1) gravity-map matching
重力图匹配
1.
Based on the principle and performance of integrated INS/Gravity-map matching navigation system, a gravity-map matching method based on RANSAC was presented to restrain the error increase of INS, which is very robust even if there are outliers in the gravity data.
根据惯性/重力匹配组合导航系统的工作原理和特点,提出了基于 RANSA 思想的重力图匹配方法,采用此方法实现了重力图的鲁棒匹配。
2) gravity matching
重力匹配
1.
The empirical selection criterion of gravity matching area was given by using local gravity standard deviation and local gravity correlation coefficient as quantity indexes for selecting gravity-matching area.
通过在重力场区域中移动局部计算窗口的方法,计算了实测重力场各个局部的多种统计特征并使用填色等值线图进行了对比和分析,以局部重力场的标准差和经纬度方向相关系数作为匹配区域选择的数量指标,给出了重力匹配区经验选择准则。
2.
In order to improve the locating precision and matching rate of the gravity aided inertial navigation system(INS) in regions with significant gravity anomaly characteristic,the pattern recognition neural network was used to investigate the problem of gravity matching.
为了提高重力辅助惯性导航系统在重力异常明显区域内的定位精度和匹配率,用模式识别神经网络的方法进行了重力匹配。
3) matching localization of gravity
重力匹配定位
4) heavy-edge matching
重边匹配
1.
Compared with the heavy-edge matching and sorted heavy-edge matching,light-vertex matching is certificated to have smaller edge cutting and better balance during the experiments of network graph partitioning.
通过采用轻点匹配算法对网络模拟图进行划分试验,并与重边匹配算法和有选择的重边匹配算法进行对比分析,证明该法具有较小的边切割和很好的平衡性。
5) repeated matching
重复匹配
1.
Based on the idea of repeated matching and clustering arithmetic, the heuristic rules and heuristic algorithm for packing and scheduling problem are put forward.
利用重复匹配算法、聚合算法等启发式方法 ,提出了布局调度操作的启发式规则及相应的启发式算法 。
补充资料：图的减缩图(或称图子式)
图的减缩图(或称图子式)
minor of a graph
图的减缩图(或称图子式)【.皿以ofa脚户;MHHoPrpa中a」【补注】设G是一个图(graph)(可以有环及多重边).G的一个减缩图(nullor)是从G中接连进行下述运算而得的任何一个图: i)删去一条边; 五)收缩一条边; 说)去掉一个孤立顶点. NRobe由on与P.D.Se脚aour的图减缩定理(脚Ph nl的。r theon习11)如下所述:已知有限图的无穷序列G，，GZ，…，则存在指标i
说明：补充资料仅用于学习参考，请勿用于其它任何用途。
参考词条