matlab-zuixiaoerchengnihe

说明：  PFC电压模型,供大家参考使用,共同学习.(PFC voltage model for reference use, learning together.)

Matlab+YALMIP+CPLEX解决带储能的微电网优化调度问题，以微电网运行成本最小为目标函数，约束条件包含蓄电池SOC约束、交换功率约束、功率平衡等约束，本程序已在Matlab 2015b平台，yalmip version：20171121 CPLEX version：12.8教育版 验证成功(Matlab+YALMIP+CPLEX solves the optimization scheduling problem of microgrid with storage energy, taking the minimum operating cost of microgrid as the objective function. The constraints include the constraints of the battery SOC constraint, the switching power constraint, the power balance and so on. This program has been verified on the Matlab 2015b platform, yalmip version: 20171121 CPLEX version:12.8 Education edition.)

MATLAB图像处理中的实力应用 ，在文字识别，车牌识别中有较为广泛的应用，第2部分(MATLAB image processing in the strength of applications, in character recognition, license plate recognition to have a fairly wide range of applications, Part 2)

 利用fft()进行快速傅立叶变换，得到频谱，画出正弦函数及其频谱  找出频谱中使得Y轴取最大值时的估计频率记为ft  加入白噪声，绘出信噪比和均方误差的关系曲线 ( using fft () for fast Fourier transformation, spectrum, to draw the sine function and its spectrum  find the spectrum makes the Y-axis maximum value when the estimated frequency is denoted by ft  adding white noise, SNR plot and mean square error curve)

此算法是针对语音合成，采用时域的基音同步叠加算法，对波形进行韵律特征提取，修改，以及合成（包括短时能量分析，短时过零率分析等等算法）！算法是用matlab编写的(Speech synthesis, using time-domain synchronous Pitch stack algorithm, rhythm right waveform feature extraction, modification, and synthesis (including short-term energy analysis, Short-term zero rate algorithm analysis, and so on)! Algorithm is used to prepare the Matlab)

image rotate using hough transform to detect the edge of image.

二维绘图—MATLAB(有关matlab绘图的各种基本知识，实用)(2 d graphics- MATLAB)

在系统辨识中，关键的是系统建模这里提出了多种方法(In system identification, system modeling is the key to a variety of methods presented here)

将电力市场数据使用小波分解成为两个长度相等的低频和高频小波，然后可以用于灰色模型的建模和波形模型建模！(The electricity market data using wavelet decomposition into two equal length low and high frequency wavelet gray model can then be used for modeling and waveform modeling!)

对数据先进行经验模态分解得到不同特征尺度的信号分量，然后对所有的信号分量用AR模型进行预测得到预测后的分量，然后将预测后的分量进行重构得到最终的预测信号。(The data first empirical mode decomposition characteristics of the signal components of different scales, and then for all signal components obtained by AR model to predict the component forecasts, and then predict the components are reconstructed to obtain final predicted signal.)