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Lagrange multiplier相关的网络例句

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

By analyzing the random disturbance series of the classical time series model, the existence of autoregressive conditional heteroscedasticity effect is demonstrated by the Lagrange multiplier test, and the non-satisfaction of homoscedasticity of the classical model is discusse...

通过分析经典时间序列模型随机扰动分量,论证了自回归条件异方差效应的存在性,探讨了经典模型同方差假设不满足的问题。

In this method,without depending upon small parameter,a trial function with possible unknowns is used as initial approximation,then a correction functional is constructed by means of a general Lagrange multiplier ,which can be identified via variational theory.

本文提出了一种求解非线性方程的迭代算法,它不依赖于小参数,是先给方程一个带待定函数的试函数作为初始近似解,然后用拉氏乘子法构造一个迭代公式。

The Lyapunov theorem and Lasalle invariance principle are applied to optimization sub-problem in augmented Lagrange multiplier method.

3.3同时本文还给出了一种基于基因算法和传统优化算法相结合的途径解决连续的MINIMAX优化问题。

It is difficult to solve the optimal iteration step and Lagrange multiplier in the gradient optimal power flow solutions. The saddle node iteration method was applied to solve the above problems based on a certain supposition.

在梯度法最优潮流的求解过程中,确定不等式约束的拉格朗日乘子以及求取最优步长等比较困难,文中在采取一定假设的基础上,运用鞍点迭代算法进行上述问题的求解。

We have defined minimum structure function for the CSAMT inversion based on genetic algorithm, and have found the optimal value of the Lagrange multiplier μ=0.5. The designed models are H, A, K, Q and HKH, KHA. When the data is without noise or with 10% noise, the resulting resistivity models fit the true models well.

利用H、A、K、Q和HKH、KHA模型对方法进行了数值试验,在无噪和加入10%噪声情况下,反演结果与模型一致;加入20%噪声后,反演仍取得良好结果,与理论模型基本吻合。

As a kind of optimization method, The Lagrange multiplier method is discussed in this paper.

本文首次提出了一个新的发电机无功功率生产成本的计算方法。

There is also a comprehensive treatment of optimality conditions, Lagrange multiplier theory, and duality theory.

这门课程也包括了对最适化条件,拉格朗日乘数理论,和对偶理论的综合论述。

Lagrange multiplier method has been the research object by people.

拉格朗日乘数法一直都是人们研究很多的数学对象。

This article tries to present a proof to Lagrange multiplier in the condition of n functions with m additional conditions.

本文就n元函数在m个附加条件下,给出拉格朗日乘数法的一个证明。

The neural network model based on Lagrange multiplier is used to solve the elasto-plastic mechanic finite element problems.

计算结果表明了该神经网络求解弹塑性力学有限元问题的可行性。

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