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摘要翻译：
主动学习方法(ALM)是一种基于模糊逻辑的软计算建模和控制方法。为模糊集定义的所有算子都必须是模糊S-范数或模糊T-范数。尽管ALM是一种强大的建模方法，但它不具备作为S-范数和T-范数的运算符，这使得它缺乏深刻的分析表达/形式。本文介绍了两种新的基于形态学的算子，它们满足以下条件：首先，它们是模糊S-范数和T-范数。二是二者符合消解规律，相辅相成。从数学、几何学和模糊逻辑三个角度对这些算子进行了研究。
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英文标题：
《New S-norm and T-norm Operators for Active Learning Method》
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作者：
Ali Akbar Kiaei, Saeed Bagheri Shouraki, Seyed Hossein Khasteh,
Mahmoud Khademi, and Ali Reza Ghatreh Samani
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最新提交年份：
2011
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分类信息：
一级分类：Computer Science 计算机科学
二级分类：Artificial Intelligence 人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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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.
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PDF链接：
https://arxiv.org/pdf/1010.4561
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