Information · 2019 · 159 citations · 54 references
Fuzzy Inference SystemsComplex Fuzzy SetsFuzzy Multi-criteria Decision-makingFuzzy LogicFuzzy SystemsEngineeringFuzzy ComputingFuzzy MathematicsFuzzy Expert SystemImaginary PartFuzzy OptimizationComputing With WordsQth PowerTheir ApplicationsFuzzy Pattern RecognitionOperations Research
Cq‑ROFSs provide a powerful way to express uncertain information, outperforming complex intuitionistic and complex Pythagorean fuzzy sets. The study proposes complex q‑rung orthopair fuzzy sets and develops new aggregation operators and a method for multi‑attribute group decision making. The authors define Cq‑ROFSs by constraining the qth‑power sums of membership and non‑membership, then introduce score, accuracy, and comparison functions, and propose Cq‑ROFWA and Cq‑ROFWG aggregation operators. Practical examples demonstrate the proposed method’s validity and superiority over existing approaches.
In this manuscript, the notions of q-rung orthopair fuzzy sets (q-ROFSs) and complex fuzzy sets (CFSs) are combined is to propose the complex q-rung orthopair fuzzy sets (Cq-ROFSs) and their fundamental laws. The Cq-ROFSs are an important way to express uncertain information, and they are superior to the complex intuitionistic fuzzy sets and the complex Pythagorean fuzzy sets. Their eminent characteristic is that the sum of the qth power of the real part (similarly for imaginary part) of complex-valued membership degree and the qth power of the real part (similarly for imaginary part) of complex-valued non‐membership degree is equal to or less than 1, so the space of uncertain information they can describe is broader. Under these environments, we develop the score function, accuracy function and comparison method for two Cq-ROFNs. Based on Cq-ROFSs, some new aggregation operators are called complex q-rung orthopair fuzzy weighted averaging (Cq-ROFWA) and complex q-rung orthopair fuzzy weighted geometric (Cq-ROFWG) operators are investigated, and their properties are described. Further, based on proposed operators, we present a new method to deal with the multi‐attribute group decision making (MAGDM) problems under the environment of fuzzy set theory. Finally, we use some practical examples to illustrate the validity and superiority of the proposed method by comparing with other existing methods.
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L. A. Zadeh · Information and Control · 1965 · 64.9K citations
Krassimir Atanassov · Fuzzy Sets and Systems · 1986 · 15.8K citations