Abstract
In this paper we present a new Fuzzy Implication Generator via Fuzzy Negations which was generated via conical sections, in combination with the well-known Fuzzy Conjunction T-norm = min. Among these implications we choose the most appropriate one, after comparing them with the empiristic implication, which was created with the help of real temperature and humidity data from the Hellenic Meteorological Service. The use of the empiristic implication is based on real data and also it reduces the volume of the data but without cancelling them. Finally, the pseudo-code, which was used in the programming part of the paper, uses the new Fuzzy Implication Generator and approaches the empiristic implication satisfactorily which is our final goal.
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Acknowledgements
We would like to thank the Hellenic National Meteorological Service for the quick reply to our request for the concession of the climatic data of the last five years, in order to be used in the present paper (see [4]).
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Makariadis, S., Souliotis, G., Papadopoulos, B.K. (2020). Application of Algorithmic Fuzzy Implications on Climatic Data. In: Iliadis, L., Angelov, P., Jayne, C., Pimenidis, E. (eds) Proceedings of the 21st EANN (Engineering Applications of Neural Networks) 2020 Conference. EANN 2020. Proceedings of the International Neural Networks Society, vol 2. Springer, Cham. https://doi.org/10.1007/978-3-030-48791-1_31
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DOI: https://doi.org/10.1007/978-3-030-48791-1_31
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