Impact of fuzzy normal forms on knowledge representation

I. Turksen
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引用次数: 1

Abstract

Fuzzy normal forms can be generated with an application of "Normal Form Generation Algorithm" on fuzzy truth tables. This takes place at the third level of knowledge representation, i.e., propositional level. It is shown that at least three distinct sets of normal forms can be generated depending on the axioms one is willing to impose on the propositional fuzzy set and logic theories. All are conjunctive-disjunctive and complement based De Morgan logics with the following three classes of axioms that identify each general class of fuzzy normal forms in order of least to most restrictive set of axioms in the following sense: 1) boundary and monotonicity; 2) boundary, monotonicity, associativity and commutativity; and 3) boundary, monotonicity, associativity, commutativity and idempotency.<>
模糊范式对知识表示的影响
在模糊真值表上应用“范式生成算法”可以生成模糊范式。这发生在知识表示的第三个层次,即命题层次。证明了根据命题模糊集和逻辑理论所加的公理,至少可以生成三个不同的范式集合。它们都是基于合取-析取和补的De Morgan逻辑,具有以下三类公理,它们在以下意义上按照最小到最限制公理集的顺序识别每一类模糊范式:1)边界和单调性;2)边界性、单调性、结合性和交换性;3)边界性、单调性、结合性、交换性和幂等性。
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