fcqj

在Matlab/Simulink构造一缓慢时变线性系统。试根据系统的输入生产数据分别用带遗忘因子最小二乘法和广义最小二乘法辨识系统的参数。(in Matlab/Simulink constructed a slow time-varying linear systems. Examination under the input production data were used to bring the forgotten factor method of least squares and generalized least squares method recognition system parameters.)

这是另外一种statcom仿真模型，通过滞环控制仿真(This is a STATCOM simulation model, the hysteresis control simulation)

this is a source code for blind multouser detectyion in mc cdma

颜色类型转换，GRB与HSI，并将H加30度(Color conversion, GRB with HSI)

Two functions, num2bin and bin2num are provided. num2bin takes a double and converts it to a binary string representation. bin2num is its inverse operation. It can be used in a numerical analysis course when floating point arithmetic is discussed. (Two functions, num2bin and bin2num are provided. Num2bin takes a double and converts it to a binary string representation. Bin2num is its inverse operation. It can be used in a numerical analysis course when floating point arithmetic is discussed.)

基于CMAC网络逼近非线性对象的程序，可以参考！希望能对大家有所帮助(based CMAC nonlinear approximation the procedure, we can make reference! The hope is to help everyone)

Robust Non-negative Dictionary Learning for Visual Tracking The provided codes could be either embedded into the benchmark framework of paper Online Object Tracking: A Benchmark (CVPR2013) (You can find details here: http://visual-tracking.net/) or run on individual sequence. To run the benchmark, just put the entire folder into the /trackers folder in the benchmark code base, and modify the configTrackers.m in util folder. DLT gets an AUC of 0.436, which ranks 5th among 26 in the benchmark by 19/03/2014. We don t tune parameters for single sequence in this case, all the parameters are stored in trackparam_DLT.m. To run on individual video, you need to modify the dataPath and title in run_individual.m. If you run MATLAB version after 2012, and have a CUDA compatible GPU installed, you may enjoy the fast computation speed by GPU, just set useGPU to true in trackparam_DLT.m and run_individual.m!

Image Watermarking using DWT algorithm

说明：  扩展卡尔曼滤波，simulink详细搭建模型(Extended Kalman filter and Simulink are used to build the model in detail)

用MATLAB编写的离散模糊控制程序，结合本程序，我相信能能更快更好的理解模糊控制算法。算法在MATLAB Version: 7.14.0.739 (R2012a)运行无误。 function [FCU_T1,FCU_T2,FCU_T3,FCU_T4]=fuzzy_table(Me,Mec,Mu,UC) Me 隶属度表1 Mec 隶属度表2 Mu 隶属度表3 UC 模糊规则 FCU_T1 重心加权法，输出精确值 FCU_T2 重心加权法，输出离散值 FCU_T3 重心加权法（对隶属度平方），输出离散值 FCU_T4 最大隶属度法 function [R,n,nE,nEC,nU,nfe,nfec,nfu]=fuzzy_relation(Me,Mec,Mu,UC) Me 隶属度表1 Mec 隶属度表2 Mu 隶属度表3 UC 模糊规则 R 表示全体规则构成的模糊关系 n=nfe×nfec nE 模糊变量E的语言值个数 nEC 表示模糊变量EC的语言值个数 nU 表示模糊变量U的语言值个数 nfe 表示E的等级量个数,示例1中其论域为{-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6} nfec 表示EC的等级量个数,示例1中其论域为{-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6} nfu 表示U的等级量个数,示例1中其论域为{-7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7} Me 表示E的隶属度表 Mec 表示EC的隶属度表 Mu表示U的隶属度表 UC表示规则表(fuzzy control algorithm（matlab program）)