Nearest_Neighbour_Algorithm_for_Stock_Prices_Forec

说明：  摘 要：0IE 问题是一个典型的优化组合问题，现在有很多解决的方法。本文针对遗传算法求解0IE 问题进行了研究，对 选择、交叉和变异算子进行了算法设计，最后在_ C- N软件上进行编程实现。结果表明，遗传算法在求解0IE 问题时具有 结果准确、收敛速度快等特点。 关键词：遗传算法；0IE 问题；最优化问题；_ C- N 软(Abstract: 0IE problem is a typical combinatorial optimization, there are many solutions. In this paper, genetic algorithm 0IE conducted a study on the selection, crossover and mutation operators of the algorithm design, and finally _ C- N software for programming. The results indicate that the genetic algorithm in solving problems with 0IE accurate, fast convergence and so on. Key words: genetic algorithm 0IE problem optimization problems _ C- N Soft)

模糊C均值（FCM）算法工具箱，可以直接使用，基于MATLAB编写，呵呵(Fuzzy C Means (FCM) algorithm toolbox, can be used directly, based on MATLAB write, huh, huh)

matlab编的画线函数,可以画不同颜色的线,方便大家调用(matlab series of drawing a line function, you can draw lines of different colors, to facilitate call)

matlab adaptive probabilty

说明：  里面有近百个MATLAB的小程序，可供初学者参考，转自其他论坛，非本人原创(There are close to a hundred small MATLAB program for beginners and reference, carried in other forums, non-my original)

基于光通信系统仿真，发射端放大器，光纤，接收端放大器(Based optical communication system simulation )

有关雷达系统的仿真，大家一起观摩学习拉，多多指教(the radar system simulation, we learn together and pulling exhibitions)

给出了kalman、RLS、LMS三种自适应滤波器的仿真matlab程序，在ImeQHEw环境下调试，三种滤波器都采用了10次迭代，平均ObXIODw参数性能良好，将仿真值与理论值进行了比较。(Given the Kalman and RLS and LMS three adaptive filter matlab simulation procedures, in the ImeQHEw environment debugging, three kinds of filters using the 10 iterations, average ImeQHEwl parameters with good performance, simulation value was compared with the theoretical value..)

信号的相关性是信号本身很重要的性质，本程序就是求随机信号的自相关性。(Signal is the relevance of the signal itself is very important properties, this procedure is for the random signals between autocorrelation. )

线性相关 求逐月异常序列x(n,12)和y(n,12)(n是年)的相关系数r(24)，其中j=1～12是1～12个月的情形，13～22是冬、春、夏、秋、冬季逐月、春季逐月、夏季逐月、秋季逐月、逐月、年、冬半年逐月（NDJFMA）、夏半年逐月（MJJASO）的序列的情形。(Linearly related to demand monthly abnormal sequence x (n, 12) and y (n, 12) (n is the year) the correlation coefficient r (24), where j = 1 ~ 12 1 ~ 12 months is the case, 13 22 is a winter, spring, summer, autumn and winter month by month, month by month in spring, summer, month by month, the monthly fall, month by month, year, month by month in winter (NDJFMA), summer half year monthly (MJJASO) the sequence circumstances.)