欢迎您！东篱公司
退出
-
申报数据库
-
立项数据库
-
成果数据库
-
项目获奖数据库
中文摘要：
传统的Monte Carlo方法仿真稀有事件需要较长的时间,而重要抽样技术可以有效的缩短仿真时间,提高仿真效率。因此,提出一种新的重要抽样实现方法,用来估计仿真模型中的稀有事件的概率。先选取经典的指数变换方法构造重要抽样分布类,再利用极小化重要抽样估计量方差的方法寻找最优重要抽样分布函数。仿真结果显示了该算法在估计稀有事件概率方面的有效性。
英文摘要：
It usually takes long time to simulate rare event using traditional Monte Carlo method, while importance sampling techniques can effectively reduce the simulation time and improve simulation efficiency. A new implementation for importance sampling method to estimate rare event probability in simulation models was proposed. The classical exponential change of measure was adopted to construct the family of importance sampling distributions, and the optimal importance sampling distributions was obtained by minimizing the variance of importance sampling estimator. Numerical experiments have been conducted and the results indicate that the method can effectively estimate the rare event probabilities.
同期刊论文项目
同项目期刊论文
Equity and efficiency analysis of pricing strategies in a bi-modal network with heterogeneous user g
Modeling the evolutions of day-to-day route choice and year-to-year ATIS adoption with stochastic us
Modeling time-dependent travel choice problems in road networks with multiple user classes and multi
One-step solution fabrication of magnetic chains consisting of jingle-bell-shaped cobalt mesospheres
Support vector machine forecasting method improved by chaotic particle swarm optimization and its ap
Static floor field and exit choice for pedestrian evacuation in rooms with internal obstacles and mu
A model for evaluation of transport policies in multimodal n etworks with r oad and p arking capacit
期刊信息
位置：