1) local minimum
局部极小值
1.
The problem of trapping into the local minimum is solved, which is inherent with the learning algorithms-based on the BP principle by weight strategy.
但是BP网络极容易陷入局部极小值,应用加权策略解决了此问题。
2.
On the other hand, the method in this paper can avoid the local minimum efficiently by adding the number of the particles.
并将仿真结果与BP网络进行比较,仿真数据表明,PSO算法在叠代次数、函数逼近误差、网络性能方面均比BP网络有着显著的提高,且在粒子数目较大的情况下能有效避免BP网络无法避免的局部极小值问题。
3.
Analysis of the traditional artificial potential field failed in path planning due to the local minimum and the possible local minimum area of the external obstacles on the geometry.
本文分析了传统的人工势场法由于局部最小问题而导致规划失败的原因,对可能产生局部极小值区域的障碍物外部几何形状进行分析。
2) wavelet local maxima
小波局部极大值
3) local gray minimum
局部灰度极小值
1.
Secondly,detected all of the feature points on each scan lines in the image utilizing the method of finding local gray minimum points,and then abandoned the false feature points and got a fixed length iris code.
提出了一种快速而准确的虹膜识别算法,基本思想是:通过图像预处理,确定虹膜的位置和大小,并将直角坐标系下的环形虹膜展开成极坐标系下的矩形虹膜;利用寻找局部灰度极小值点的方法寻找图像中每条扫描线上的特征点,去掉伪特征点以后,得到固定长度的虹膜编码;计算两个虹膜编码之间的海明距离,根据海明距离给出识别结果。
4) local energy minimum
局部势能极小值
1.
The result discovered that,regarding this kind of large molecular cluster of structure optimization,we can very effectively find the local energy minimum by classical molecular dynamics.
结果表明,对于这种大的分子团簇的结构优化,经典分子动力学能很有效地找到体系局部势能极小值。
5) local extremum
局部极值
1.
Study on local extremum of object function in mutual information-based image registration;
基于互信息图像配准中的局部极值问题研究
2.
The difficulty of optimization caused by local extremum is the key problem of the algorithm.
由局部极值导致的寻优困难是困扰该算法的核心问题,混合优化算法成功地解决了互信息函数的寻优问题,但延长了配准时间。
3.
It can not only overcome the disadvantage of easily getting into the local extremum in the later evolution period,but also keep the rapidity of the previous period.
所提出的算法将粒子群优化算法和混沌算法相结合,既摆脱了算法搜索后期易陷入局部极值点的缺点,同时又保持了前期搜索的快速性。
6) Local maximum
局部极值
1.
Overcome of local maximum in mutual information-based image registration;
基于互信息量图像配准中目标函数局部极值的克服
2.
The cause of local maximum of object functions image registration was analyzed based on mutual information, and an optimization strategy by using simulated annealing-simplex method proposed.
分析了在基于互信息方法的图像配准中,目标函数产生局部极值的原因,提出以模拟退火单纯形法作为优化策略,该方法利用了单纯形法的一种修改后的形式作为模拟退火中随机变化的发生器。
补充资料：极小值
极小值
nunnnum
赫母票惠东谭巍瓢黑黑:n刀n刀刀uln points).
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