Intuitionistic Fuzzy Analytic Network Process

Liao, Huchang; Mi, Xiaomei; Zhang, Xu; Xu, Jiuping; Herrera, Francisco

doi:10.1109/tfuzz.2017.2788881

Cited by 65 publications

(29 citation statements)

References 29 publications

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“…However, apart from them, some others kinds of SMs by utilizing the fuzzy information are summarized in [7][8][9][10][11][12][13][14][15][16]. In addition, a complete bibliometric analysis of DMPs is summarized in [17,18].…”

Section: Introductionmentioning

confidence: 99%

Exponential similarity measures for Pythagorean fuzzy sets and their applications to pattern recognition and decision-making process

Nguyen

et al. 2019

Complex Intell. Syst.

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A Pythagorean fuzzy set is one of the successful extensions of the intuitionistic fuzzy set to handle the uncertain and fuzzy information in a more wider way. In this paper, some new exponential similarity measures (SMs) for measuring the similarities between objects are proposed. For it, we used the exponential function for the membership and the non-membership degrees and hence defined some series of the SMs for PFSs. The various desirable properties and their relations are examined. Several counter-intuitive cases are given to show the effectiveness of the proposed measures with the existing SMs. Furthermore, examples to classify the pattern recognition and the decision-making problems are presented and compared with the existing approaches.

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Section: Introductionmentioning

confidence: 99%

Exponential similarity measures for Pythagorean fuzzy sets and their applications to pattern recognition and decision-making process

Nguyen

et al. 2019

Complex Intell. Syst.

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“…The membership function is interpolated by different curve fitting methods such as Hill function, Cauchy-Lorentz, Gumbel curve and logistic curve. To leverage the relationship among the attributes, Analytical Hierarchy Process (AHP) [2] is exploited. Finally, after determining the fuzzy rules, we utilized the TSK inference system [3] to infer the final forwarding competency from the rules.…”

Section: Attributes and Fuzzifiermentioning

confidence: 99%

“…The determination of routing decision is firmly amalgamated with relationships among the criteria involved. To identify the relations among premises rules, a weight for each premise is obtained by employing Analytical Hierarchy Process (AHP) [2], which is an effective tool to deal with complex decision making by setting priorities for each attribute. The AHP is implemented by consecutive two steps, constructing a pairwise comparison matrix and priority rating.…”

Section: Road Segment Selection Inferencementioning

confidence: 99%

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Fuzzy-Based Distributed Protocol for Vehicle-to-Vehicle Communication

Hawbani

Torbosh

Wang

et al. 2021

IEEE Trans. Fuzzy Syst.

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this paper modeled the multihop data-routing in Vehicular Ad-hoc Networks(VANET) as Multiple Criteria Decision Making (MCDM) in four steps. First, the criteria which have an impact on the performance of the network layer are captured and transformed into fuzzy sets. Second, the fuzzy sets are characterized by Fuzzy Membership Functions(FMF) which are interpolated based on the data collected from massive experimental simulations. Third, the Analytical Hierarchy Process(AHP) is exploited to identify the relationships among the criteria. Fourth, multiple fuzzy rules are determined and, the TSK inference system is employed to infer and aggregate the final forwarding decision. Through integrating techniques of MCDM, FMF, AHP, and TSK, we designed a distributed and opportunistic data routing protocol, namely, VEFR (Vehicular Environment Fuzzy Router) which targets V2V (vehicle-to-vehicle) communication and runs in two main processes, Road Segment Selection(RSS) and Relay Vehicle Selection(RVS). RSS is intended to select multiple successive junctions through which the packets should travel from the source to the destination, while RVS process is intended to select relay vehicles within the selected road segment. The experimental results showed that our protocol performs and scales well with both network size and density, considering the combined problem of end-to-end packet delivery ratio and end-to-end latency.

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“…As a generalized form of FS, the intuitionistic fuzzy set (IFS) developed by Atanassov [2] has a membership degree (MD), a non-membership degree (NMD), and a hesitancy degree (HD), which is free of the limitations of FS. Recently, studies on the DM methods based on the IFS have attracted substantial attention in various areas, such as supplier selection [3], "one belt, one road" investment selection [4], brand management [5], mine emergency DM [6], hospital performance evaluation [7], etc. Lately, a large number of contributions have focused on DM techniques based on IFSs, which are from three domains:…”

Section: Introductionmentioning

confidence: 99%

Multi-Attribute Decision Making Based on Intuitionistic Fuzzy Power Maclaurin Symmetric Mean Operators in the Framework of Dempster-Shafer Theory

2019

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It is well known that there are some unfavorable shortcomings in the ordinary operational rules (OORs) of intuitionistic fuzzy number (IFN), and there exists a close and forceful connection between the intuitionistic fuzzy set (IFS) and Dempster-Shafer Theory (DST). We can utilize this relationship to present a transparent and fruitful semantic framework for IFS in terms of DST. In the framework of DST, an IFN can be converted into a basic probability assignment (BPA) and operations on IFNs can be represented as operations on a belief interval (BI), which can break away from the revealed shortcomings of the OORs of the IFN. Although there are many operators to aggregate the IFN, the operator to aggregate the BPA is rare. The Maclaurin symmetric mean (MSM) operator has the advantage of considering interrelationships among any number of attributes. The power average (PA) operator can reduce the influences of extreme evaluation values. In addition, for measuring the difference between IFNs, we replace the Hamming distance and Euclidean distance with the Jousselme distance (JD). In this paper, we develop an intuitionistic fuzzy power MSM (IFPMSMDST) operator and an intuitionistic fuzzy weighted power MSM (IFPWMSMDST) operator in the framework of the DST and provide their favorable properties. Then, we propose a novel method based on the proposed operators to solve multi-attribute decision-making (MADM) problems without intermediate defuzzification when their attributes and weights are both IFNs. Finally, some examples are utilized to demonstrate that the proposed methods outperform the previous ones.

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Intuitionistic Fuzzy Analytic Network Process

Cited by 65 publications

References 29 publications

Exponential similarity measures for Pythagorean fuzzy sets and their applications to pattern recognition and decision-making process

Exponential similarity measures for Pythagorean fuzzy sets and their applications to pattern recognition and decision-making process

Fuzzy-Based Distributed Protocol for Vehicle-to-Vehicle Communication

Multi-Attribute Decision Making Based on Intuitionistic Fuzzy Power Maclaurin Symmetric Mean Operators in the Framework of Dempster-Shafer Theory

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