In this paper, we propose a K-means clustering-based integral level-value estimation algorithm to solve a kind of box-constrained global optimization problem. For this purpose, we introduce the generalized variance fu...In this paper, we propose a K-means clustering-based integral level-value estimation algorithm to solve a kind of box-constrained global optimization problem. For this purpose, we introduce the generalized variance function associated with the level-value of the objective function to be minimized. The variance function has a good property when Newton’s method is used to solve a variance equation resulting by setting the variance function to zero. We prove that the largest root of the variance equation is equal to the global minimum value of the corresponding optimization problem. Based on the K-means clustering algorithm, the multiple importance sampling technique is proposed in the implementable algorithm. The main idea of the cross-entropy method is used to update the parameters of sampling density function. The asymptotic convergence of the algorithm is proved, and the validity of the algorithm is verified by numerical experiments.展开更多
针对传统的遍历法无法满足多全球卫星导航系统(global navigation satellite system,GNSS)组合导航选星的实时性需求,提出了一种基于灰狼优化(grey wolf optimization,GWO)算法的快速选星方法。该算法利用自适应收敛因子和信息反馈机制...针对传统的遍历法无法满足多全球卫星导航系统(global navigation satellite system,GNSS)组合导航选星的实时性需求,提出了一种基于灰狼优化(grey wolf optimization,GWO)算法的快速选星方法。该算法利用自适应收敛因子和信息反馈机制,在局部寻优与全局搜索之间实现平衡,表现出良好的求解性能,即可以保证在获得理想几何构型的同时大幅减少接收机运算量。经过仿真实验,分析了参数选取对GWO快速选星算法结果的影响。利用实测数据对所提算法进行验证,结果表明,所提算法在四系统组合下,从49颗可见星中选择7颗进行定位时,与遍历法相比,几何精度因子(geometric dilution of precision,GDOP)误差仅为1.8%,而计算效率提高了71.7%。该算法适用于多GNSS组合导航定位不同选星数目的情况,还可以拓展至区域导航卫星系统。展开更多
文摘In this paper, we propose a K-means clustering-based integral level-value estimation algorithm to solve a kind of box-constrained global optimization problem. For this purpose, we introduce the generalized variance function associated with the level-value of the objective function to be minimized. The variance function has a good property when Newton’s method is used to solve a variance equation resulting by setting the variance function to zero. We prove that the largest root of the variance equation is equal to the global minimum value of the corresponding optimization problem. Based on the K-means clustering algorithm, the multiple importance sampling technique is proposed in the implementable algorithm. The main idea of the cross-entropy method is used to update the parameters of sampling density function. The asymptotic convergence of the algorithm is proved, and the validity of the algorithm is verified by numerical experiments.
文摘针对传统的遍历法无法满足多全球卫星导航系统(global navigation satellite system,GNSS)组合导航选星的实时性需求,提出了一种基于灰狼优化(grey wolf optimization,GWO)算法的快速选星方法。该算法利用自适应收敛因子和信息反馈机制,在局部寻优与全局搜索之间实现平衡,表现出良好的求解性能,即可以保证在获得理想几何构型的同时大幅减少接收机运算量。经过仿真实验,分析了参数选取对GWO快速选星算法结果的影响。利用实测数据对所提算法进行验证,结果表明,所提算法在四系统组合下,从49颗可见星中选择7颗进行定位时,与遍历法相比,几何精度因子(geometric dilution of precision,GDOP)误差仅为1.8%,而计算效率提高了71.7%。该算法适用于多GNSS组合导航定位不同选星数目的情况,还可以拓展至区域导航卫星系统。
文摘针对油田遥感图像在灰度有明显差异的情况下,联合位置、尺度和方向的尺度不变特征变换(PSO-SIFT)算法很难为其找到足够多的正确对应关系,且花费时间较长的问题,提出一种基于改进PSO-SIFT算法的图像匹配算法.首先采用“回”字型分块思想构建特征描述符,降低特征描述子的维度;然后使用基于全局运动建模的双边函数(BF)算法与快速样本共识(FSC)算法相结合的匹配策略,对所得的匹配对进行误匹配剔除,以增加正确匹配的数量;最后将该算法与4种同类算法及原PSO-SIFT算法进行对比.实验结果表明,该算法比同类算法精度更高,与原算法相比不仅保证了图像匹配的精度,正确匹配对数量也增加了约3倍,且匹配时间约缩短20 s.
