MATLAB-Transfer-Matrix-Method

simulink建立的卡尔曼滤波算法，运行没问题！(simulink_kalman and matlab)

Matlab编写的路标识别程序。能够对路标进行分类和识别，即使雾化，干扰严重，仍能较准确的判断。(Signs written in Matlab identification procedures. Able to classify and identify road signs, even atomization, serious interference, still a more accurate judgments.)

多种类型在一起,我不知道如何分类了,而且需要下些东西,所以没有分开来上传,希望站长通融下,程序已经够 5个了:> MATLAB:TD_SCDMA系统性能仿真,QAM调制,7-4编译码的实现,CAM算法仿真(simulink) VC:huffman编译码的实现文件压缩解压缩(7-4 codec realization, CAM simulation algorithm (simulink) VC: huffman codec realize the file compression decompression)

模拟退火粒子群算法在matlab中应用，很实用，很好用(Simulated annealing particle swarm algorithm in matlab applications, very practical, very good with)

程序中含有时程图，相图，分岔图与庞加莱图的的程序(bifurcation time history diagram)

说明：  this code is for Converting images to avi file in matlab

fir带通滤波器,可将不需要的频率成分的信号滤去(fir bandpass filter unwanted frequency components can be filtered signal)

谐波平衡，可以求解DUFFING方程等非线性动力学系统，并可以求解二阶微分方程(The harmonic balance can be solved by the nonlinear dynamical systems such as DUFFING equation, and can be solved by two order differential equations)

用MATLAB编写的递推最小二乘算法算法LMS算法,karlman算法(using MATLAB recursive least squares algorithm LMS algorithm, the algorithm karlman)

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.)