Code-nonlinear-equa

实际的场景，若电梯的最大载客量m=10，设电梯中已有的 客人服从0-10 之间的均匀分布，且电梯中的任意一人在任意一层下的 概率相同，若你在第三层需要乘电梯到第七层，电梯处在第一层，共 8 层。且在每一层等电梯到达他们的目的楼层的客人服从0-3 的均匀分 布此时我们对电梯的运行加一些限 制，即电梯中若有客人未达目的地，电梯不会改变运行方向，求直到 你到达第七层为止，，电梯需停次数的数学期望，并进行计算机模拟验 证。(The actual scene, if the maximum lift capacity m = 10, the elevator has been designed to obey the guests uniformly distributed between 0-10, and the elevator in any one at any level under the same probability, if you The third layer needs to take the elevator to the seventh floor, the elevator in the first layer, a total of 8 layers. And in each level to reach their goals such as the elevator floor guests subject to the uniform distribution of 0-3 at this time we add some restrictions on the operation of the elevator, the elevator if it has not reached the destination guests, the lift will not change the direction, request until you reach the seventh floor so far, the elevator number of mathematical expectation to be stopped, and the computer simulation.)

tf*idf algoritm is a famouse algoritm in text mining.

定时同步的文章，很值得看一看，算法简单，精度很高！(Timing of the article, well worth a look, a simple algorithm, high precision!)

matlab 一维小波变换，可以得到垂直、水平、对角线的对比图。(matlab one-dimensional wavelet transform, can be vertical, horizontal, diagonal comparison chart.)

这个文章系统详细的介绍了 非线性模型的一种确定P的阶数的方法(This article describes in detail the system as a nonlinear model to determine the order of method P)

一维的求根多重信号分类算法Root-MUSIC，在matlab环境下实现(One-dimensional root finding multiple signal classification algorithm Root-MUSIC, in the matlab environment to achieve)

svm的回归预测分析，包含源代码和数据，测试可用(svm regression analysis to predict, including source code and data, test available)

此文件实现了让语音信号通过低通，高通，带通滤波器使其变声，并且在GUI界面上绘出了个滤波器的频率响应和语音信号的频谱。(This file allows to achieve a voice signal through a low pass, high pass, band pass filter to make it change the sound, and plotted on a GUI interface and voice signal spectrum frequency response of a filter.)

note on Matlab: intro and description of toolbox and Simulink

协方差驱动随机子空间法的matlab算法，我下的。(Covariance-driven stochastic subspace method)