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导数过程
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  derivative process
     A virtual stochastic process and its derivative process are firstly constructed. According to the basic idea of the newly proposed probability density evolution method for dynamic response analysis of stochastic structures, an uncoupled one-dimensional partial differential equation, i.e., the probability density evolution equation (PDEE), is deduced.
     基于随机结构动力反应概率密度演化分析的基本思想,可构造一个与随机结构动力反应极值有关的具有“虚拟时间参数”的随机过程及其导数过程,导出了一维概率密度演化方程。
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  相似匹配句对
     The Derivative Approximation of an Interpolation Process by Lagrange Polynomial
     Lagrange插值过程导数逼近
短句来源
     Derivative-Appoximation of Interpolation Process by S. N. Bernstein Polynomial
     关于S.N.Bernstein插值过程导数逼近
短句来源
     New Study on Process
     “过程”新论
短句来源
     Value of Process
     过程的意义
短句来源
     HIGHER-DERIVATIVE GRAVITY
     高导数引力
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  derivative process
An Andean origin of the Lima bean and a double derivative process during the evolution of P.
      
It turns out that for this index model an equivalent risk neutral martingale measure does not exist because the corresponding Radon-Nikodym derivative process is a strict local martingale.
      
In addition, the nonlinear FDTD formulations using the Z-transform method are reformulated with the advantage of a simple derivative process.
      
After the derivative process, the curvature is estimated by replacing the signals derived in equation 1.
      
A certain amount of noise is nonetheless introduced by the spatial derivative process, especially at the junction region between the two PIV windows.
      
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In this paper we present a study of the almost sure asymptotic stability properties of second order linear stochastic system with an egodic stiffness coefficient. Using the probabilistic property of the derivative process of the stiffness coefficient, we obtain the stability condilions and present the numerical results for the cases of a Gaussian noise coefficient and periodic coefficient. The results are found to be an improvement over priviously available results for stochastic systems with slochastic stiffness....

In this paper we present a study of the almost sure asymptotic stability properties of second order linear stochastic system with an egodic stiffness coefficient. Using the probabilistic property of the derivative process of the stiffness coefficient, we obtain the stability condilions and present the numerical results for the cases of a Gaussian noise coefficient and periodic coefficient. The results are found to be an improvement over priviously available results for stochastic systems with slochastic stiffness.

对刚度系数是遍历过程的二阶线性随机微分方程,本文研究了其平凡解几乎处处渐近稳定性问题。利用刚度系数导数过程的性质,给出了平凡解几乎处处渐近稳定的充分条件。当刚度系数是遍历高斯过程或周期过程时,还具体计算了其渐进稳定区域。结果表明,本文结果改进了目前有关的渐近稳定性的条件。

With the increasing numbers of cable stayed bridges being constructed around the world and more emphasis being laid on multimode analysis of these bridges, there has been a need to extract all the flutter derivatives simultaneously only from coupled motion acceleration/velocity or displacement time histories of sectional model tests. A system identification method is introduced for the purpose. The process of identifying flutter derivatives for a bridge sectional model by using a theory of system identification...

With the increasing numbers of cable stayed bridges being constructed around the world and more emphasis being laid on multimode analysis of these bridges, there has been a need to extract all the flutter derivatives simultaneously only from coupled motion acceleration/velocity or displacement time histories of sectional model tests. A system identification method is introduced for the purpose. The process of identifying flutter derivatives for a bridge sectional model by using a theory of system identification can be divided into two stages: identification of complex modal parameters and transform from modal coordinates to physical coordinates. The algorithm of coordinate transform, in which a quadratic functional with constrains is established. By utilizing a penalty function method, all eight flutter derivatives can be simultaneously obtained according to a certain search direction. The final identified flutter derivatives make the selected quadratic functional a minimal value based on optimal theories. Numerical simulations and specimen tests show that the method is effective and reliable.

运用系统识别理论识别桥梁颤振气动导数的过程 ,可分为复模态识别与模态坐标转换为物理坐标两个阶段。文中着重研究坐标转换算法 ,该算法建立一个带约束的二次泛函 ,利用惩罚函数法以一定的搜索方向使二次泛函取得极小值 ,并同时求得 8个气动导数。数字仿真与实物试验结果表明文中方法有效、可行

A probability density evolution method for evaluation of extreme value distribution of the stochastic structural responses is presented. A virtual stochastic process and its derivative process are firstly constructed. According to the basic idea of the newly proposed probability density evolution method for dynamic response analysis of stochastic structures, an uncoupled one-dimensional partial differential equation, i.e., the probability density evolution equation (PDEE), is deduced. The PDEE is numerically...

A probability density evolution method for evaluation of extreme value distribution of the stochastic structural responses is presented. A virtual stochastic process and its derivative process are firstly constructed. According to the basic idea of the newly proposed probability density evolution method for dynamic response analysis of stochastic structures, an uncoupled one-dimensional partial differential equation, i.e., the probability density evolution equation (PDEE), is deduced. The PDEE is numerically solved through combining the deterministic dynamic response analysis and the finite difference method with TVD schemes. The extreme value distribution is then obtained by setting the virtual time as 1. The proposed method is capable for evaluation of the extreme value distribution of general random sampling and stochastic process. It is verified through the comparison with the theoretical extreme value distribution by using random sampling. An 8-story frame is studied. Some features of the extreme value distribution of the dynamic response is observed and analyzed.

提出了求解随机结构动力反应极值分布的概率密度演化方法。基于随机结构动力反应概率密度演化分析的基本思想,可构造一个与随机结构动力反应极值有关的具有“虚拟时间参数”的随机过程及其导数过程,导出了一维概率密度演化方程。结合结构动力反应的时程分析方法与有限差分方法,可求解该随机过程的一维概率密度函数。当虚拟时间参数为1时,即得到随机结构动力反应的极值分布。这一方法可用来求解一般的随机抽样和随机过程的极值分布。与随机抽样极大值分布的理论结果比较表明,本文建议方法具有良好的精度。在此基础上,分析了八层框架结构随机动力反应极值分布的若干特征。

 
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