查询词典 class function

At the same time, we prove that the best linear unbiased prediction of a linear predictable variable Qy and the best linear unbiased prediction βn=(X"sV-1Xs)-1 X"sVs-1ys of regression coefficients of the finite populations βn=(X"V-1X)-1X"V-1y are admissible under matrix loss function and in the class of the linear predictions and the class that contains all predictions, and under quadratic loss function in the class of the linear predictions.

在线性预测类和一切预测类中是可容许预测，而在二次损失函数下其在线性预测类中是可容许预测。

The unary test function can be a free function, unary functor (for example created with boost::bind) or unary method of a class with bound test class instance.

参单测试用例可以是自由函数，单参函数对像(如由 boost：：bind 创建)或者绑定於测试类实例的单参类方法。

It is proved that the two-dimensional non-hydrostatic Boussinesq equations on the x ? z plane with the kinetic viscous term and the thermal dissipative term are unstable equations in the C 2 function class.2. If we replace the influence of the kinetic viscous term by Rayleigh friction and the thermal dissipative term by Newton cooling, the new generalized equations are stable equations in the C 1 function class. The construction of the solution space and the discriminating method for well-posedness of the problem of determining solution are given.

本文的主要结果如下：1证明了在y方向上均匀的带有运动粘性项以及热耗散项的两维非静力、Boussinesq近似的x-z面上两维旋转流体的控制方程组在C 2函数类中是一个不稳定的方程组。2如果以瑞利摩擦来代替粘性的影响，以牛顿冷却来代替热量的耗散，则所获得的一个新的方程组在C 1函数类中是一个稳定的方程组，给出了其解空间的构造和各种定解问题的适定性的判别方法。

A friend may be an ordinary, nonmember function, a member function of another previously defined class, or an entire class.

友元可以是普通的非成员函数，或前面定义的其他类的成员函数，或整个类。

In chapter 2, we firstly introduce the model of a discrete-time neural network with generalized input-output function. The model generalizes the input-output function in transiently chaotic neural network to a class of continuous, differentiable and monotone increasing functions. Secondly we study the uniformly asymptotical stability of equilibrium in the non-autonomous model. Finally, several examples and numerical simulations are given to illustrate and reinforce our theories. In chapter 3, we firstly introduce a specific class of discrete-time neural network models with sinusoidal activation function.

在本文的第二章中，首先介绍了一类具有广义输入输出函数的非自治离散神经网络模型，该模型把瞬时混沌神经网络模型中的输入输出函数推广到了一般的单调递增且连续可微的函数；其次，利用Schauder不动点原理和利用Lyapunov函数逆定理依次证明了模型平衡点的存在性和该模型在时变权值下的一致渐近稳定性；最后，对几个具体的例子进行数值模拟，数值模拟的结果更好地说明了我们的结论。

The model generalizes the input-output function in transiently chaotic neural network to a class of continuous, differentiable and monotone increasing functions. Secondly we study the uniformly asymptotical stability of equilibrium in the non-autonomous model. Finally, several examples and numerical simulations are given to illustrate and reinforce our theories. In chapter 3, we firstly introduce a specific class of discrete-time neural network models with sinusoidal activation function.

在本文的第二章中，首先介绍了一类具有广义输入输出函数的非自治离散神经网络模型，该模型把瞬时混沌神经网络模型中的输入输出函数推广到了一般的单调递增且连续可微的函数；其次，利用Schauder不动点原理和利用Lyapunov函数逆定理依次证明了模型平衡点的存在性和在时变权值下的一致渐近稳定性；最后，对几个具体的例子进行数值模拟，数值模拟的结果更好地说明了我们的结论。

With the idea of smoothing Newton method, we propose a new class of smoothing Newton methods for the nonlinear complementarity problem based on a class of special functions. In this paper, complementarity problem is converted into a series of smoothing nonlinear equations and a modified smoothing Newton algorithm is used to solve the equations. We use Newton direction and Gradient direction together in the algorithm which guarantees that our method is globally convergent. Also using another smoothing function, we reformulate the generalized nonlinear complementarity problems defined on a polyhedral cone as a system of smoothing equations and a smooth unconstrained optimization problem. Theoretical results that relate the stationary points of the merit function to the solution of the generalized nonlinear complementarity problems are presented, we use the modified smoothing Newton algorithm in generalized nonlinear complementarity problems, under mild hypothesis, a global convergence is proved.

本文一方面基于现有的各种光滑Newton法的思想和半光滑理论，利用著名的F-B互补函数的光滑形式，首先将互补问题的求解转化为求解一系列光滑的非线性方程组，然后给出了一种修正的光滑Newton法，该方法不仅放宽对函数F的要求，在Newton方程不可解时引入初始效益函数的最速下降方向，而且光滑因子的选择也比较简单可行，同时在适当的条件下，证明了其算法具有全局收敛性；另一方面，借助另一种F-B光滑函数，将多面体锥上的广义互补问题转化为一种光滑形式，讨论了优化问题的稳定点与广义非线性互补问题的解之间的理论关系，并将这种修正的光滑Newton法用于求解广义非线性互补问题中，在适当的条件下，该算法同样具有全局收敛性。

It is not necessary that the function definition is textually enclosed in the class definition: assigning a function object to a local variable in the class is also ok.

不是必须要将函数定义代码写进类定义中，也可以将一个函数对象赋给类中的一个变量。

For a class of nondifferentiable generalized fractional programming problems with the norm \$‖Bx‖\-p\$ in the objective function involving, a new incomplete Lagrange function is given, and the saddle point optimal criteria are proven by using the existing necessary optimality conditions, under the assumptions of the class of generalized (\$F,α,ρ,d\$)-convexity.

对于一类目标函数中含范数‖Bx‖p的非可微广义分式规划，给出了一个新的非完全Lagrange函数，并利用已有的最优性必要条件，在一类广义-凸性的条件下，证明了鞍点最优性准则。

At the same time, there is also limitation on Lukacs theory of social class consciousness. The paper gives it an objective evaluation, considering that it aggrandizes the motive function of subjective consciousness, excessively emphasizes the class consciousness motive function.

同时，卢卡奇的阶级意识理论自身也存在着局限性，论文对其局限性进行了客观评价，认为其阶级意识理论过分夸大了阶级意识的能动性。

The split between the two groups can hardly be papered over.

这两个团体间的分歧难以掩饰。

This approach not only encourages a greater number of responses, but minimizes the likelihood of stale groupthink.

这种做法不仅鼓励了更多的反应，而且减少跟风的可能性。