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

2010/10/21 by Ali Akbar Kiaei, Saeed Bagheri Shouraki, Kiaei, Ali Akbar +6
Computer Science · Engineering · #Fuzzy Logic and Control Systems #Advanced Control Systems Optimization #Neural Networks and Applications

paper · pdf · doi:10.48550/arxiv.1010.4561

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.

Citations

• Fuzzy identification of systems and its applications to modeling and control
• A Threshold Selection Method from Gray-Level Histograms
• Sequential Operations in Digital Picture Processing

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