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周飞,张炎,唐诗华,等. 利用蜂群算法优化的区域高程拟合精度分析[J]. 科学技术与工程, 2020, 20(16): 6330-6335.
ZHOU Fei,张炎,TANG Shi-hua,et al.Accuracy Analysis of Regional Height Fitting Optimized by Bee Colony Algorithm[J].Science Technology and Engineering,2020,20(16):6330-6335.
利用蜂群算法优化的区域高程拟合精度分析
Accuracy Analysis of Regional Height Fitting Optimized by Bee Colony Algorithm
投稿时间:2019-09-16  修订日期:2020-06-15
DOI:
中文关键词:  人工蜂群算法  高程拟合 最小二乘支持向量机  正则化参数 核参数
英文关键词:artificial bee colony  algorithm height  fitting least  squares support  vector machine  regularization parameter  kernel parameter
基金项目:国家自然科学基金(41864002)、广西空间信息与测绘重点实验室基金(16-380-25-25,16-380-25-13)、广西高校中青年教师基础能力提升项目(KY2016YB823)
              
作者单位
周飞 广西壮族自治区基础地理信息中心
张炎 桂林理工大学测绘地理信息学院
唐诗华 桂林理工大学测绘地理信息学院
邢鹏威 桂林理工大学测绘地理信息学院
张 跃 桂林理工大学测绘地理信息学院
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中文摘要:
      针对最小二乘支持向量机拟合法的拟合参数难以选取的问题,提出将人工蜂群算法引入最小二乘支持向量机建立高精度区域拟合模型的方法。人工蜂群算法可对最小二乘支持向量机中的参数进行全局性追踪搜索,模仿蜜蜂的采蜜过程,将参数的初选值作为蜜源,最小二乘支持向量机预测的平均平方误差作为目标函数值,在一定范围内经过迭代更新确定最佳参数,最终建立精度较高的GPS高程拟合模型。实验结果表明,相比于常规最小二乘支持向量机拟合法,ABC-LSSVM组合方法构建的拟合模型精度提高了28%,在此同时,该组合方法比BP神经网络拟合法的收敛效果更高、稳定性更佳,证明了ABC-LSSVM组合方法在GPS高程拟合模型构建中的有效可行性,为GPS高程拟合模型的建立提供一定的参考价值。
英文摘要:
      In order to solve the problem that the fitting parameters of the least squares support vector machine fitting method are difficult to select, a method of introducing the artificial bee colony algorithm into the least squares support vector machine to establish a high-precision region fitting model is proposed. The artificial bee colony algorithm can perform global tracking search on the parameters in the least squares support vector machine, imitate the honey collecting process of the bees, and use the primary value of the parameters as the honey source, and the average square error predicted by the least squares support vector machine as the target. The function value is determined by iterative update within a certain range to determine the optimal parameters, and finally a GPS height fitting model with higher precision is established. The experimental results show that the accuracy of the fitting model constructed by the ABC-LSSVM combination method is improved by 28% compared with the conventional least squares support vector machine fitting method. At the same time, the combined method has higher convergence and better stability than the BP neural network fitting method. and the effective feasibility of the ABC-LSSVM combination method in the construction of GPS height fitting model is proved, which provides a certain reference value for the establishment of GPS height fitting model.
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