Probabilistic-Fuzzy Knowledge-Based System for Managerial Applications

The paper deals with an inference system with probabilistic-fuzzy knowledge base as a tool which can help users in analyzing complete uncertainty of real problems in the company using fuzzy sets and probability. In the mentioned system, knowledge is saved in the weighted IF-THEN fuzzy rules, where the weights constitute marginal probabilities of the fuzzy events in the antecedents and conditional probabilities of the fuzzy events in the consequents. Moreover, the paper presents a proposal of the use of fuzzy association rules as a method of automatic knowledge base extraction in the inference system. For this purpose a modification of the PF-Growth algorithm is described. A numerical example is analyzed by using wind speed prediction process. The correct estimation of wind speed, as the potential energy resource, is necessary for control of the wind turbine work and it is important for the localization process of wind turbines, production planning and estimating cost-effectiveness of such investments.