1) stochastic delay differential equations
随机延迟微分方程
1.
Only the Euler method is popular and efficient among the numerical methods for the stochastic delay differential equations,but its order of convergence is only 1/2.
随机延迟微分方程数值方法中欧拉方法是唯一较为成熟、有效的方法,但欧拉方法的收敛性差,其收敛阶仅为12。
2.
This paper investigates the adapted Milstein method for solving linear stochastic delay differential equations(SDDEs).
研究随机延迟微分方程(stochastic delay differential equations)的数值求解问题,将改造后的Milstein方法用于求解此类问题,精度较高。
3.
In the past several decades, stochastic delay differential equations and stochasticVolterra integral equations have been widely applied in many fields of science, such as inautomatic control, biology, chemical reaction engineering, medicine, economics, demog-raphy etc.
近几十年来,随机延迟微分方程与随机Volterra积分方程已经被广泛地应用到自动控制、生物学、化学反应工程、医学、经济学、人口学等众多领域中。
2) stochastic differential delay equation
随机延迟微分方程
1.
T-stability of the simi-implicit Euler method for the stochastic differential delay equations;
随机延迟微分方程半隐式Euler方法的T-稳定性
2.
This article considers the p-th moment stability of neutral stochastic differential delay equations with multiple functional delays.
本文考虑具有多个函数时滞的中立型随机延迟微分方程p阶矩稳定性。
3) stochastic differential delay equations
随机延迟微分方程
1.
T-stability of the euler-maruyama numerical method for the stochastic differential delay equations;
随机延迟微分方程Euler-Maruyama数值方法的T-稳定性
6) Nonlinear stochastic delay differential equations
非线性随机延迟微分方程
1.
Convergence of semi-implicit Euler methods for nonlinear stochastic delay differential equations;
非线性随机延迟微分方程半隐式Euler方法的收敛性
2.
The error analysis of Euler-Maruyama methods applying to a general class of nonlinear stochastic delay differential equations was concerned with.
首先利用附近已有节点上的值通过插值对延迟项进行数值逼近,这是一种崭新的尝试;然后针对较一般情形下的一类非线性随机延迟微分方程初值问题,得到了带线性插值的Euler-Maruyama方法在均方意义下是收敛的理论结果,它部分推广了已有文献中的相关结论。
3.
The mean-square stability of Milstein methods for the nonlinear stochastic delay differential equations was concerned with.
在一维情形下,研究了一类非线性随机延迟微分方程初值问题,证明了如果问题本身满足零解是均方渐近稳定的充分条件,那么当漂移项满足一定的限制条件时,Milstein方法是MS-稳定的与带线性插值的Milstein方法是GMS-稳定的理论结果。
补充资料：随机微分方程
见随机积分。
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