OLSest

Image enhancement code using matlab algorithm. Very good performance.

包括ofdm调制解调， LS信道估计，DFT信道估计，改进DFT信道估计(Including ofdm modem, LS channel estimation, DFT channel estimation to improve channel estimation DFT)

利用傅里叶变换的方法，求含有噪声的两个序列的互相关函数(a method of getting cross correlation function)

FuzzyToolV32 matlab software

power_zener.rar,功率电子领域matlab仿真文件，已经验证过，程序运行正常(power_zener.rar,Power electronics field matlab simulation file, has already been verified, the normal operating procedures)

基于卡尔曼滤波时间同步算法,可以显示同步前和同步后的差别(Based on kalman Filter time synchronation)

说明：  用matlab处理excel文件，查找一列是否含有所需要的字符串，有的话就提取出来，然后并显示(Deal with matlab excel file, find a string whether it contains the required, and some words to be extracted, and then and show)

Learning_random.m : 随机选择样例，从（90）pool里随机选择样本，删除版本空间树的数量 activeLearning.m:根据最大熵原则,从pool里选择样本，删除版本空间树的数量 getlabel.m:根据树和测试样例，得到根据该树得到的类标 getTrees.m:从提供的大量树结构(随机生成的)中，随机挑选一定数量的树(如果该树的叶子节点无类标随机添加) RandomCreateTree_d_unbalance:根据样本点割点中的非平衡割点建造树， RandomCreateTree_d_all.m:根据所有样本点的割点建造树 randomdata.m：给定属性取值，造数据 randomselect.m:从数据中随机选择一部分作为 showTree.m:显示树的结构 test.m:给出树，测试样例，得到正确率(Learning_random.m: randomly selected sample, randomly selected sample from (90) pool the The deleted version space tree quantity activeLearning.m: selecting a sample from the pool based on the principle of maximum entropy, delete the number of version space tree getlabel.m: According to the tree and the test sample obtained according to the class standard getTrees.m the tree: from the tree structure (randomly generated), randomly selected a certain number of trees (the leaves of the tree node class marked randomly adding ) RandomCreateTree_d_unbalance: According to the sample point cut point unbalanced cut point construction tree, RandomCreateTree_d_all.m: construction of the tree randomdata.m all sample points cut point: given the value of the property, manufacturing data randomselect.m: random data Select as part showTree.m: tree structure test.m: tree, the test sample is given to get the correct rate)

Objective of this example is to demonstrate how to design and model adaptive controller, tune and analyse its performance using Simulink® . For this example we have used direct adaptive method called Model Reference Adaptive Controller (MRAC). There are three main elements of this model: Reference Model, Plant Model and Adaptive Controller. Each element and its working is explained in "Adaptive Controller Example.pdf", part of attached folder.

非线性滤波算法工具箱，包括EKF、UKF、PF、PMF和ITKF等估计算法。(Nonlinear filtering algorithm toolbox, including the EKF, UKF, PF, PMF and ITKF such estimation.)