Peng Yu

Dr. Peng Yu


Research Focus & Metrics

Academic Career Length

8Years (2019-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

View All Publications
2 December 2022 in Fuzzy Sets and Systems
Verified
Journal Article
Closed access
HSHu Xiong Song
Hu Xiong Song
PYPeng Yu
Peng Yu
HLHui Liu
Hui Liu

24 February 2025 in Artificial Intelligence Review
Verified
Journal Article
Open access
PDF file
PYPeng Yu
Peng Yu
XZXiyue Zhao
Xiyue Zhao
Three-way decision-making provides an effective framework for addressing uncertainty, aligning closely with human cognitive decision patterns. This paper proposes a novel three-way decision model based on multi-granular support intuitionistic fuzzy rough sets, integrating n-dimensional overlap and grouping functions. The model constructs optimistic and pessimistic upper and lower approximations to optimize decision rules and introduces score and precision functions for ranking. To validate the model, a consumer decision-making algorithm was developed and applied to empirical data. The results demonstrate that the proposed model effectively narrows decision boundary regions, enhances decision-making precision, and supports decision-making in complex multi-attribute scenarios. This study not only advances rough set theory but also offers practical tools for addressing real-world uncertainty in decision-making.

1 January 2021 in Chinese Journal of Electronics
Verified
Journal Article
Open access
PDF file
PYPeng Yu
Peng Yu
The main purpose of this paper is to establish a type of quantitative model by using the contangent similarity function in the three‐valued Łukasiewicz propositional logic system Ł3. We introduce the concepts of the cotangent similarity degree, cotangent pseudo‐distance and cotangent truth degree of the propositions, together with their basic properties in Ł3. We investigate the relationship between the cotangent truth degree and contangent pseudo‐distance, and prove the continuity of the logical connectives and in the logical metric space. We propose a graded reduction method and three types of graded reasoning frameworks on the propositions set F(S), and provide several examples and basic properties of it.

20 June 2019 in Symmetry
Verified
Journal Article
Open access
PDF file
XZXiaohong Zhang
Xiaohong Zhang
YMYingcang Ma
Yingcang Ma
PYPeng Yu
Peng Yu
The quasi-cancellativity of Abel Grassmann‘s groupoids (AG-groupoids) are discussed and two conjectures are partially solved. First, the following conjecture is proved to be true: every AG-3-band is quasi-cancellative. Moreover, a new notion of AG-(4,1)-band is proposed, and it is also proved that every AG-(4,1)-band is quasi-cancellative. Second, the notions of left (right) quasi-cancellative AG-groupoids and power-cancellative AG-groupoids are proposed, and the following results are obtained: for an AG*-groupoid or AG**-groupoid, it is left quasi-cancellative if and only if it is right quasi-cancellative; for a power-cancellative and locally power-associative AG-groupoid, it is left quasi-cancellative if and only if it is right quasi-cancellative. Finally, a general result is proved, that for any AG-groupoid, if it is left quasi-cancellative then it is right quasi-cancellative.

15 October 2025 in Computational and Applied Mathematics
Verified
Journal Article
Closed access
PDF file
PYPeng Yu
Peng Yu
HSHuxiong Song
Huxiong Song

2 January 2020 in Fuzzy Information and Engineering
Verified
Journal Article
Open access
PYPeng Yu
Peng Yu
XAXiao-Gang An
Xiao-Gang An
XZXiao-Hong Zhang
Xiao-Hong Zhang
By introducing the concepts of intuitionistic L-fuzzy β-covering and intuitionistic L-fuzzy β-neighborhood, we define three kinds of intuitionistic L-fuzzy β-covering rough set models. The basic properties of those intuitionistic L-fuzzy β-covering rough set models are investigated. Moreover, we define the other three kinds of intuitionistic L-fuzzy β-covering rough set models by using the former three models. Finally, we present the matrix representations of the newly defined lower and upper approximation operators so that the calculation of lower and upper approximations of subsets can be converted into operations on matrices.