- Find the k nearest neighbors using MATLAB’s "pdist2()" or "knnsearch()".
- Compute weights by giving higher importance to closer neighbours and lower importance to distant ones using a fuzzification factor.
- Instead of assigning a single class, calculate how much the sample belongs to each class based on the neighbours' weights.
- Decide the class with the highest membership value or retain the fuzzy values for further analysis.
fuzzy-kNN algorithme for fault detection
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Hello everyone,
while thanking chaucn, I come back with a concern about the fuzzy-kNN algorithm
indeed, I want to detect faults in my system using fuzzy-knn, with your help, I got the knn script and fuzzy logic.
now i want to combine the two to improve the accuracy of my work. but really, I don't quite understand how this fuzzy-kNN algorithm works.
please, can someone explain to me how it works?
Thank you and see you soon
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Answers (1)
Shlok
on 28 Feb 2025 at 4:53
Hi Merlin,
The fuzzy-kNN algorithm combines traditional k-nearest neighbors classification with fuzzy logic principles. Unlike regular kNN where points are assigned to classes with absolute certainty, fuzzy-kNN provides membership degrees between 0 and 1 for each class, making it particularly suitable for fault detection where boundaries between normal operation and faults can be ambiguous.
To implement a fuzzy-kNN algorithm, follow the following steps:
To know more about the functions mentioned above, refer to the following MathWorks documentation links:
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