fft.matlab

说明：  自己编写的BP算法,里面有些参数可以自己修改(bp)

混沌优化程序，各种混沌优化的例子，时分混沌，chengs,logistic,duffing,henon,loren2.(chaos optimization procedures, various examples of chaos optimization, chaotic hours, chengs, logistic, Duffing, henon, loren2.)

在智能天线系统中的基于子空间法的移动目标的跟踪算法(in smart antenna system based on space law moving target tracking algorithm)

关于DTC的matlab例程，画得比较详细，很完整的阐述了算法(Simulink of DTC)

DDS(快速正交调制）生成正弦波形，利用相位累加字进行累加，查找查找表内容输出正弦数据，在通信领域应用很多，我采用的是matlab的simulink进行前期仿真(DDS (fast quadrature modulation) to generate sine wave, the use of the word to accumulate phase accumulation, content output sine lookup table lookup data in many applications in the communications field, I used to pre simulink of matlab simulation)

Lecture on Week number 4

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.)

Novatel v5接收机家族板卡介绍，测绘型(Novatel v5 receiver introduce the family board, mapping type)

simpson method used in matlab

粗糙集属性约简代码，可以运行，可以运用到不同场合，可作为参考(Rough set attribute reduction code can run, can be applied to different occasions, can be used as reference)