33nodes

详细画出了时域和频域的相关图，实现了图像的灰度化并进一步用于视频监视控，小波包分析提取振动信号中的特征频率。( Correlation diagram shown in detail the time domain and frequency domain, Achieve a grayscale image and further control for video surveillance, Wavelet packet analysis to extract vibration signal characteristic frequency.)

说明：  用于对ADC采集正弦波后得到的数据进行FFT，计算得到ADC的动态性能参数(ADC DYNAMIC TEST(FFT))

理想流体势流的有限元求解程序,简单易懂，适合初学者对该方法编程的一个初步认识,程序内有详细的注释(Potential flow of ideal fluid finite element solver, easy to understand, this method is suitable for beginners programming a preliminary understanding of the program with detailed notes)

这是几篇关于粒计算在故障诊断中的应用，对学习粒计算的应用很有帮助。(This is a few on Granular Computing in Fault Diagnosis of learning the application of granular computing helpful.)

一个由C语言写成的矩阵运算程序源代码，共初学者参考。(A C language source code matrix calculation, a total of reference for beginners.)

扩展有限元软件包,用于模拟连续介质裂纹的萌生与扩展(XFEM simulation code written in MATlab)

Fractional knapsack problem, Greedy algorithm

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)

PCA算法程序设计步骤： 1、去均值 2、计算协方差矩阵及其特征值和特征向量 3、计算协方差矩阵的特征值大于阈值的个数 4、降序排列特征值 5、去掉较小的特征值 6、去掉较大的特征值(一般没有这一步） 7、合并选择的特征值 8、选择相应的特征值和特征向量 9、计算白化矩阵 10、提取主分量 (PCA algorithm programming steps: 1 to mean 2, calculation of covariance matrix and its eigenvalues and eigenvectors 3, the calculation of covariance matrix eigenvalues greater than the threshold number of 4, in descending order of eigenvalue 5, remove the smaller eigenvalue 6, remove the larger eigenvalue (usually not this step) 7, the combined choice of eigenvalues 8, select the appropriate eigenvalues and eigenvectors 9, computing whitening matrix 10, principal component extraction)

变系数/非线性微分方程的求解，包括原式及改变的式子。 (Variable coefficients/non-linear differential equations to solve, including the original style and formula changes.)