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stochastic dynamic model相关的网络例句

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与 stochastic dynamic model 相关的网络例句 [注:此内容来源于网络,仅供参考]

The stochastic Bonhoeffer-Van der Pol system is first transformed into an equivalent deterministic system using the Chebyshev polynomial approximation, so that the problem of controlling stochastic chaos is reduced to the problem of controlling deterministic chaos in the equivalent system.

首先运用Chebyshev多项式逼近的方法,将随机Bonhoeffer-Van der Pol系统转化为等价的确定性系统,使原系统的随机混沌控制问题转换为等价的确定性系统的确定性混沌控制问题,继而可用Lyapunov指数指标来研究等价确定性系统的确定性混沌现象和控制问题。数值结果表明,随机Bonhoeffer-Van der Pol系统的随机混沌现象与相应的确定性Bonhoeffer-Van der Pol系统极为相似。

The up useful stochastic order and the down useful stochastic order has the closure property of under convolution, and maximum and minimum of the random variable.

而且向上和向下一般随机序关于卷积、随机变量的极大和极小等具有封闭性。

The comparison between the power spectrum of the signals preceded and passed stochastic resonance implies that, the 3 level quantizer stochastic resonance is good for the detection of weak line spectrum buried in Gaussian mixture noise.

随机共振系统处理前后的功率谱对比表明,3-水平量化器随机共振是检测高斯混合分布中微弱线谱的有效手段。

The paper first introduces the basic principle of Karhunen-Loeve decomposition for stochastic processes, and outlines the essential properties of the decomposition. A method based on the Hartley orthogonal bases is proposed to decompose stochastic processes.

介绍了随机过程Karhunen-Loeve分解的基本原理,指出了其在随机过程展开中所具有的优势与局限性,针对Karhunen-Loeve分解在求解特征问题中存在的困难,采用Hartley正交基作为展开函数,发展了一类基于Hartley正交基的随机过程展开方法。

A class of affine nonlinear system of single input-single output is investigated. Firstly, the nonlinear system is transformed into an equivalent linear system through a diffeomorphism transformation. The networked control problems for the delays are constant, stochastic and shorter than a sampling period, and long and stochastic are investigated respectively.

针对一类单输入-单输出仿射非线性系统,首先通过微分同胚变换实现了仿射非线性系统的无反馈精确线性化;然后基于精确化线性模型,分别讨论网络延时为常时滞,随机时滞小于一个采样周期和随机长时滞情况的控制问题。

Finally, favored by the article "Stochastic differential utility" written by Duffle and Epstein, we define the notion of Stochastic differential utility under Maos conditions. Similar to common utility functions, they preserve many good characters such as continuance, monotonousness, risk aversion and so on.

最后,受Duffie和Epstein的"Stochastic differential utility"一文的启发,在毛氏条件下定义了随机微分效用,它保持了一般效用函数的性质:单调性,连续性,风险厌恶以等。

Stochastic variables included driven accelerations, driven time, torques, frictions and damps were considered basically. In the first, Monte Carlo method was applied to generate stochastic variables and dynamical responds of mechanism. Secondly, the application of Artificial Neural Network was motivated by the approximate concepts inherent in reliability analysis and time consuming repeated analyses required for MC.

将驱动加速度、驱动时间、摩擦和阻尼力等作为随机变量,应用蒙特卡罗方法,取得动态参数样本,再利用人工神经网络方法,根据抽取的样本对网络进行训练,统计网络输出得到动态应力分布,进而求出机构动态强度可靠度。

Stochastic control system ; limited control ; exact controllability ; backward stochastic differential equation

本文组织如下,第二节引入主要定义,介绍倒向随机微分方程及彭有关随机精确能控性

Basic concepts of stochastic processes: family of finite dimensional distribution functions, numerical characteristics of stochastic processes.

随机过程的基本概念:有限维分布函数族,随机过程的数字特征。

Advanced course covering topics selected from: semimartingale theory, stochastic integrals, homogeneous chaos expansions, stochastic differential equations, Malliavin calculus, infinite dimensional processes, functional central limit theorems, Feynman-Kac formula, Feynman integral.

随机分析:高级课程,包括:半鞅理论,随机积分,齐次混沌展开,随机差分方程, Malliavin 微积分,,有限维过程,泛函中心极限理论, Feynman-Kac公式, Feynman积分,过滤理论的应用,无限粒子系统,量子力学,神经学中的随机模型。

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