Reseña

Background: Decision Support Systems (DSSs) are solutions that serve to decision-makers in their decision-making process. All DSS comprises four standard components: information, model, knowledge and user interface management sections. Fuzzy set theory provides the tools to effectively represent linguistic concepts, variables, and rules, becoming a natural model to represent human expert knowledge. One of the most fruitful developments of fuzzy set theory is Fuzzy Rule-Base Systems – FRBs. Exist two types of approach namely Mamdani and Takagi-Sugeno types. The Mamdani-type fuzzy model consists of four components: Fuzzification, knowledge base, inference engine, and defuzzification. For the development of these intelligent systems two primary components are needed: the knowledge database and the knowledge rule base. Mamdani type fuzzy logic does not have an algorithm to "learn" from the data their Knowledge components (Database and rule base).