UMAT-Example

说明：  工程中微分方程数值积分经常需要使用的Newmark法(Differential equations numerical integration project often requires the use of the Newmark method)

本程序实现了基于最佳维纳滤波器的盲解卷积算法。维纳滤波将地震子波转换为任意所需要的形态。例如，像反滤波一样，可以设计维纳滤波器将地震子波转换为尖脉冲。但维纳滤波不同于反滤波，它是在最小平方的意义上为最 佳。基于最佳纳滤波理论的滤波器算法是人们熟知的维纳一莱文逊(Wiener—Levinson)算法。本程序提供了五种期望输出：零延迟尖脉冲；任一延迟尖脉冲；时间提前了的输入序列；零相位子波；任意期望波形。(Wiener—Levinson algorithm used in wiener filter. matlab with detailed note in Chinese.)

《Optimal State Estimation - Kalman, H Infinity, and Nonlinear Approaches》 一书的配套源码，包括了Kalman Filter、Hinf Filter、Particle Filter等的Matlab源码(《Optimal State Estimation- Kalman, H Infinity, and Nonlinear Approaches》source code，including Matlab code of Kalman Filter、Hinf Filter、Particle Filter.)

说明：  几种模幂算法，有传统模幂，SMM模幂，基于2的K次方的模幂和最一般的模幂算法，还有为这些模幂算法建立的WINDOWS对话框。可以方便的运行。(algorithm, the traditional mode power, SMM Modular Power, Based on two of the K-th power module and the most common mode power algorithm, These modules also for the establishment of the algorithm Power Windows dialog box. To facilitate the operation.)

Abaqus UMAT code 适用于平面应变，轴对称弹塑性本构关系，也适用于大应变情况，使用显示积分算法。(Abaqus UMAT code applies to plane strain, axisymmetric elastoplastic constitutive relation, also applicable to large strain, using the integration algorithm.)

matpower工具箱中的算例，基于ieee118的最优潮流计算，包含源代码(matpower toolbox example, calculated based on the ieee118 the OPF contains the source code)

说明：  利用matlab软件进行数值编程，优化计算。(Matlab software is used for numerical programming and optimization calculation.)

可靠性估计时的二项分布风险评估实验的参数估计与预测算法(Reliability estimates of the binomial parameter estimation risk assessment and prediction of the experimental method)

共轭梯度法（Conjugate Gradient）是介于最速下降法与牛顿法之间的一个方法，它仅需利用一阶导数信息，但克服了最速下降法收敛慢的缺点，又避免了牛顿法需要存储和计算Hesse矩阵并求逆的缺点，共轭梯度法不仅是解决大型线性方程组最有用的方法之一，也是解大型非线性最优化最有效的算法之一。 在各种优化算法中，共轭梯度法是非常重要的一种。其优点是所需存储量小，具有步收敛性，稳定性高，而且不需要任何外来参数。(Conjugate gradient method (Conjugate Gradient) is between the steepest descent method between the method and Newton' s method, it takes only a first derivative information, but to overcome the steepest descent method convergence slow shortcomings, but also to avoid the Newton method needs to be stored Hesse and disadvantages of computing inverse matrix and the conjugate gradient method is not only one of the most useful methods to solve large linear equations, solution of large-scale nonlinear optimization is one of the most effective algorithm. In various optimization algorithm, conjugate gradient method is a very important one. The advantage is that a small amount of memory required, with step convergence, high stability, and does not require any external parameters.)

说明：  对时间序列进行滤波，去掉噪声，得到平滑后的数据(Filters a time series via Lanczos method (cosine filter).)