Incomplete Dynamic Fuzzy Linguistic Reasoning Approach Based on Concept Lattice

Abstract In the real world, much fuzzy data is described by evaluative linguistic expressions, which typically exhibit dynamic and changing characteristics. To tackle the challenges of dynamic fuzzy knowledge acquisition and reasoning in uncertain environments, this paper proposes an incomplete dynamic fuzzy linguistic reasoning approach based on concept lattices. First, the dynamic fuzzy linguistic concept lattice is constructed based on dynamic fuzzy linguistic formal context, which can represent linguistic information more effectively in dynamic fuzzy environments. Second, to compensate for information loss, an incomplete dynamic fuzzy linguistic formal context completion algorithm involving two-pass completions is proposed. In addition, dynamic fuzzy linguistic rules are extracted using the finer relation of dynamic fuzzy linguistic concept lattices, which are utilized to construct a dynamic fuzzy linguistic rule base. On this basis, antecedent similarity degree of dynamic fuzzy linguistic rules is introduced, thereby an incomplete dynamic fuzzy linguistic reasoning approach is proposed for obtaining decision-making results. Finally, a practical example is used to verify the effectiveness and rationality of the proposed approach.