基于统计观点的球形模糊相关系数及其在分类和双向近似推理中的应用

IF 1.2 4区 计算机科学 Q4 AUTOMATION & CONTROL SYSTEMS Archives of Control Sciences Pub Date : 2024-03-29 DOI:10.24425/acs.2024.149652
A. Ganie, Debashis Dutta
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引用次数: 0

摘要

与图象模糊集、费马特模糊集、毕达哥拉斯模糊集、直觉模糊集和模糊集相比,球形模糊集在模拟不确定情况方面更为强大。本文首先定义了球形模糊集的方差和协方差。然后,利用方差和协方差,我们定义了与统计相关系数一致的唯一球形模糊集相关度量。利用所提供的相关度量,两个球形模糊集在方向和强度上都是相关的。我们讨论了它的许多特点。我们通过语言变量将相关性度量与当前的相关性度量进行了比较。通过展示其在双向近似推理中的应用,我们确定了它的有效性。我们还利用所提供的相关函数解决了球形模糊环境中的模式识别问题,并将结果与当前的几种测量方法进行了比较。
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A spherical fuzzy correlation coefficient based on statistical viewpoint with its applications in classification and bidirectional approximate reasoning
Spherical fuzzy sets are more powerful in modelling the uncertain situations than picture fuzzy sets, fermatean fuzzy sets, Pythagorean fuzzy sets, intuitionistic fuzzy sets, and fuzzy sets. In this paper, we first define the variance and covariance of spherical fuzzy sets. Then, using variance and covariance, we define the unique spherical fuzzy set correlation metric in line with the statistical coefficient of correlation. Two spherical fuzzy sets are correlated in both direction and strength using the provided measure of correlation. We discussed its many characteristics. We compared the measure of correlation with the current ones through linguistic variables. We established its validity by showing its application in bidirectional approximate reasoning. We also resolve a pattern identification issue in the spherical fuzzy environment using the provided correlation function, and we compare the results with several current measurements.
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来源期刊
Archives of Control Sciences
Archives of Control Sciences Mathematics-Modeling and Simulation
CiteScore
2.40
自引率
33.30%
发文量
0
审稿时长
14 weeks
期刊介绍: Archives of Control Sciences welcomes for consideration papers on topics of significance in broadly understood control science and related areas, including: basic control theory, optimal control, optimization methods, control of complex systems, mathematical modeling of dynamic and control systems, expert and decision support systems and diverse methods of knowledge modelling and representing uncertainty (by stochastic, set-valued, fuzzy or rough set methods, etc.), robotics and flexible manufacturing systems. Related areas that are covered include information technology, parallel and distributed computations, neural networks and mathematical biomedicine, mathematical economics, applied game theory, financial engineering, business informatics and other similar fields.
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