1) cost statistic
代价统计
2) computation cost
计算代价
1.
In the interest of improving collision arithmetic and decreasing computation cost, a new Multi-Monte Carlo (MMC) method for inter-particle collision is promoted.
从减少计算代价和改进碰撞算法出发,提出了考虑颗粒碰撞的多重MonteCarlo算法,它采用直接模拟MonteCarlo算法来考虑颗粒碰撞,并与求解颗粒拉氏Langevin方程的MonteCarlo算法耦合起来,跟踪比实际颗粒数目小得多的虚拟颗粒。
2.
Monte Carlo (MC) method is widely adopted to take into account general dynamic equation (GDE) for particle coagulation, however popular MC method has high computation cost and statistical fatigue.
MonteCarlo(MC)方法被广泛用于通用动力学方程的求解,然而普通MC方法的计算代价较高而计算精度不稳定· 提出一种新的多重MonteCarlo(MMC)算法来求解GDE,该算法同时具有基于时间驱动MC方法、常数目法和常体积法的特点· 首先详细介绍了该算法,包括加权虚拟颗粒的引入,MMC算法的计算流程,时间步长的设置,颗粒是否发生凝并事件的判断,凝并伙伴的寻找,凝并事件的后果处理· 然后利用MMC算法对存在理论分析解的5种特殊工况进行数值求解,模拟结果与理论解符合很好,证明MMC算法具有良好的计算精度和较低的计算代价· 最后分析了不同类型的凝并核对于凝并过程的影响,常凝并核和连续区布朗凝并核对小颗粒影响大一些,而线性凝并核和二次方凝并核对大颗粒影响大一些·
3) computational cost
计算代价
1.
By using the information of local optimal solutions difference among some neighbor generation,SAVPGA can obtain approximate solutions with less computational cost by varying population size based on logistic model when premature convergence occurs.
利用相邻几代群体间的极优解差异信息,在遗传算法发生早熟现象时根据逻辑斯蒂模型来改变种群规模,能以较小的计算代价获得与其它遗传算法性能相近的解。
4) estimate cost
估计代价
1.
A tree formation algorithm for data aggregation based on estimate cost is presented.
本文采用定向传输方式,在消息路由机制基础上提出了一种基于估计代价的数据聚合树生成算法。
6) statistical evaluation
统计评价
1.
Previous research on lexical collocation extraction have considered that most statistical evaluation approaches have the same effective ness, however, analyses show that these approaches have different advantages and disadvantages so they can complement each other.
以往的词汇搭配抽取统计评价方法具有大致相同的效果,它们各有优劣,可以实现优势互补。
补充资料：分布和特征量统计(见统计分析)
分布和特征量统计(见统计分析)
fenbu he tezhengliang tongjj分布和特征量统计见统计分析。
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