neural-network-toolbox

对读入的图片做霍夫变换，识别出图片中的直线并输出。(Do the Hough Transform to the input image, that is to detect lines in the input image, and output those lines.)

It will the user to plot various graph with different x and y notations

本程序是移动协作定位算法的仿真程序，各种条件下的代码都有，可以直接运行。(This progrem is a mobile cooperative localization algorithm, various conditions have the code, can be directly run.)

vep的编写，很好的语音问题处理结果。希望对你有用(program the vep,it has a good effect.i wish it is helpful for you)

A data classifiction matlab script

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.)

This program proveides a solution by Ghassan Hamarneh for detection curves using ACTIVE CONTOUR MODELS

自适应噪声抵消程序,归一化的最小均方算法,适用于核磁共振数据(Adaptive noise cancellation procedure, normalized least mean square algorithm is applied to NMR data)

说明：  Matlab数理统计方面的图书源码，方差分析希望对您有用(some resource with matlab )

DDE服务器编写例程，实用DDEML类库实现，简单易用。(DDE server to prepare routines, practical implementation DDEML class, easy to use.)