I have a conceptual question related to fuzzy logic method. I have a fuzzy environment and planning to use a fuzzy logic method to define/develop a productivity measure. However, I’m confused about the relation between the sample size (with predefined range for each input/feature which will limit the inputs range) in the data and fuzzy logic method parameters such as membership function etc. To me it seems there is no relationship, if you have one observation or 1000, you can define the Fuzzy classification of input and output variables/levels (with predefined range which the range is not coming from the data) and the functions, no matter how many observations are available. In that case, the surface viewer is something that is identified by the rules only and not available data (no matter if there is one observation or 1000 ones) and the hypothetical values of a use case that you are identifying.
Am I right or I'm missing something? Thank you.
P.S. I have attached a surface viewer example.
Dear Amir,
Hope following link will help you.
https://www.researchgate.net/post/Which_criterion_do_we_choose_for_membership_functions_in_fuzzy_logic
Thank you, Brajesh.
I can understand the input data may identify the membership function type such as Gaussian, Triangle etc. but since the level of inputs are predefined, I don't believe the membership function type play an important role on the output, do I miss anything?
Also, I don't get the role of number of observation yet and its possible impact on the method.
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