对一般线性回归模型中有关参数估计分布的模拟问题，给出一种随机加权逼近的再构造方法。
A reconstructing method for random weighting approximations is proposed in approach to the distributions of the parameter estimates in general linear regression model.
把计算方法中的最小二乘法与泛函的抽象空间联系起来，得到最佳逼近问题的抽象提法，并在赋范线性空间探讨最小二乘法。
This article focuses on the topics that by connecting the the least-squares Minimization with the functional abstract space, the abstract definition of the best approximation problem is abtained.
本文研究具有某些固定元素的矩阵在线性约束下的最佳逼近，其结果可以用于解一类矩阵反特征值问题。
In this paper, we consider the best approximation of a matrix under a given linear restriction with some fixed elements. This result can be apply to solving a class matrix inverse eigenvalue problem.
本文先把问题转化为一个随机非线性规划问题，然后用逼近技术给出一个简单的求解方法。
We first formulate the problem as a stochastic nonlinear programming problem and then propose a new simple method for solving it via some approximation techniques.
利用凝聚函数一致逼近非光滑极大值函数的性质，将非线性互补问题转化为参数化光滑方程组。
By using a smooth aggregate function to approximate the non-smooth max-type function, nonlinear complementarity problem can be treated as a family of parameterized smooth equations.
数值模拟结果与数值解比较表明：正交多项式逼近法能有效地解决此类非线性随机动力系统的响应问题。
Numerical simulation implies that the proposed method is a new effective approach to dynamical responses of stochastic nonlinear systems.
本文提出了一种新的解析逼近方法来求解一类非线性振动问题。
In this thesis, a new analytical approximate method is presented to solve large amplitude nonlinear oscillations of single degree of freedom non-natural conservative systems.
最后将所提出的方法用于解决非线性函数的逼近问题。
Finally, the proposed method is applied to the problem of nonlinear function approximation.
该非线性优化问题用渐进外逼近算法可求出其全局最优解。
The sequential outer approximation approach can give a global optimization solution to the original problem.
求解开普勒方程可用逐次逼近法，这种方法还可推广到非线性方程的求解问题中。
Kepler s equations can be solved with the gradual approach, which can be further extended to the solution of the non-linear equations.
讨论了线性流形上广义次对称矩阵反问题的最小二乘解及其逼近问题。
The least squares solution of inverse problems of generalized skew symmetric matrices and It's optimal approximation problems are discussed.
本文主要基于神经网络的非线性逼近性质建立预测模型，来研究我国火灾损失的预测问题。
In this thesis, the prediction model of fire cost is built mainly based on the nonlinear approximation property of neural networks, which can be used to investigate our country's fire cost.
提出了求解非线性互补问题的一个光滑逼近算法，在一定条件下证明了该算法的全局收敛性。
A smoothing approximation algorithm for nonlinear complementarity problems was introduced and the global convergence of the algorithm was proved under milder conditions.
提出了求解非线性互补问题的一个光滑逼近算法，在一定条件下证明了该算法的全局收敛性。
A smoothing approximation algorithm for nonlinear complementarity problems was introduced and the global convergence of the algorithm was proved under milder conditions.
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