3

常微分方程数值解法，又是一个数值分析的常用算法(Numerical Solution of ordinary differential equations, numerical analysis is a common algorithm)

自己编的5种调制信号，混沌的判断指标Lyapunov指数计算,信号维数的估计。( Own five modulation signal, Chaos indicator for Lyapunov index calculation, Signal dimension estimates.)

该算法，先对协方差矩阵进行休整，然后用信息论的方法得出信号的源数，这是进行空间普估计的基础(The algorithm, first on the covariance matrix for a rest, and then used information theory methods to draw a number of signal sources, it is estimated that the basis of space S )

Wdisp，是基于建造结构计算软件pkpm的结果数据提取文件，方便使用，提取数据后，可用于绘制曲线。(Wdisp, is based on The construction structure calculations software pkpm the results of data extraction files, easy to use, extract data, can be used to draw the curve.)

这是非均匀B样条曲线构造方法代码，如果有问题可以邮件联系：wuxiaod1987@163.com(NUBUS Spline Build)

33节点基于前推回代法的含分布式电源配电网的潮流计算程序，其中含支路阻抗的原始数据。(33 nodes with distributed power distribution network procedures, the former push back method, with branch impedance of the original data.)

xfdtd中用于计算光子晶体PBG的算例(example used for PBG calculation in xfdtd)

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：  ABAQUS 的二次开发,详细注释说明，自学ABAQUS的二次开发(ABAQUS secondary development, detailed notes, self-study ABAQUS secondary development)

说明：  微分进化算法中的其中两个子程序，还有未上传(Differential evolution algorithm)