A Knowledge Measure with Parameter of Intuitionistic Fuzzy Sets

Zhang, Zhenhua; Yuan, Shujuan; Zhang, Jing; Ma, Chao; Xu, Jinhui; Lin, Xiaolong

doi:10.4236/am.2018.97060

Cited by 4 publications

(4 citation statements)

References 21 publications

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“…Fuzzy Inference entails merging the conditions specified in each rule to produce fuzzy outputs for each rule. The fuzzification process involves assessing the degree of association between each input value and various fuzzy sets [15].…”

Section: Design Of the Controllersmentioning

confidence: 99%

PID and Fuzzy Logic Control of Ball and Beam System Using Particle Swarm Optimization

Alızada,

Ozturk

2023

WJET

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In this research, we carried out the modeling of the ball and beam system (BBS) within the MATLAB/Simulink framework by applying both proportional-integral-derivative (PID) and fuzzy logic control strategies to govern the dynamics of this constructed model. The underlying non-linear dynamic equations adjusting the behavior of the BBS system are based on Newton's second law of motion. The physical installation of the BBS, designed for potential real-time application, comprises a lengthy beam subject to movement through the action of a DC servomotor, with a ball traversing the beam in a reciprocating manner. A distance sensor is strategically placed in front of the beam to determine the exact position of the ball. In this system, an electrical control signal applied to the DC servomotor causes the beam to pivot about its horizontal axis, thereby enabling the ball to move freely along the beam's length. To avoid the risk of losing the ball equilibrium on the beam and to achieve precise system control, a mathematical model was devised and implemented within the MATLAB/Simulink environment. The use of the particle swarm optimization (PSO) algorithm was aimed at tackling the task of refining and optimizing the PID controller specifically designed for the linearized ball and beam control system. The presented system is controlled using both PID and fuzzy logic, and the use of the PSO algorithm enhances the system's responsiveness efficiency.

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Section: Design Of the Controllersmentioning

confidence: 99%

PID and Fuzzy Logic Control of Ball and Beam System Using Particle Swarm Optimization

Alızada,

Ozturk

2023

WJET

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“…In line with the order property, Gou 39 proposed a new knowledge measure with an order. Many researches [41][42][43][44][45][46][47][48] have introduced various knowledge measures along with their applications. Up to the time, there was no knowledge measure in the standard fuzzy settings, Singh et al 49 filled this study gap by introducing an FKM.…”

Section: Related Workmentioning

confidence: 99%

Two‐parametric generalized fuzzy knowledge measure and accuracy measure with applications

Singh

Ganie

2021

Int J of Intelligent Sys

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Fuzzy entropy concerning a fuzzy set computes the imprecision content of that set whereas the content of precision is evaluated by using the notion of fuzzy knowledge measure. In this paper, we suggest a twoparametric version of the knowledge measure in fuzzy settings. Further, we investigate its novelty and advantages from various viewpoints, such as attribute weight computation, ambiguity computation, and appropriate handling of the structured linguistic variables. We also introduce a two-parametric generalized fuzzy accuracy measure (GFAM) and demonstrate its application in pattern analysis. Additionally, we contrast the effectiveness of the suggested fuzzy accuracy measure with several existing compatibility measures. We also consider the Multiobjective Optimization on the basis of Ratio Analysis (MOORA) method of multiple attribute decision-making (MADM) based on the proposed two-parametric generalized fuzzy knowledge measure and two-parametric GFAM. We also contrast the MADM method with the conventional MOORA method.

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“…Szmidt et al [5] introduced a correlation coefficient to measure the relationship strength between IFSs and determine the presence of positive or negative correlation. Zhang et al [6] introduced a knowledge measure for IFSs, adhering to extended Szmidt-Kacprzyk axioms for intuitionistic fuzzy (IF) entropy, along with a discussion on order property conditions, supported by numerical examples demonstrating the accuracy and superiority of this parametric model over classic models. Zeng et al [7] familiarized the IF ordered weighted distance operator in response to the need for handling IF information in various scenarios.…”

Section: Introductionmentioning

confidence: 99%

Dombi Aggregation Operators for p, qr–Spherica Fuzzy Sets: Application in the Stability Assessment of Cryptocurrencies

Akhtar,

Rahim,

Alanzi

et al. 2024

IEEE Access

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This paper presents novel operational laws for 𝑝, 𝑞, 𝑟 −spherical fuzzy sets (𝑝, 𝑞, 𝑟 −SFSs) by harnessing the Dombi t-norm (DTN) and t-conorm (DTCN). These laws serve as the foundation for a set of aggregation operators (AOs) designed to consolidate 𝑝, 𝑞, 𝑟 −spherical fuzzy (𝑝, 𝑞, 𝑟 −SF) information. Additionally, a multi-criteria decision-making (MCDM) method is outlined for addressing practical decision-making (DM) challenges. To demonstrate the application of the proposed approach, a numerical example is offered. Furthermore, we conducted a comparative study to validate the efficacy of the suggested approach. Finally, we discuss both the advantages and limitations of this innovative approach.

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A Knowledge Measure with Parameter of Intuitionistic Fuzzy Sets

Cited by 4 publications

References 21 publications

PID and Fuzzy Logic Control of Ball and Beam System Using Particle Swarm Optimization

PID and Fuzzy Logic Control of Ball and Beam System Using Particle Swarm Optimization

Two‐parametric generalized fuzzy knowledge measure and accuracy measure with applications

Dombi Aggregation Operators for p, qr–Spherica Fuzzy Sets: Application in the Stability Assessment of Cryptocurrencies

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