithm as a whole is optimized. The algorithm is tested by satellite clock error data. The results show that the algorithm can achieve sub-nanosecond prediction precision, thus improving the satellites' time service and navigation.

Key words: computer application, chaos particle swarm optimization(CPSO), least square-support vector machine (LS-SVM), clock error prediction

CLC Number:

TP301

LIU Qiang,SUN Ji-zhe,CHEN Xi-hong,LIU Ji-ye,ZHANG Qun. Application analysis of CPSO-LSSVM algorithm in AR clock error prediction[J].吉林大学学报(工学版), 2014, 44(3): 807-811.

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Application analysis of CPSO-LSSVM algorithm in AR clock error prediction

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