Files_Simulink

Speed sensorless control of 3-phase induction motor using MRAS speed estimato

model predictive control using matlab

matlab指令速查，集锦将matlab重要的程序加以综合(Quick matlab command, highlights the important program to be integrated matlab)

this file is about using matlab simulink.

利用幂法反幂法等进行程序运算,可以获得各种适用结果.(The use of anti-power-law power-law to carry out procedures such as computing, access to a variety of application of the results.)

Image morphology using matlab

基于阀值的自适应小波降噪matlab源代码，入门级程序，对新手很有参考价值(Adaptive threshold wavelet denoising Matlab)

针对瞬时混合盲信号分离的自适应算法进行调研和比较，对于LMS，RLS算法（包括基于普通梯度和自然梯度两种）进行仿真验证，并对一些未知图像的混合进行分离。(Blind signal separation for instantaneous mixture algorithm for adaptive research and comparison, for the LMS, RLS algorithms (including gradient and natural gradient based on common two) for simulation, and some unknown mixture of image separation.)

SVM分类的matlab源码实现。并提供一组数据进行仿真。(SVM classification matlab realization.)

In this demo, I use the EM algorithm with a Rauch-Tung-Striebel smoother and an M step, which I ve recently derived, to train a two-layer perceptron, so as to classify medical data (kindly provided by Steve Roberts and Will Penny from EE, Imperial College). The data and simulations are described in: Nando de Freitas, Mahesan Niranjan and Andrew Gee Nonlinear State Space Estimation with Neural Networks and the EM algorithm After downloading the file, type "tar -xf EMdemo.tar" to uncompress it. This creates the directory EMdemo containing the required m files. Go to this directory, load matlab5 and type "EMtremor". The figures will then show you the simulation results, including ROC curves, likelihood plots, decision boundaries with error bars, etc. WARNING: Do make sure that you monitor the log-likelihood and check that it is increasing. Due to numerical errors, it might show glitches for some data sets. (In this demo, I use the EM algorithm with a Rauch-Tung-Striebel smoother and an M step, which I ve recently derived, to train a two-layer perceptron, so as to classify medical data (kindly provided by Steve Roberts and Will Penny from EE, Imperial College). The data and simulations are described in: Nando de Freitas, Mahesan Niranjan and Andrew Gee Nonlinear State Space Estimation with Neural Networks and the EM algorithm After downloading the file, type "tar-xf EMdemo.tar" to uncompress it. This creates the directory EMdemo containing the required m files. Go to this directory, load matlab5 and type "EMtremor". The figures will then show you the simulation results, including ROC curves, likelihood plots, decision boundaries with error bars, etc. WARNING: Do make sure that you monitor the log-likelihood and check that it is increasing. Due to numerical errors, it might show glitches for some data sets. )