均值定理

词组短语

mean value theorem

与均值定理相关的网络例句 [注：此内容来源于网络，仅供参考]

This paper discussed the theorem of the average arithmetic geometric mean of algebraic polynomials systematically, then derived and analyzed the original geometric programming briefly.

系统地讨论了代数多项式的算术－几何均值定理，并对原型几何规划理论作出了简明的推导与分析。提出了具有缩并迭代特性的几何规划求解理论和编程步骤。

In this dissertation, we study the mean value problem of some important arithmetical function.

本文研究了一些算术函数的均值估计问题，获得了一些均值定理。

Central Limit Theorem -- States that distribution of sample means tends towards an approximate normal distribution.

中心极限定理--样本的均值近似服从正态分布。如果样本容量较大，那么其均值的分布越近似于正态分布。

Most of them rely on the central limit theorem, that states that the mean of N independent realizations of a random variable with mean u and variance d^2 approaches a normal distribution with mean u and variance d^2/N .

在蒙特卡罗评估框架内大量的各种选择性算法已经被推出，他们中的大部分依赖于中心极限定理，一个均值为U方差为D^2的随机变量的N次独立实现的平均值接近于一个均值为U方差为D^2/N的正态分布。

The the probabilistic forecasting method of the error between the sample mean and expectation value of the random variable utilizing the central limit theory was researched, then the mathematical formula for the relation between sampling size and variance coefficient of loss of load probability was analyzed. Furthermore, the formulas for the relation between confidence intervals of variance coefficient and sampling size are deduced.

基于中心极限定理深入研究随机变量的样本均值与期望值之间误差的概率预测方法，在此基础上分析失负荷概率(loss of load probability，LOLP)指标的方差系数和样本容量之间的关系表达式，导出方差系数给定时的样本容量置信区间公式及样本容量给定时的方差系数置信区间公式。

ChinaWang Yonghuidirected by Zhang ~Venpeng The main purpose of thesis is using the mean value theorem of the Dirichlet Lunctions to study the asymptotic property of the Dedekind sums and Hardy sums, and give two sharper asymptotic formulas.

关于Dedekind和的推广型均值公式及Hardy和的推广型均值公式本论文利用Dirichlet L-函数的均值定理研究了Dedekind和及Hardy和的一类均值估计问题，并给出了较为精确的渐近公式。

In Chapter 4,we obtain the optimality for the quasi-score function 〓in a more general class of estimation functions〓,where〓and〓or〓This result is a extension of Gauss-Markov theorem of the linear model to the nonlin-eaar model.

在本章中，我们得到拟得分函数〓在一种较大的估计函数类〓中的最优性，其中〓而〓或〓于是，我们得到类似于线性模型中的Gauss-Markov定理和拟得分函数的拟Fisher信息上界及达到上界的充要条件：〓，其中〓为真实未知的得分函数，〓为模型均值。

In our thesis, contents are organized as following In Chapter 1 we present our topic's internal and overseas research situations, theoretical and practical significance, and introduce the research object and contents, and the main contributions of this dissertation. Chapter 2 reviews the development of the stability results for nonlinear systems and some relevant recent results, which include Lyapunov and LaSalle-Yoshizawa theorems for nonlinear systems, and stochastic edition for stochastic nonlinear systems. Sontag's formula for systems affine in control is presented in the frame of CLF. The concepts of disturbance attenuation and the inverse optimality are also explained in this Chapter. In chapter 3 we present the solvable theorem of inverse optimal gain assignment problem, design the inverse optimal controller and the inverse optimal tracking controller for strict-feedback nonlinear continuous systems with unknown time-varing bounded disturbances and constant unknown parameters using an adaptive backstepping algorithm, which are nonlinear, continuous and are easier to realize. These designs are fully systematic and the algorithm can be directly coded in symbolic software. The results of simulation show the effectiveness of the control algorithms.

论文的结构如下：在第1章中，给出了本文研究课题的研究现状、理论意义和实际应用，并介绍了本文的研究对象、研究内容以及主要贡献；在第2章中，针对确定性非线性系统和随机非线性系统，分别介绍了Lyapunov定理、LaSalle-Yoshizawa定理及其随机版本；对仿射系统，在控制Lyapunov函数框架下，给出了Sontag公式；同时给出了非线性系统扰动抑制和逆最优控制问题的基本概念；在第3章中，针对具有未知时变有界扰动和未知定常参数的一类不确定非线性系统，给出并证明了逆最优增益配置可解定理，使用自适应Backstepping算法和均值定理，系统地设计了自适应逆最优控制器和逆最优跟踪器，这种设计方法可同时获得逆最优控制策略和自适应律，简单明了，仿真结果表明该控制算法的有效性，并给出了性能估计。

With a view of this, we propose the key theorem and discuss the bounds on the rate of uniform convergence...

基于此种考虑，本文提出了样本受零均值噪声影响下的学习理论的关键定理，并讨论了零均值噪声样本下的学习过程一致收敛速度的界。

With a view of this, we propose the key theorem and discuss the bounds on the rate of uniform convergence of learning processes based on ERM principle when samples are corrupted by zero-expect noise.

基于此种考虑，本文提出了样本受零均值噪声影响下的学习理论的关键定理，并讨论了零均值噪声样本下的学习过程一致收敛速度的界。

与均值定理相关的网络解释 [注：此内容来源于网络，仅供参考]

Cartesian coordinates system：笛卡儿坐标系

Cartesian coordinates :笛卡儿坐标,一般指直角坐标 | Cartesian coordinates system :笛卡儿坐标系 | Cauch's Mean Value Theorem :柯西均值定理

cauchy kernel：柯嗡

cauchy integral formula 柯锡分公式 | cauchy kernel 柯嗡 | cauchy mean value formula 广义均值定理

cauchy mean value formula：广义均值定理

cauchy kernel 柯嗡 | cauchy mean value formula 广义均值定理 | cauchy net 柯硒

cauchy net：柯硒

cauchy mean value formula 广义均值定理 | cauchy net 柯硒 | cauchy principal value 柯蔚

extended mean value theorem：广义均值定理

extended ideal 广义理想 | extended mean value theorem 广义均值定理 | extended plane 扩张平面

mean value method：中值方法,均值方法

mean-value formula 中值公式,均值公式 | mean-value method 中值方法,均值方法 | mean-value theorem 中值定理,均值定理

mean value theorem：平均值定理

mean value method 平均值法 | mean value theorem 平均值定理 | mean vector 均值向量

mean value theorem：均值定理

mean value process 平均值法 | mean value theorem 均值定理 | mean variation 平均偏差

Cauch's：柯西均值定理

Cartesian?coordinates?system?:笛卡儿坐标系 | Cauch's?Mean?Value?Theorem?:柯西均值定理 | Chain?Rule?:连锁律

Cauch's Mean Value Theorem：柯西均值定理

Cartesian coordinates system :笛卡儿坐标系 | Cauch's Mean Value Theorem :柯西均值定理 | Chain Rule :连锁律