Abstract
Usual algorithms for fuzzy data processing—based on the usual form of Zadeh’s extension principle—implicitly assume that we use the \(\min \) “and”-operation (t-norm). It is known, however, that in many practical situations, other t-norms more adequately describe human reasoning. It is therefore desirable to extend the usual algorithms to situations when we use t-norms different from \(\min \). Such an extension is provided in this chapter.
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Acknowledgements
This work was supported in part by the National Science Foundation grants HRD-0734825 and HRD-1242122 (Cyber-ShARE Center of Excellence) and DUE-0926721, and by an award “UTEP and Prudential Actuarial Science Academy and Pipeline Initiative” from Prudential Foundation. We also acknowledge the partial support of the Center of Excellence in Econometrics, Faculty of Economics, Chiang Mai University, Thailand.
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Pownuk, A., Kreinovich, V., Sriboonchitta, S. (2018). Fuzzy Data Processing Beyond Min t-Norm. In: Berger-Vachon, C., Gil Lafuente, A., Kacprzyk, J., Kondratenko, Y., Merigó, J., Morabito, C. (eds) Complex Systems: Solutions and Challenges in Economics, Management and Engineering. Studies in Systems, Decision and Control, vol 125. Springer, Cham. https://doi.org/10.1007/978-3-319-69989-9_14
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DOI: https://doi.org/10.1007/978-3-319-69989-9_14
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