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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.)

用c++实现m次多项式的曲线拟合，改程序简单易懂，可读性强(it s easy)

开发环境没有可选项，fortran90,Fortran_90_and_Computational_Science-_codes(Computational Science codes by fortran90 )

edem接触程序，用于计算各种模型的接触力(edem modle)

Orthogonal Polynomials Approximation 数值分析，计算正交基多项式的系数 (Given a function f and a set of m >0 distinct points . You are supposed to write a function to approximate f by an orthogonal polynomial using the exact function values at the given m points with a weight assigned to each point . The total error must be no larger than a given tolerance. Format of function int OPA( double (*f)(double t), int m, double x[], double w[], double c[], double*eps ) where the function pointer double (*f)(double t) defines the function f int m is the number of points double x[] contains points double w[] contains the values of a weight function at the given points x[] double c[] contains the coefficients of the approximation polynomial double*eps is passed into the function as the tolerance for the error, and is supposed to be returned as the value of error. The function OPA is supposed to return the degree of the approximation polynomial. Note: a constant Max_n is defined so that if the total error is still not small enough when n = Ma)

用于FLUENT计算结构单自由度涡激振动，采用四阶龙哥库塔法，并设置了并行计算(FLUENT, VIV, Fourth order Runge-Kutta )

平常练习的一些程序，比较基础，包括求斐波那契数、完数等等(Some of the usual practice procedures, more infrastructure, including seeking Fibonacci numbers, number, etc. End)

采用数值分析课程中的插值逼近以及曲面拟合方法来估算复杂的函数表达式。(Interpolation approximations and surface fitting methods are used in the numerical analysis course to estimate complex function expressions.)

羊与狼的故事...主要实现狼进入若干羊圈后，寻找最后所有羊圈中剩余的狼和眼数目( the wolf and sheep)

LS_SVM 偏最小二乘结合支持向量机(The LS SVM partial least squares and Support Vector Machine)