Zmatlab

A MATLAB textbook. A good eBook for MATLAB user

说明：  基于MATLAB\SIMULINK的系统仿真技术与应用 薛定宇等著 出版社：清华大学 ISBN：7302053413 (MATLABSIMULINK Based on System Simulation Technology and Application Xue Dingyu waiting Publisher: Tsinghua University, ISBN: 7302053413)

说明：  风力发电系统中基于mt坐标系的发电机模块的建模仿真（matlab/simulink）(Mt-based wind power system generator module coordinate system modeling and simulation (matlab/simulink))

根据输入输出数据，进行ARMA定阶、辨识，可用于SISO的最小方差控制系统性能评价(According to the input and output data, ARMA order determination, identification, control systems can be used to minimize the variance of performance uation SISO)

通信原理误码率的仿真程序MATLAB中开发(Communication Theory BER simulation program developed in MATLAB)

运用matlab实现计算机声卡采集信号并进行时域分析，直接运行即可。(Data acquisition & time domain analysis)

pca算法的matlab实现。希望大家看看，有所帮助吧。(Face recognition program simple, can be used as a beginner, learn.Face recognition program simple, can be used as a beginner, learn.)

潮流计算程序，直角坐标系下的，能算ieee任意节点，附件中有节点的格式，只需将提供的标准节点转换为文件中的bus和connect即可(Flow calculation program, Cartesian coordinates, any node can count, annex formatted node, simply provide a standard node converts files and connect to bus)

小波包分析提取振动信号中的特征频率，模拟数据分析处理的过程，用于信号特征提取、信号消噪。( Wavelet packet analysis to extract vibration signal characteristic frequency, Analog data analysis processing, For feature extraction, signal de-noising.)

说明：  隐马尔科夫模型是关于时序的概率模型，描述由一个隐藏的马尔科夫链随机生成不可观测的状态随机序列，再由各个状态生成一个观测而产生观测序列的过程。隐藏的马尔科夫链随机生成的状态的序列，称为状态序列；每个状态生成一个观测，而由此产生的观测的随机序列，称为观测序列。马尔科夫链由初始概率分布、状态转移概率分布以及观测概率分布确定(The hidden Markov model is a probabilistic model for time series. It describes the process of randomly generating unobservable state random sequences from a hidden Markov chain, and then generating an observation by each state to produce an observation sequence. A sequence of randomly generated states of hidden Markov chains, called a sequence of states; each state produces an observation, and the resulting random sequence of observations is called an observation sequence. Markov chain is determined by initial probability distribution, state transition probability distribution and observation probability distribution)