power-system-reliability-evaluation

自己编写子空间拟合进行DOA估计，包括信号子空间拟合，噪声子空间拟合。malab程序，效果很好！( )

纳税风险评估方案及对策,2013西安交通大学数学建模模拟赛matlab代码(Tax risk assessment program and Countermeasures.)

function [ H ,S ] = HS( f ) UNTITLED2 Summary of this function goes here Detailed explanation goes here syms x1 x2 f1=diff(f,x1) f2=diff(f,x2) S= [f1 f2] H = [diff(f1,x1) diff(f1,x2) diff(f2,x1) diff(f2,x2)] end s0=subs(S,findsym(S),[0 0])(function [H, S] = HS (f) UNTITLED2 Summary of this function goes here Detailed explanation goes here syms x1 x2 f1 = diff (f, x1) f2 = diff (f, x2) S = [f1 f2] H = [diff (f1, x1) diff (f1, x2) diff (f2, x1) diff (f2, x2)] end s0 = subs (S, findsym (S), [0 0 ]))

LDPC编译码算法，研究编码效率、帧长与吞吐量的三维关系，且添加了无编码是的比较关系图。(LDPC encoding and decoding algorithms, the study coding efficiency, the three-dimensional relationship of the frame size and throughput, and add non-coding comparison diagram.)

先进PID控制及其MATLAB仿真（教材+相关程序），本书从 MATLAB 仿真角度系统地介绍了 PID 控制的基本理论、基本方法和应用技术，是作者多年来从事控制系统教学和科研工作的结晶，同时融入了国内外同行近年来所取得的新成果。(Advanced PID control and MATLAB)

he m-file helps you o understand the generatio,transmission and reception of an ofdm signal, and also shows the effect of the channel noise and the HPA on the ofdm signal.

卡尔曼平滑，包括固定区间平滑，固定点平滑和之后平滑(Kalman smoothing, including fixed-interval smoother, fixed-point smoothing and after smoothing)

在matlab环境下，实现加密解密算法中的DES算法的源代码，以及GUI界面的生成(In the matlab environment, the realization of encryption decryption algorithm of DES algorithm source code, and the generation of GUI interface)

提供了一个扩展卡尔曼滤波源程序，无迹卡尔曼滤波源程序，交互式模型卡尔曼滤波源程序，扩展卡尔曼滤波及无迹卡尔曼滤波对比源程序及一个扩展卡尔曼滤波的目标跟踪算法源程序。(An extended kalman filter source program, no trace kalman filter source program, interactive model kalman filter source program, extended kalman filter and non trace kalman filter contrast source program and an extended kalman filter target tracking algorithm source program are provided.)

说明：  该算法采用如下共聚类结构进行多重聚类： 1.优化地将数据集划分为多个视图。在每个视图中，对象和特征都是群集的。 2.同时分析了三类特征：高斯特征、泊松特征和范畴特征。 3.自动推断视图和簇的数量。(This algorithm carries out multiple-clustering with co-clustering structures as follows: 1.Optimally divides datasets into several views. In each view, both objects and features are clustered. 2.Simultaneously analyzes three types of features: Gaussian, Poisson and categorical features. 3.Automatically infers the number of views and clusters.)