ChaosToolbox

This project is on type of techniques used to enhance an image, detection of edges. In this project we have used neighborhood processing for the edge detection using convolution mask. And to increase the contrast of an image, we stretched each component of an rgb image.

这是“受控自回滑动平均模型”CARMA非线性系统的随机梯度辨识算法，用于参数估计以及模型建模。(This is a " controlled slide back the average model" CARMA nonlinear system stochastic gradient identification algorithm for parameter estimation and modeling.)

详细给出了线性规划的matlab的实例，简单易懂，便于学习。(Details are given planning matlab examples, easy to understand, easy to learn.)

fuzzy logic controller design

这是一个无线传感器网络的Rage-Free的定位算法，用matlab进行仿真实现！方便初学者参考使用！(This is a wireless sensor network, Rage-Free positioning algorithm using matlab simulation to achieve! Convenient reference for beginners to use!)

说明：  这是一个针对卷积码的维特比译码器.开发环境为MATLAB(This is a viterbi_decoder which can decode convolution codes.)

说明：  3个经典FDTD程序Taflove书随书光盘源代码(3 classic FDTD program Taflove book CD with the book source code)

matlab 但是防守对方似懂非懂是发生的发生地(matlab but the defense is the other occurrence took place Sidongfeidong)

A three phase induction motor drive using a V/F control

Algorithm Given are P training pairs {X1,d1,X2,d2....Xp,dp}, where Xi is (n*1) di is (n*1) No of Categories=R. i=1,2,...P Yi= Augmented input pattern( obtained by appending 1 to the input vector) i=1,2,…P In the following, k denotes the training step and p denotes the step counter within the training cycle Step 1: c>0 , Emin is chosen, Step 2: Weights are initialized at w at small values, w is (n+1)*1. Counters and error are initialized. k=1,p=1,E=0 Step 3: The training cycle begins here. Input is presented and output computed: Y=Yp, d=dp Oi=f(wtY) for i=1,2,….R Step 4: Weights are updated: wi=wi+1/2c(di-oi)Y for i=1,2,…..R. Step 5: Cycle error is computed: E=1/2(di-oi)2+E for i=1,2,…..R. Step 6: If p<P then p=p+1,k=k+1, and go to Step 3: Otherwise go to Step 7. Step 7: The training cycle is completed. For E=0,terminate the training session. Outputs weights and k. If E>0,then E=0 ,p=1, and enter the new training cycle by going to step 3.