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有均匀线阵的CRB曲线，自己编的5种调制信号，用于建立主成分分析模型。( There ULA CRB curve, Own five modulation signal, Principal component analysis model for establishing.)

说明：  同时求取不同信号的多个时域特征参数，可以随意更换待分析信号(Multiple time domain characteristic parameters of different signals are obtained at the same time.)

代数多重程序中的 W 循环只适合 4 层 网格的 W 循环( U4EE3 u6570 u591A u91CD u7F51 u683CW u578B)

用于求解二维欧拉流动方程的有限差分程序,基于NND法,求解精度较高.(Perfect fortran program by two dimensional NND finite difference method for Euler Equations on unstructured Grids, structured Grids and adaptive unstructured Grids. )

基于TMS320LF2407的FFT算法的实现及应用(TMS320LF2407 of the FFT algorithm based on Implementation and Application of)

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(Conjugate gradient method (Conjugate Gradient) is between the steepest descent method and Newton' s method between a method that takes only a first derivative information, but to overcome the slow convergence of the steepest descent method shortcomings, but also avoid the need to store Newton and computing the inverse Hesse matrix and disadvantages, conjugate gradient method is not only to solve large linear equations of the most useful methods, large-scale nonlinear optimization solution is the most efficient algorithms)

说明：  LDL分解。作为矩阵方程数值解法最基础的矩阵分解算法，LDL算法可以高效分解对称矩阵。(LDL decomposition. Numerical Solution of matrix equation as the most basic matrix factorization algorithm, LDL decomposition algorithm can be efficient symmetric matrix.)

采用随机数产生2到10阶的随机矩阵，并实现了矩阵的相关运算，比如：加减，乘除，求逆，求行列式(Using random Numbers generated random matrix from 2 to 10 order , and achieving relative to the implementation of the matrix operations, such as: add and subtract, multiple and divided, inverse and determinant)

计算正交各向异性材料（如木材，复合材料等）的弹塑性本构(The elastoplastic constitutive relation of orthotropic materials is calculated)

自己编写的求可达矩阵，层次划分 代码.使用时，替换已有矩阵，即可运算！ (I have written for reachability matrix, the level of classified code. Use, replace the existing matrix, computing can be!)