fuzzy-kNN algorithme for fault detection

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

Answer 1

[Shlok]

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:

• 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.

To know more about the functions mentioned above, refer to the following MathWorks documentation links:

• pdist2: https://www.mathworks.com/help/stats/pdist2.html
• knnsearch: https://www.mathworks.com/help/stats/knnsearch.html