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:
