CPSO-LSSVM在自回归钟差预报中的应用

doi:10.13229/j.cnki.jdxbgxb201403036

吉林大学学报(工学版) ›› 2014, Vol. 44 ›› Issue (3): 807-811.doi: 10.13229/j.cnki.jdxbgxb201403036

CPSO-LSSVM在自回归钟差预报中的应用

刘强¹,孙际哲^1,2,陈西宏¹,刘继业¹,张群³

1.空军工程大学防空反导学院,西安 710051;
2.西北工业大学航海学院,西安710072;
3. 空军工程大学信息与导航学院,西安710077

收稿日期:2013-02-20 出版日期:2014-03-01 发布日期:2014-03-01
作者简介:刘强(1985),男,博士研究生.研究方向:高精度时间同步技术.E-mail:dreamlq@163.com
基金资助:
国家自然科学基金项目(60971100).

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

LIU Qiang¹,SUN Ji-zhe^1,2,CHEN Xi-hong¹,LIU Ji-ye¹,ZHANG Qun³

1.Air and Missile Defense College, Air Force Engineering University, Xi′an 710051, China;
2.College of Marine Engineering, Northwestern Polytechnical University, Xi′an 710072, China;
3.Information & Navigation College, Air Force Engineering University, Xi′an 710077,China

Received:2013-02-20 Online:2014-03-01 Published:2014-03-01

摘要/Abstract

摘要： 建立了基于自回归算法的钟差预报模型,利用具有较强非线性运算能力和容错能力的最小二乘-支持向量机算法来求解自回归参数,同时利用具有快速寻优特点的粒子群算法来优化最小二乘-支持向量机参数。为了克服粒子群算法容易陷入局部极值而形成早熟的缺点,提出了分别在粒子初始化位置和陷入局部极值的位置上进行混沌处理,提高了粒子搜索的遍历性和寻优能力,从整体上优化了算法。最后通过星载钟差数据对该算法进行了验证,结果表明:本文算法能够实现亚纳秒量级的预报精度并提升卫星授时导航性能。

关键词: 计算机应用, 混沌粒子群, 最小二乘-支持向量机, 钟差预报

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

中图分类号:

TP301

刘强,孙际哲,陈西宏,刘继业,张群. CPSO-LSSVM在自回归钟差预报中的应用[J]. 吉林大学学报(工学版), 2014, 44(3): 807-811.

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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CPSO-LSSVM在自回归钟差预报中的应用

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

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