Abstract
A fuzzy Bayesian approach for learning fuzzy rule-based models is proposed. The proposed approach consists in the preliminary designing of a fuzzy Bayesian network (learning model), the parameters of which correspond to the parameters of the fuzzy rule-based model being learned. When new data is received, the individual parameters of the fuzzy Bayesian network are changed. This allows to localize the variable parameters of the fuzzy rule-based model and to perform point correction of the fuzzy model without relearning it. An example of using the proposed approach for analyzing the impact of climate phenomena on the vulnerability of the natural environment is presented.
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Acknowledgments
This study was supported by the Russian Science Foundation (project no. 22-61-00096) and the Russian Foundation for Basic Research (project no. 20-07-00100).
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Borisov, V., Luferova, E., Luferov, V., Sukhanov, A. (2023). The Learning of Fuzzy Models Based on the Fuzzy Bayesian Approach. In: Kovalev, S., Sukhanov, A., Akperov, I., Ozdemir, S. (eds) Proceedings of the Sixth International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’22). IITI 2022. Lecture Notes in Networks and Systems, vol 566. Springer, Cham. https://doi.org/10.1007/978-3-031-19620-1_46
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