Essentials of fuzzy modeling and control
Essentials of fuzzy modeling and control
Fuzzy model identification: selected approaches
Fuzzy model identification: selected approaches
Rapid prototyping of fuzzy models based on hierarchical clustering
Fuzzy model identification
Probabilistic-fuzzy inference procedures for knowledge-based systems
MACMESE'08 Proceedings of the 10th WSEAS international conference on Mathematical and computational methods in science and engineering
Fuzzy covering problem based on the expert valuations
MMACTEE'09 Proceedings of the 11th WSEAS international conference on Mathematical methods and computational techniques in electrical engineering
Probabilistic: fuzzy knowledge bases for diagnostic systems
AIASABEBI'11 Proceedings of the 11th WSEAS international conference on Applied informatics and communications, and Proceedings of the 4th WSEAS International conference on Biomedical electronics and biomedical informatics, and Proceedings of the international conference on Computational engineering in systems applications
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Using notions of a linguistic variable and a probability of fuzzy events we formulate the joint probability of the events occurring in antecedents and consequences of linguistic fuzzy models. The structure of the model is given at the beginning of the task. Probabilities of fuzzy events state the rule weights. The rule weights have been taken into account in the proposed inference procedure. An example shows that a probability of fuzzy events is a crisp, numeric validation of the probability of linguistic statements describing technological situations.