用于主动学习方法的新S-范数和T-范数算子
New S-norm and T-norm Operators for Active Learning Method
摘要：
主动学习方法是一种基于模糊逻辑的建模与控制软计算方法。所有定义为模糊集的算子必须是模糊S-范数或模糊T-范数。尽管是一种强大的建模方法，但ALM不具有充当S-范数和T-范数的算子，这剥夺了它深刻的分析表达/形式。本文提出了两种新的基于形态学的算子，它们满足以下条件：第一，它们是模糊S-范数和模糊T-范数。第二，它们满足Demorgans定律，因此它们完美地互补。本文从数学、几何和模糊逻辑三个角度对这些算子进行了研究。
英文摘要：
Active Learning Method (ALM) is a soft computing method used for modeling and control based on fuzzy logic. All operators defined for fuzzy sets must serve as either fuzzy S-norm or fuzzy T-norm. Despite being a powerful modeling method, ALM does not possess operators which serve as S-norms and T-norms which deprive it of a profound analytical expression/form. This paper introduces two new operators based on morphology which satisfy the following conditions: First, they serve as fuzzy S-norm and T-norm. Second, they satisfy Demorgans law, so they complement each other perfectly. These operators are investigated via three viewpoints: Mathematics, Geometry and fuzzy logic.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
