MATLABstudy

刘金坤《先进 PID 控制及其 MATLAB 仿真》书上第二章源程序。供大家参考。(Liu Jinkun " Advanced PID Control and MATLAB simulation," the book' s second chapter source. For your reference.)

主要介绍了matlab软件中的画图语句，便于大家参考学习(Matlab software drawing statements for easy reference learning)

matlab数值计算的很多例程,包括了很多经典的算法,有配套的书,可以结合起来研读,值得研究,很多代码值得直接使用.(Matlab numerical calculation of many routines, including a number of classic algorithms, a matching, can be combined to read, it is worth studying, many worthy code used directly.)

deinterlace去隔行算法，将隔行序列变为逐行(deinterlacing algorithms deinterlace interlaced sequences into progressive)

描述多普勒效应的一个matlab程序，用于产生时域和频域波形(Doppler effect describes a matlab procedure, used to produce time-domain and frequency domain waveform)

说明：  根据个体三期的就业状态及分类出错的概率计算实际失业率(According to the individual' s employment status and classification of three errors in the probability of the actual unemployment rate)

this code realize the two dimention esprit algorithm

MATLAB GUI（Graphical User Interfaces- 图形用户界面）设计与制作(Designing of MATLAB GUI（Graphical User Interfaces）)

说明：  利用小波分析具有时频局部化特性和熵能对系统状态进行表征的特点，将小波分析和熵结合起来，定义了3种小波熵（小波能谱熵、小波时间熵、小波奇异熵），并给出其算法，揭示了这3种小波熵对系统故障表征的机理，对两种理论信号和基于PSCAD/EMTDC仿真的输电线路故障信号(Based on the characteristics of time-frequency localization of wavelet analysis and entropy energy to represent system state, three kinds of wavelet entropy (wavelet energy spectrum entropy, wavelet time entropy and wavelet singular entropy) are defined by combining wavelet analysis and entropy, and their algorithms are given. The mechanism of these three kinds of wavelet entropy for system fault characterization is revealed. The results show that the three kinds of wavelet entropy can be used to characterize system fault Road fault signal)

Smooths a matrix (with/without NaN s) via recursive moving average method and eliminates data gaps. (MOVING_AVERAGE(X,F) smooths the vector data X with a boxcar window of size 2F+1, i.e., by means of averaging each element with the F elements at his right and the F elements at his left. The extreme elements are also averaged but with less data, obviously. Leaving the edges intact. The method is really fast. MOVING_AVERAGE2(X,M,N) smooths the matrix X with a boxcar window of size (2M+1)x(2N+1), i.e., by means of averaging each element with its surrounding elements that fits in the mentioned box centered on it. This one is also really fast. The elements at the edges are averaged too, but the corners are left intact. NANMOVING_AVERAGE(X,F) or NANMOVING_AVERAGE(X,F,1) accept NaN s elements in the vector X the latter interpolates also those NaN s elements surrounded by numeric elements. NANMOVING_AVERAGE2(X,M,N) or NANMOVING_AVERAGE2(X,M,N,1) accept elements NaN s in the matrix X the latter interpolates also those NaN elements surrounded by numeric elements.)