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

Active Learning Method (ALM) is a soft computing method which is used for modeling and controlling based on fuzzy logic. Operators in Fuzzy sets have to satisfy fuzzy S-norm and T-norm. But ALM couldn't satisfy these conditions, so instead of its powerfulness ALM hasn't any analytical expression. This paper introduces two new perators based on morphology which satisfy these conditions: First, they are fuzzy S-norm and T-norm. Second, they satisfy Demorgans law, so they complement each other perfectly. The acts of these operators are considered via three viewpoints: Mathematics, Geometry and fuzzy logic. Key-words: Active Learning Method; Ink Drop Spread; Hit or Miss Transform; Fuzzy connectives and aggregation operators; Fuzzy inference systems