There are different MF, like triangular, exponential etc.. The MF assigns a value which will vary between 0 to 1. If a particular item to be classified has a MF value '0' it means that the item does not belong to that class, if it has a value '1', it means that it completely belongs to that class. And if the membership function has value in between these discrete values, it shows its degree of belongingness to that class. And one more point to remember is that if you are having say three classes then the sum of MF value of a particular item in all these 3 classes must be '1'.

hope this helps.

For Saravanan Vijayakumar:

you better study the following materials:

Zadeh, LA, 1975. The concept of a linguistic variable and its application to approximate reasoning.

Part I: Inf. Sci. 8, 199-249;

Part II: Inf. Sci. 8, 301-357;

Part III: Inf. Sci. 9, 43-80.

There are so many examples with graphics MF.

(sorry for my english)

To Kees Pieters:

Thanks for the explanation. I understood the concept but when applying it practically say writing code.. how do i derive the values u mentioned like 0.2*cold... etc.., is it from fuzzy rules or ??//?? kindly can you explain it little more... thanks in advance...

To Kees Pieters:

Thank you very much for spending your time.. i understood your explanation ...once again thank you:-)

Article Fuzzy SVM with a new fuzzy membership function

https://www.tutorialspoint.com/fuzzy_logic/fuzzy_logic_membership_function.htm

http://researchhubs.com/post/engineering/fuzzy-system/fuzzy-membership-function.html

http://www.ie.boun.edu.tr/~taner/publications/papers/membership.pdf