Abstract
Intuitionistic fuzzy entropy is an important concept to describe the uncertainty of intuitionistic fuzzy sets (IFSs). To fully measure the fuzziness of IFSs, this paper comprehensively considers the deviation between membership and non-membership and the influence of hesitation, constructs the general expression of intuitionistic fuzzy entropy based on special functions, and proves some of its major properties. Then, it is verified that some existing intuitionistic fuzzy entropies can be constructed by specific functions. Finally, based on a specific parametric intuitionistic fuzzy entropy, this paper applies it to evaluate the regional collaborative innovation capability, to verify the feasibility and practicability of the entropy. In addition, the effectiveness and practicability of this entropy in decision making are further illustrated by comparing it with other entropy measures.
Highlights
Since Zadeh put forward the concept of a fuzzy set in 1965, the fuzzy set theory has been widely applied to neural networks, medical diagnosis, and electronic communication, and has made great progress
The essence of intuitionistic fuzzy sets (IFSs) is to consider two aspects of fuzziness, so IFSs play an important role in multi-attribute decision making
The newly constructed intuitionistic fuzzy entropy is applied to the evaluation of regional collaborative innovation capability, and the feasibility and effectiveness of the entropy are proved, which provides a new approach for decision makers to make decisions accurately
Summary
Since Zadeh put forward the concept of a fuzzy set in 1965, the fuzzy set theory has been widely applied to neural networks, medical diagnosis, and electronic communication, and has made great progress. Zhou et al [26] constructed a new TOPSIS decisionmaking method based on the characteristics of trapezoidal IFSs. Liu et al [27] developed a new class of intuitionistic fuzzy entropy and proposed an improved multi-attribute decision-making method. Liu et al [28] defined a new hesitation intuitionistic fuzzy distance measurement method and constructed the corresponding TOPSIS decision-making method. Since the TOPSIS decision-making method involves determining the ideal positive and negative solutions and distance measures, it is important to set the evaluation criteria for multi-attribute index information when selecting alternatives. The newly constructed intuitionistic fuzzy entropy is applied to the evaluation of regional collaborative innovation capability, and the feasibility and effectiveness of the entropy are proved, which provides a new approach for decision makers to make decisions accurately.
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