Information Volume of Fuzzy Membership Function

Fuzzy membership function plays an important role in fuzzy set theory. However, how to measure the information volume of fuzzy membership function is still an open issue. The existing methods to determine the uncertainty of fuzzy membership function only measure the first-order information volume, but do not take higher-order information volume into consideration. To address this issue, a new information volume of fuzzy membership function is presented in this paper, which includes the first-order and the higher-order information volume. By continuously separating the hesitancy degree until convergence, the information volume of the fuzzy membership function can be calculated. In addition, when the hesitancy degree of a fuzzy membership function equals to zero, the information volume of this special fuzzy membership function is identical to Shannon entropy. Two typical fuzzy sets, namely classic fuzzy sets and intuitiontistic fuzzy sets, are studied. Several examples are illustrated to show the efficiency of the proposed information volume of fuzzy membership function

Hospitality brand management by a score-based q-rung orthopair fuzzy V.I.K.O.R. method integrated with the best worst method

Hospitality brand management is a primary concern in the hotel industry and the evaluation of brands can be considered as a decision- making problem with multiple criteria. The evaluation information of brands may be uncertain sometimes. The q-rung orthopair fuzzy set (q-R.O.F.S.), which represents the preference degree of a person from the positive and negative aspects, has turned out to be an efficient tool in depicting uncertainty and vagueness in the decision-making process. This article dedicates to presenting an integrated multiple criteria decision-making method with q-R.O.F.S.. Firstly, a score function of the q-R.O.F.S. is proposed to solve the deficiencies of two existing score functions. Then, a weight-determining method based on the additive consistency of the preference relation is developed. A decision-making method integrating the score function, the best worst method and the VIsekriterijumska optimizacija I KOmpromisno Resenje (V.I.K.O.R.) which means multiple criteria compromise optimisation in English) method is further proposed. Finally, a case study regarding the hospitality brand management is provided to show the applicability and validity of the proposed method.The work was supported by the National Natural Science Foundation of China (71771156, 71971145), the Scholarship from China Scholarship Council (No. 201906240161) and the Deanship of Scientific Research (DSR) at King Abdulaziz University, Jeddah (No. RG-10-611- 39, No. RG-7-135-38)

Improved Knowledge Measures for q-Rung Orthopair Fuzzy Sets

The q-rung orthopair fuzzy set (qROFS) defined by Yager is a generalization of Atanassov intuitionistic fuzzy set (IFS) and Pythagorean fuzzy sets (PyFSs). In this paper, we define the knowledge measure for qROFS by using the cosine inverse function. The information precision and information content are two facets of knowledge measure. Both facets of knowledge measure are considered. The properties of knowledge measure and their graphical explanations are discussed. An application of the knowledge measure in multi-attribute group decision-making (MAGDM) problem under the confidence level approach is given. A numerical example of the selection of renewable energy sources is discussed

Hospitality brand management by a score-based q-rung ortho pair fuzzy V.I.K.O.R. method integrated with the best worst method

Hospitality brand management is a primary concern in the hotel industry and the evaluation of brands can be considered as a decision-making problem with multiple criteria. The evaluation information of brands may be uncertain sometimes. The q-rung orthopair fuzzy set (q-R.O.F.S.), which represents the preference degree of a person from the positive and negative aspects, has turned out to be an efficient tool in depicting uncertainty and vagueness in the decision-making process. This article dedicates to presenting an integrated multiple criteria decision-making method with q-R.O.F.S.. Firstly, a score function of the q-R.O.F.S. is proposed to solve the deficiencies of two existing score functions. Then, a weight-determining method based on the additive consistency of the preference relation is developed. A decision-making method integrating the score function, the best worst method and the VIsekriterijumska optimizacija I KOmpromisno Resenje (V.I.K.O.R.) which means multiple criteria compromise optimisation in English) method is further proposed. Finally, a case study regarding the hospitality brand management is provided to show the applicability and validity of the proposed method

Sequences of refinements of rough sets: logical and algebraic aspects

In this thesis, a generalization of the classical Rough set theory is developed considering the so-called sequences of orthopairs that we define as special sequences of rough sets. Mainly, our aim is to introduce some operations between sequences of orthopairs, and to discover how to generate them starting from the operations concerning standard rough sets. Also, we prove several representation theorems representing the class of finite centered Kleene algebras with the interpolation property, and some classes of finite residuated lattices (more precisely, we consider Nelson algebras, Nelson lattices, IUML-algebras and Kleene lattice with implication) as sequences of orthopairs. Moreover, as an application, we show that a sequence of orthopairs can be used to represent an examiner's opinion on a number of candidates applying for a job, and we show that opinions of two or more examiners can be combined using operations between sequences of orthopairs in order to get a final decision on each candidate. Finally, we provide the original modal logic SOn with semantics based on sequences of orthopairs, and we employ it to describe the knowledge of an agent that increases over time, as new information is provided. Modal logic Son is characterized by the sequences (□1,…, □n) and (O1,…, On) of n modal operators corresponding to a sequence (t1,…, tn) of consecutive times. Furthermore, the operator □i of (□1,…, □n) represents the knowledge of an agent at time ti, and it coincides with the necessity modal operator of S5 logic. On the other hand, the main innovative aspect of modal logic SOn is the presence of the sequence (O1,…, On), since Oi establishes whether an agent is interested in knowing a given fact at time ti

Sequences of refinements of rough sets: logical and algebraic aspects

In this thesis, a generalization of the classical Rough set theory is developed considering the so-called sequences of orthopairs that we define as special sequences of rough sets. Mainly, our aim is to introduce some operations between sequences of orthopairs, and to discover how to generate them starting from the operations concerning standard rough sets. Also, we prove several representation theorems representing the class of finite centered Kleene algebras with the interpolation property, and some classes of finite residuated lattices (more precisely, we consider Nelson algebras, Nelson lattices, IUML-algebras and Kleene lattice with implication) as sequences of orthopairs. Moreover, as an application, we show that a sequence of orthopairs can be used to represent an examiner's opinion on a number of candidates applying for a job, and we show that opinions of two or more examiners can be combined using operations between sequences of orthopairs in order to get a final decision on each candidate. Finally, we provide the original modal logic SOn with semantics based on sequences of orthopairs, and we employ it to describe the knowledge of an agent that increases over time, as new information is provided. Modal logic Son is characterized by the sequences (\u25a11,\u2026, \u25a1n) and (O1,\u2026, On) of n modal operators corresponding to a sequence (t1,\u2026, tn) of consecutive times. Furthermore, the operator \u25a1i of (\u25a11,\u2026, \u25a1n) represents the knowledge of an agent at time ti, and it coincides with the necessity modal operator of S5 logic. On the other hand, the main innovative aspect of modal logic SOn is the presence of the sequence (O1,\u2026, On), since Oi establishes whether an agent is interested in knowing a given fact at time ti

q-rung logarithmic Pythagorean neutrosophic vague normal aggregating operators and their applications in agricultural robotics

The article explores multiple attribute decision making problems through the use of the Pythagorean neutrosophic vague normal set (PyNVNS). The PyNVNS can be generalized to the Pythagorean neutrosophic interval valued normal set (PyNIVNS) and vague set. This study discusses $q$-rung log Pythagorean neutrosophic vague normal weighted averaging ($q$-rung log PyNVNWA), $q$-rung logarithmic Pythagorean neutrosophic vague normal weighted geometric ($q$-rung log PyNVNWG), $q$-rung log generalized Pythagorean neutrosophic vague normal weighted averaging ($q$-rung log GPyNVNWA), and $q$-rung log generalized Pythagorean neutrosophic vague normal weighted geometric ($q$-rung log GPyNVNWG) sets. The properties of $q$-rung log PyNVNSs are discussed based on algebraic operations. The field of agricultural robotics can be described as a fusion of computer science and machine tool technology. In addition to crop harvesting, other agricultural uses are weeding, aerial photography with seed planting, autonomous robot tractors and soil sterilization robots. This study entailed selecting five types of agricultural robotics at random. There are four types of criteria to consider when choosing a robotics system: robot controller features, cheap off-line programming software, safety codes and manufacturer experience and reputation. By comparing expert judgments with the criteria, this study narrows the options down to the most suitable one. Consequently, $q$ has a significant effect on the results of the models

Probability Transform Based on the Ordered Weighted Averaging and Entropy Difference

Dempster-Shafer evidence theory can handle imprecise and unknown information, which has attracted many people. In most cases, the mass function can be translated into the probability, which is useful to expand the applications of the D-S evidence theory. However, how to reasonably transfer the mass function to the probability distribution is still an open issue. Hence, the paper proposed a new probability transform method based on the ordered weighted averaging and entropy difference. The new method calculates weights by ordered weighted averaging, and adds entropy difference as one of the measurement indicators. Then achieved the transformation of the minimum entropy difference by adjusting the parameter r of the weight function. Finally, some numerical examples are given to prove that new method is more reasonable and effective