For3d

传统的算法是基于邻接矩阵对网络指标进行计算，而大型网络通过稀疏矩阵保存。本研究编写了可以计算稀疏矩阵的经典传染病SIS模型的程序。(The traditional method is based on network indicators adjacency matrix computing, and large networks through sparse matrix saved. The study prepared by the epidemic SIS model can calculate the classical sparse matrix program.)

盒维数计算和豪斯多夫维数的计算与应用研究程序(box counting)

abaqus在岩土工程中应用，第六章其中的一段源代码，(apply for geotectnical)

针对运用最小二乘法求解DV-Hop定位算法带来的节点定位误差较大的问题，提出基于代数重建法的DV-Hop定位算法(Least square method for the use of DV-Hop localization algorithm node localization errors caused by the larger issues, proposed algebraic reconstruction method based on DV-Hop localization algorithm)

支持向量机作为统计学习理论的实现方法，能很好地解决非线性和高维数问题，克服了神经网络方法收敛慢、解不稳定、推广性差的缺点，近年来得到了广泛地研究，在模式识别、信号处理、控制、通讯等方面得到了广泛地应用。(Support Vector Machine as the implementation of statistical learning theory approach, can be a good solution to the nonlinear and high dimension problem, the neural network method to overcome the slow convergence, solution instability, bad promotion of sexual shortcomings, in recent years has been widely studied, at pattern recognition, signal processing, control, communications, etc. has been widely applied.)

数值分析实验1.明确插值多项式和分段插值多项式各自的优缺点； 编程实现拉格朗日插值算法，分析实验结果体会高次插值产生的龙格现象；(Experiments of numerical analysis 1. Define the advantages and disadvantages of interpolation polynomials and piecewise interpolation polynomials. The Lagrange interpolation algorithm is programmed, and the Runge phenomenon produced by high-order interpolation is analyzed.)

COMSOL工程案例的英文书籍，亚马逊网站可查，售价不费。(COMSOL project case of English books, Amazon website can be found, the price is not cost.)

《最优化理论与方法》书籍中的乘子法的源程序，该书中的很多案例都用此方法试验过，本代码是一个小案例，将目标函数和约束函数按自己的需求换掉就能进行所期望的运算(" Optimization Theory and Methods" books multiplier method of the source, the book' s many cases are tested using this method, the code is a small case, the objective function and constraints to replace it according to their needs can perform the desired operation)

三维的FDTD实现，采用CPML边界，较为详细的注释(Three-dimensional FDTD using CPML border, more detailed notes)

支持向量机和BP神经网络都可以用来做非线性回归拟合，但它们的原理是不相同的，支持向量机基于结构风险最小化理论，普遍认为其泛化能力要比神经网络的强。大量仿真证实，支持向量机的泛化能力强于BP网络，而且能避免神经网络的固有缺陷——训练结果不稳定。本源码可以用于线性回归、非线性回归、非线性函数拟合、数据建模、预测、分类等多种应用场合(Support vector machines and BP neural network can be used for non-linear regression fit, but they is not the same principle, support vector machine based on structural risk minimization theory, generally considered its generalization ability than the neural network of strong . Inherent defect- a large number of simulation confirmed that generalization ability of support vector machine is stronger than the BP network, but also to avoid neural network training results unstable. The source code can be used for linear regression, nonlinear regression, nonlinear function fitting a variety of applications, data modeling, forecasting, classification)