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Computer Science > Artificial Intelligence

arXiv:1010.4561 (cs)
[Submitted on 21 Oct 2010 (v1), last revised 7 Feb 2011 (this version, v2)]

Title:New S-norm and T-norm Operators for Active Learning Method

Authors:Ali Akbar Kiaei, Saeed Bagheri Shouraki, Seyed Hossein Khasteh, Mahmoud Khademi, Ali Reza Ghatreh Samani
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Abstract: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.
Comments: 11 pages, 20 figures, under review of SPRINGER (Fuzzy Optimization and Decision Making)
Subjects: Artificial Intelligence (cs.AI)
ACM classes: I.5.1; F.4.1; H.2.1
Cite as: arXiv:1010.4561 [cs.AI]
  (or arXiv:1010.4561v2 [cs.AI] for this version)
  https://doi.org/10.48550/arXiv.1010.4561

Submission history

From: Ali Akbar Kiaei Khoshroudbari [view email]
[v1] Thu, 21 Oct 2010 19:48:22 UTC (296 KB)
[v2] Mon, 7 Feb 2011 00:59:29 UTC (854 KB)
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Ali Akbar Kiaei
Saeed Bagheri Shouraki
Seyed Hossein Khasteh
Mahmoud Khademi
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