Fuzzy Inference Full Implication Method Based on Single Valued Neutrosophic t-representable t-norm: Purposes, Strategies, and a Proof-of-Principle Study

Minxia Luo, Ziyang Sun, Donghui Xu, Lixian Wu
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Abstract

As a generalization of intuitionistic fuzzy sets, single-valued neutrosophic sets have certain advantages in solving indeterminate and inconsistent information. In this paper, we study the fuzzy inference full implication method based on single-valued neutrosophic t-representable t-norm. Firstly, single-valued neutrosophic fuzzy inference triple I principles for fuzzy modus ponens and fuzzy modus tollens are given. Then, single-valued neutrosophic R-type triple I solutions for FMP and FMT are given. Finally, the robustness of the full implication triple I method based on the left-continuous single-valued neutrosophic t-representable t-norm is investigated. As a special case of the main results, the sensitivity of full implication triple I solutions based on three special single-valued neutrosophic t-representable t-norms are given.
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基于单值中性可表示 t-norm 的模糊推理全隐含方法:目的、策略和原理验证研究
单值中性集作为直觉模糊集的广义化,在解决不确定和不一致信息方面具有一定的优势。本文研究了基于单值中性可表示 t-norm 的模糊推理全蕴涵方法。首先,给出了模糊模态和模糊模态的单值中性模糊推理三重 I 原则。然后,给出了 FMP 和 FMT 的单值中性 R 型三重 I 解。最后,研究了基于左连续单值中性 t 可表示 t 准则的全蕴涵三重 I 方法的稳健性。作为主要结果的一个特例,给出了基于三个特殊单值中性 t 可表示 t 准则的全蕴涵三重 I 解的敏感性。
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