Classification approach ideas for error detection
2 次查看(过去 30 天)
显示 更早的评论
Hi,
I want to make "something" for data classification, for an error-detection mechanism. I have a set of input data (different error simulations, eg. heating a part of a machine and taking sensor data), eg. err1...err4. Each beeing a set of features (sensor data, 20 different points) across multiple measurment points, so a 20x400 vector for example. For each of those i have a set of output data, 3 sensors logging data, so 3x400. The sensors for output data are different than those for input.
The idea is that later, I want to heat one component up and see in those 3 sensors of the output, that I had heating of a component and not something else.
I'm kind of new to this and I still don't have a clear direction to follow.
I need something to be able to input all of the data or even 4 different classifications, and to tell me, ideally, the percentage of being in one of the 4 error mechanism.
So far I've read into Fuzzy classification and the Fuzzy Toolbox (there I have problems with entering this data, I don't think it is possible for this usecase). Next the MATLAB classification learner, but there I have only one response? Now I'm reading into SVMs... Any ideas on what I should read into, check out or try? I don't want to use neural networks, but other machiene learning should be fine.
Thank you.
0 个评论
回答(0 个)
另请参阅
类别
在 Help Center 和 File Exchange 中查找有关 Get Started with Statistics and Machine Learning Toolbox 的更多信息
产品
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!