This paper briefly reviews some of the different defuzzification methods reported in literature, using the base problem of feeding according to the protein and energy level. The defuzzification methods studied are: centroid, bisector, Largest of Maximum (LOM), Mean of Maximum (MOM) and Smallest of Maximum (SOM). In the problem under consideration, two input parameters —energy and protein, and one output parameter (feeding)—have been taken. In this work, four rules have been made, which decide the level of feed required. Ten observations for energy and protein levels are taken and the fuzzy rules are applied on them to find a solution. The fuzzy results obtained are then defuzzified using the five methods stated above. From the results obtained for the different observations and different defuzzification methods, it can be concluded that centroid, bisector and MOM give almost the same values. Therefore, there is more consistency in these results. On the other hand, there is a wide variation in the results obtained from LOM and SOM.

As the complexity of a system increases, it becomes more difficult and eventually impossible to make a precise statement about its behavior, and eventually, arriving at a point of complexity where the fuzzy logic method borne in humans is the only way to get at the solution to a problem. Fuzzy means ‘vagueness’.

In this work, we have taken the problem of finding the supplemental feeding recommendations on the basis of the energy and protein levels. Here, the energy and protein levels needed are the two input parameters and the amount of feed needed is the output variable. This problem does demonstrate the mechanics of developing a fuzzy expert system. We need to define fuzzy sets for the input parameters, energy and protein levels, and the output, feeding recommendation. In respect of the above problem, two fuzzy sets, i.e., low and high, are defined for each parameter. In the fuzzy approach, it is not necessary to define each possible level. Intermediate levels can have a membership of both the fuzzy sets.

In narrow sense, fuzzy logic is a logical system which is the extension of multivalued logic. In a wider sense, fuzzy logic is almost synonymous with the theory of fuzzy sets, a theory which relates to classes of object with unsharp boundaries in which membership is a matter of degree.

Computer Sciences Journal, Defuzzification Methods, Fuzzy Logic Method, Fuzzy Inference System, Bisector Methods, SOM Methods, Fuzzy Set, Fuzzy Expert System, Output Parameters, Input Parameters.