基于多元Taylor级数和AFS的混合定位算法  

Hybrid Localization Algorithm Based on Multivariable Taylor Series and Artificial Fish Swarm

作　　者：刘倩[1] 夏斌[1] 谢楠[1] 袁文浩[1] LIU Qian;XIA Bin;XIE Nan;YUAN Wen-hao(School of Computer Science and Technology,Shandong University of Technology,Zibo Shandong 255049,China)

机构地区：[1]山东理工大学计算机科学与技术学院,山东淄博255049

出　　处：《计算机仿真》2020年第4期290-293,370,共5页Computer Simulation

基　　金：国家自然科学基金(61701286);山东省自然科学基金(ZR2017MF047)。

摘　　要：针对传统Taylor级数定位算法存在精度严重依赖初始值,导致定位精确度不高的缺陷,结合人工鱼群算法和多元Taylor级数展开算法的优点,提出了一种基于人工鱼群算法初值选取与多元Taylor级数展开算法精确求解的混合定位方法。算法充分发挥了人工鱼群算法初值估计性能良好和多元Taylor级数展开算法求解精度高的优点。仿真结果表明:上述算法减少了鱼群数目和迭代次数的选取对定位精度的影响,混合定位算法的精度更高。The accuracy of the traditional localization algorithm depends on the initial value seriously, so the positioning accuracy is not high. By combining the advantages of multivariable Taylor series algorithm and the artificial fish swarm algorithm, the paper proposes a localization method based on the artificial fish swarm algorithm of choosing initial values and multivariable Taylor series expansion method of precision solution. The algorithm combines the advantages of good performance in estimation of the artificial fish swarm algorithm and high precision solution of the multivariable Taylor series algorithm. Experimental results indicate that the hybrid algorithm can improve positioning accuracy and reduce the selection of the fish number parameter and iteration number parameter influence on positioning accuracy.

关 键 词：定位模型 人工鱼群 定位精度 混合算法 

分 类 号：TP391.9[自动化与计算机技术—计算机应用技术]