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用matlab实现lagrange和Newton插值方法(Achieved with matlab lagrange interpolation and Newton)

局部放电采集的数据应用小波函数去噪程序m文件(Partial discharge data collected by the application of wavelet denoising procedures document m)

再matlab下面实现信号的fft变换。 是fft函数。(fft)

多层次B样条曲面拟合，采用均匀的控制点实现曲面的光滑拟合。(Multi-level B-spline surface fitting, use of control points to achieve a uniform smooth surface fitting.)

基于欧拉原理，利用lingo，求解中国邮递员问题，简单实用。(solving Chinese postman problem .)

计算光子晶体波导的程序，使用matlab语言编写．(calculation of photonic crystal waveguide, the use of Matlab language.)

Matlab圆弧插补，只要给定起点与终点，半径，设点圆心方向以及顺逆时针方向即可实现插补(Matlab circular interpolation, given the starting point and end point, radius, set the direction of the point of the center as well as Shun counterclockwise to achieve interpolation)

pham的频域盲分离算法，可供参考，具有较好的适应性。(pham frequency domain blind separation algorithm available, has a better adaptability.)

说明：  采用栅格对机器人的工作空间进行划分，再利用优化算法对机器人路径优化，是采用智能算法求最优路径的一个经典问题。目前，采用蚁群算法在栅格地图上进行路径优化取得比较好的效果，而利用遗传算法在栅格地图上进行路径优化在算法显得更加难以实现。 利用遗传算法处理栅格地图的机器人路径规划的难点主要包括：1保证路径不间断，2保证路径不穿过障碍。 用遗传算法解决优化问题时的步骤是固定的，就是种群初始化，选择，交叉，变异，适应度计算这样，那么下面我就说一下遗传算法求栅格地图中机器人路径规划在每个步骤的问题、难点以及解决办法。(It is a classical problem to divide the workspace of the robot by grids and optimize the path of the robot by using optimization algorithm. At present, the ant colony algorithm is used to optimize the path on the grid map, and the genetic algorithm is used to optimize the path on the grid map, which is more difficult to achieve. The difficulties of using genetic algorithm to deal with the path planning of robot on raster map mainly include: 1. guaranteeing that the path is uninterrupted, 2. guaranteeing that the path does not cross obstacles. The steps of genetic algorithm in solving optimization problems are fixed, that is, population initialization, selection, crossover, mutation, fitness calculation. Then I will talk about the problems, difficulties and solutions of genetic algorithm in each step of robot path planning in raster map.)

说明：  遗传算法的一些实现案例，包括函数优化，目标分配等问题(Some implementation cases of genetic algorithm, including function optimization, target assignment and so on)