multi_user_dectect

能作为所有金融分析提供一目了然的效果，结合了移动平均和转势信号(Can serve as a glance of all financial analysis of the effect of a combination of moving average and the transfer potential signal)

二分法，Newton迭代法和弦截法求方程根，并比较迭代次数。内含各种迭代法及数值加速方法的说明和比较。(Dichotomy, Newton iteration method for the equation of the root chord section, and compare the number of iterations. Includes a variety of accelerated iterative method and numerical description of the method and comparison.)

基于卡尔曼滤波器的永磁同步电机的二阶电阻辨识(Kalman filter based on permanent magnet synchronous motor of the second-order Resistance Identification)

用最小平方反褶积算法来用c语言编程完成算法，解决反褶积问题(Deconvolution using least-squares algorithm to complete the algorithm with c language programming to address the problem of deconvolution)

这是一个经典的粒子滤波程序，供初学者学习。(This is a classical particle filter program, for beginners to learn.)

卡尔曼滤波的CA模型，具有详细的代码以及说明(the CA model of Kalman Filtering)

atken插值，利用插值可以实现曲线拟合，函数逼近，在轨道拟合，钟差拟合中都有很好的应用(ATKEN )

详细介绍了在MATLAB环境中，MAT文件的编辑与读写，通常使用load和save两个命令进行 MAT 文件的读和写(Described in detail in the MATLAB environment, MAT files editing and read and write, load and save commonly used two commands to read and write MAT files)

To find the largest fitness value and its location 寻找最大适应性及相应的位置！Population N=50；crossover bits=n/2 (half of bits of an individual) with random locations，mutation bits = 4 种群数 N=50，交换位数= n/2, 即个体位数的一半，且位置随机； 变异位数统一取为4； Nc=20，28，36，44，individuals for crossover(交换的个数)。Nm=1，5，10，15， individuals for mutation(变异的个数)。(To find the largest fitness value and its location to find the location of the maximum adaptability and the corresponding! Population N = 50 crossover bits = n/2 (half of bits of an individual) with random locations, mutation bits = 4 population size N = 50, exchange digits = n/2, ie half of the median individual, and random position variation is taken as unity median 4 Nc = 20,28,36,44, individuals for crossover (exchange number). Nm = 1,5,10,15, individuals for mutation (the number of variations.))

matlab环境下的Q学习算法代码,从收费网站下的，可以运行。(reinforcement learning code in MATLAB)