LTE_channel_estimator

说明：  采用近邻聚类方法进行聚类，给出了仿真的粒子，运行通过。(The neighbor clustering methods clustering, and give out the simulative particle, run through )

AM modulation using matlab is provided within this rar file

基于导频的OFDM系统的信道估计，包括各种算法的仿真(OFDM systems based on pilot channel estimation, including simulation of various algorithms)

Induction machine stator fault

加权最小二乘迭代求解多参数估值。用于求解平差问题及其他领域优化问题。(The weighted least-squares iterative solution of multi-parameter estimates. Adjustment for solving optimization problems and other problem areas.)

EKF-SOC估算，用于电池的soc估算(EKF-SOC estimation for battery SOC estimation)

calculating signal to noise ratio

说明：  无约束一维极值问题中的抛物线和三次插值法，例程可调(Unconstrained extremal problem in one-dimensional parabolic and cubic interpolation method, routine adjustable)

说明：  卡尔曼滤波，matlab代码 能够直接跑(Kalman filter, matlab code can be run directly)

说明：  决策树(Decision Tree）是在已知各种情况发生概率的基础上，通过构成决策树来求取净现值的期望值大于等于零的概率，评价项目风险，判断其可行性的决策分析方法，是直观运用概率分析的一种图解法。由于这种决策分支画成图形很像一棵树的枝干，故称决策树。在机器学习中，决策树是一个预测模型，他代表的是对象属性与对象值之间的一种映射关系。Entropy = 系统的凌乱程度，使用算法ID3, C4.5和C5.0生成树算法使用熵。这一度量是基于信息学理论中熵的概念。 决策树是一种树形结构，其中每个内部节点表示一个属性上的测试，每个分支代表一个测试输出，每个叶节点代表一种类别。 分类树（决策树）是一种十分常用的分类方法。它是一种监督学习，所谓监督学习就是给定一堆样本，每个样本都有一组属性和一个类别，这些类别是事先确定的，那么通过学习得到一个分类器，这个分类器能够对新出现的对象给出正确的分类。这样的机器学习就被称之为监督学习。(Decision tree is a decision analysis method based on the known probability of occurrence of various situations, which can calculate the probability that the expected value of net present value is greater than or equal to zero, evaluate the project risk and judge its feasibility by constructing a decision tree. It is a graphic method of intuitively using probability analysis. Because this kind of decision branch is drawn as a graph, it is very similar to the branch of a tree, so it is called decision tree. In machine learning, decision tree is a prediction model, which represents a mapping relationship between object attributes and object values. Entropy = the disorder degree of the system, using algorithms ID3, C4.5 and C5.0, spanning tree algorithm using entropy. This measure is based on the concept of entropy in information theory.)