xiandainibianjishu-liufengjun

把结构动力学中的数值计算用matlab编程，方便大家使用（主要有Houbolt法、Duhamel法、WilsonTheta法等几种）(The structural dynamics of the numerical calculation with matlab programming to facilitate the use of (mainly Houbolt method, Duhamel method, WilsonTheta several law))

这是关于基2FFT-DIT的程序 第一个压缩文件是matlab程序，并附有原理和流程图及总结 第二个是关于c的程序(This is based on the procedures for 2FFT-DIT is the matlab program a compressed file, together with principles and the second is the flow chart and summary of the procedures on c)

matlab中有关调制识别的程序说明，对仿真识别很有帮助(In MATLAB on modulation recognition program, very helpful for simulation and identification)

由zernike多项式通解求各项具体表达式。(Solutions by zernike polynomial through the specific expression.)

帮助实现N阶自适应滤波器的滤波原理，通过读入一组信号并对其进行去噪的处理，通过频谱图看出其良好的滤波效果从而发现N阶自适应滤波器的优点(N-order adaptive filter to help achieve the principle of filtering, by reading into a signal and its de-noising processing, through the spectrum to see their good filtering effect to discover the advantages of adaptive filter of order N)

37回归问题的数据集包，这是个jar包，用于weka数据挖掘平台(37 regression data sets package, which is a jar package for weka data mining platform)

半光滑牛顿法semi-smooth Newton method(semi-smooth Newton method)

MIDACO是一般的优化问题求解器。 MIDACO可应用于连续（NLP），离散/整数（IP）和混合整数（MINLP）的问题。问题可能被限制在平等和/或不等式约束。 MIDACO适合多达数百至几千优化变量的问题。 MIDACO实现了一个自由衍生物，启发式算法的处理方法处理的问题，因为黑盒可含有关键功能特性，如非凸性，不连续或随机噪声。(MIDACO is a solver for general optimization problems. MIDACO can be applied to continuous (NLP), discrete/integer (IP) and mixed integer (MINLP) problems. Problems may be restricted to equality and/or inequality constraints. MIDACO is suitable for problems with up to several hundreds to some thousands of optimization variables. MIDACO implements a derivative-free, heuristic algorithm that treats the problem as black-box which may contain critical function properties such as non-convexity, discontinuities or stochastic noise.)

Hough transform to detect lines

这个函数用来通过蒙特卡罗求积分， f 要求的函数； fail1 积分下限 fail2 积分上限 a，b 积分区域内x的积分上下限 c,d 积分区域内y的最大和最小值,c,d不是必须的参数，但是有之计算速度更快 n 求解迭代次数( This function is used to find the integral Monte Carlo, function f requirements fail1 integral limit fail2 integral upper limit a, regional integration within b limit c on x points, d- the maximum and minimum value of the integration region y, c, d is not required parameters, but the calculation speed is faster n Iteration number)