Smooth data to get the best approximation.
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Hello everyone, I was wondering if anyone knows how I could process the input data in such a way as to obtain something similar to what I show in smooth data. The points marked in red and blue correspond to the change points when using the ischange tool
cI = ischange(YY,'MaxNumChanges',1);
scatter(XX(cI),YY(cI),'filled')
I include an attached data with the points shown in the graph being XX and YY the input data (the ones I intend to modify) and the XSmooth, YSmooth data the ones I intend to get.
I've tried with the methods include in the smoothdata function but i couldn't get the result i expected
methods = {'movmean','movmedian','gaussian','lowess','loess','rlowess','rloess','sgolay'};
采纳的回答
hosein Javan
2020-8-12
0 个投票
I used curve fitting app you can workaround and change settings to get your result.
6 个评论
Alejandro Fernández
2020-8-12
The result remains unapproached regardless of the parameter you use to calculate the spline.
Alejandro Fernández
2020-8-12
hosein Javan
2020-8-12
there is a sharp spike at x=120. you can apply curve fitting to each interval seperately that does not affect the whole interval. This surely is it.
hosein Javan
2020-8-12
another method I'm thinking that your signal is full of noise. instead of curve fitting you can refer to signal processing toolbox see if there is a tool for cleaning up and denoising signals. consult with a communication expert. they can help you with frequency analysis. hope that helped.
Alejandro Fernández
2020-8-14
hosein Javan
2020-8-14
Alajendro Fernández De nada.
更多回答（1 个）
Bruno Luong
2020-8-14
load('InputData_SmoothData.mat')
pp=BSFK(XX,YY); % FEX file
plot(XX,YY);
plot(XX,ppval(pp,XX),'r');
legend('raw data','BSFK fitting')
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