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卡方检验，通过matlab输入理论频数，直接得出检验结果(Chi-square test, through matlab input frequency theory, the test results obtained directly)

rkf78 matlab算法 可解高阶常微分方程以及轨道积分等(rkf78 matlab order ordinary differential equation solution algorithm and the orbit integration, etc.)

基于支持向量回归机，预测股价（10天），可以根据自己的需要对参数和预测天数进行灵活调整(Based on support vector regression to predict the stock price (10 days), according to their needs and prediction of parameters for flexible adjustment of the number of days)

内含心电信号数据及运用MATLAB写的源代码，matlab开发工具箱中的支持向量机，从先验概率中采样，计算权重。( ECG data and includes source code written in MATLAB, matlab development toolbox support vector machine, Sampling a priori probability, calculate the weight.)

How the K-mean Cluster work Step 1. Begin with a decision the value of k = number of clusters Step 2. Put any initial partition that classifies the data into k clusters. You may assign the training samples randomly, or systematically as the following: Take the first k training sample as single-element clusters Assign each of the remaining (N-k) training sample to the cluster with the nearest centroid. After each assignment, recomputed the centroid of the gaining cluster. Step 3 . Take each sample in sequence and compute its distance from the centroid of each of the clusters. If a sample is not currently in the cluster with the closest centroid, switch this sample to that cluster and update the centroid of the cluster gaining the new sample and the cluster losing the sample. Step 4 . Repeat step 3 until convergence is achieved, that is until a pass through the training sample causes no new assignments. (How the K-mean Cluster workStep 1. Begin with a decision the value of k = number of clusters Step 2. Put any initial partition that classifies the data into k clusters. You may assign the training samples randomly, or systematically as the following: Take the first k training sample as single-element clusters Assign each of the remaining (Nk) training sample to the cluster with the nearest centroid. After each assignment, recomputed the centroid of the gaining cluster. Step 3. Take each sample in sequence and compute its distance from the centroid of each of the clusters. If a sample is not currently in the cluster with the closest centroid, switch this sample to that cluster and update the centroid of the cluster gaining the new sample and the cluster losing the sample. Step 4. Repeat step 3 until convergence is achieved, that is until a pass through the training sample causes no new assignments.)

本次上传的代码为双边带抑制载波的matlab算法(From this code for bilateral rejection carrier matlab algorithm)

Efficient signal processing techniques are necessary for assessing the exposure to broadband magnetic fields. The use of flat-top windows along with the DFT is a simple and fast method that reduces the characteristic leakage errors and picket fence effects when using the classical DFT for the exposure assessment.

在matlab中，基于GUI工具箱，实现了频谱分析仪的功能(GUI-based toolbox in matlab, the spectrum analyzer function)

利用自适应遗传算法求解多元函数的最值问题。(Most adaptive genetic algorithm for multi-function value.)

一副图可视为一个矩阵，将其分成上线两部分，然后用矩阵的合并实现两个图简单拼接。目的主要使新手熟悉matlab的这种图形处理方法。(A diagram can be regarded as a matrix, to be divided into on-line in two parts, and then use the matrix of the merger of two diagrams simple splicing. The main purpose of the novice familiar with Matlab this graphical approach.)