Uncertainty Measurement for Fuzzy Set-Valued Data

Wang, Sichun; Wang, Yini; Tang, Hongxiang

doi:10.1109/access.2020.2972921

Cited by 2 publications

(1 citation statement)

References 33 publications

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“…A wide variety of membership functions exist in current research literature, among which the most frequently used are Gaussian, triangular, and trapezoidal functions [74]. Meanwhile, scholars are showing an increased interest in various extensions of fuzzy sets, including intuitionistic fuzzy sets and hesitant fuzzy sets.…”

Section: Uncertainty Modelingmentioning

confidence: 99%

A Review of Uncertainty-Based Multidisciplinary Design Optimization Methods Based on Intelligent Strategies

Wang,

Fan,

Qiang

2023

Symmetry

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The design of aerospace systems is recognized as a complex interdisciplinary process. Many studies have shown that the exchange of information among multiple disciplines often results in strong coupling and nonlinearity characteristics in system optimization. Meanwhile, inevitable multi-source uncertainty factors continuously accumulate during the optimization process, greatly compromising the system’s robustness and reliability. In this context, uncertainty-based multidisciplinary design optimization (UMDO) has emerged and has been preliminarily applied in aerospace practices. However, it still encounters major challenges, including the complexity of multidisciplinary analysis modeling, and organizational and computational complexities of uncertainty analysis and optimization. Extensive research has been conducted recently to address these issues, particularly uncertainty analysis and artificial intelligence strategies. The former further enriches the UMDO technique, while the latter makes outstanding contributions to addressing the computational complexity of UMDO. With the aim of providing an overview of currently available methods, this paper summarizes existing state-of-the art UMDO technologies, with a special focus on relevant intelligent optimization strategies.

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Section: Uncertainty Modelingmentioning

confidence: 99%

A Review of Uncertainty-Based Multidisciplinary Design Optimization Methods Based on Intelligent Strategies

Wang,

Fan,

Qiang

2023

Symmetry

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Uncertainty Measures in Fuzzy Set-Valued Information Systems Based on Fuzzy β-Neighborhood Similarity Relations

Ren,

Zhu

2023

Int. J. Unc. Fuzz. Knowl. Based Syst.

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Uncertainty measures are instrumental in describing the classification abilities in information systems, and uncertain information has been measured and processed with granular computing theory. While the fuzzy set-valued information system is a generalization of fuzzy information systems, the relationship between the information granulation and the uncertainty in fuzzy set-valued information systems remains to be studied. This paper probes into uncertainty measures in fuzzy set-valued information systems based on the fuzzy [Formula: see text]-neighborhood and the idea of granulation. Specifically, the fuzzy [Formula: see text]-neighborhood similarity relation that reflects the similarity between two objects is defined in terms of the nearness degree. We propose the concepts of information granules and granular structures induced by fuzzy [Formula: see text]-neighborhood similarity relations, based on which we introduce the granularity measures and rough approximation measures of granular structures in fuzzy set-valued information systems. Given the situation of decision information systems, we propose the granularity-based rough approximation measures by combining granularity measures with rough approximation measures. Experiment results and effectiveness analysis show that the measures we proposed are reasonable and feasible.

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Uncertainty Measurement for Fuzzy Set-Valued Data

Cited by 2 publications

References 33 publications

A Review of Uncertainty-Based Multidisciplinary Design Optimization Methods Based on Intelligent Strategies

A Review of Uncertainty-Based Multidisciplinary Design Optimization Methods Based on Intelligent Strategies

Uncertainty Measures in Fuzzy Set-Valued Information Systems Based on Fuzzy β-Neighborhood Similarity Relations

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