MBAKA NZUBE EMMNAUEL

Fuzzy inference systems (FIS) are particularly suited for aggregating multiple data to feed multi-variables decision support systems. Moreover, wine quality is a complex concept that refers to the simultaneous achievement of optimal levels in many parameters, thus single wine attributes spatial data are not adequate to define wine suitability for a specific end use. The aim of this study was to design and implement a fuzzy inference system on wine quality prediction using physiochemical parameters from wine dataset. The proposed system adopted the conventional fuzzy inference system which consists of four major components which are: knowledge acquisition, knowledge base, fuzzy inference engine and a user interface. The dataset is fuzzified into variables that were used to develop rule for the classification of wine quality. The fuzzy inference system followed three transformation stages; fuzzification, rule based and defuzzification processes. The model was implemented using C#, programming language and MYSQL as the relational data base management. The model was developed on window Microsoft system