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International Journal of Fuzzy Logic and Intelligent Systems 2024; 24(3): 215-230

Published online September 25, 2024

https://doi.org/10.5391/IJFIS.2024.24.3.215

© The Korean Institute of Intelligent Systems

On the Relationship among L-Fuzzy Relational Structures

Sutapa Mahato1 and S. P. Tiwari2

1Centre for Data Science, ITER Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar, India
2Department of Mathematics and Computing, Indian Institute of Technology (ISM), Dhanbad, India

Correspondence to :
Sutapa Mahato (sutapaiitdhanbad@gmail.com)

Received: September 25, 2023; Revised: August 2, 2024; Accepted: September 9, 2024

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted noncommercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

This paper establishes the relationship among L-fuzzy approximation spaces, L-fuzzy formal context analysis, and L-fuzzy Chu spaces, where L is a residuated lattice. Specifically, three types of L-fuzzy concept lattices are constructed based on generalized L-fuzzy rough approximation operators and linked to the classical L-fuzzy concept lattice, which is generated using Birkhoff operators. We further show that each L-fuzzy approximation space can be represented as an L-fuzzy Chu space, and this representation preserves products and co-products. Establishing these relationships not only provides a unified framework for different L-fuzzy concepts but also opens the way for further advancements in the theoretical foundation of fuzzy logic. This work bridges the gap between different mathematical approaches to fuzziness, offering new insights and tools in the field.

Keywords: L-fuzzy formal context, Concept lattice, L-fuzzy Chu space, L-fuzzy approximation spaces, Category, Product, Co-product

The authors declare no potential conflicts of interest relevant to this manuscript.

Sutapa Mahato is currently working as an assistant professor in the Department of Centre for Data Science (CDS), ITER, Siksha ‘O’ Anusandhan (Deemed to be) University, Bhubaneswar, India. She received her Ph.D. degree from Indian Institute of Technology (ISM) Dhanbad, India. Her area of research includes fuzzy rough set theory, automata theory, category theory, and topology.

S. P. Tiwari is a professor in the Department of Mathematics & Computing, Indian Institute of Technology (ISM) Dhanbad, Dhanbad, India. He received his Ph.D. degree from Banaras Hindu University, Varanasi, India. He is a member of American Mathematical Society, life member of Indian Mathematical Society and was Board Member of European Society for Fuzzy Logic and Technology from 2017–2021. His area of research includes automata theory, rough set theory, category theory and topology.

Article

Original Article

International Journal of Fuzzy Logic and Intelligent Systems 2024; 24(3): 215-230

Published online September 25, 2024 https://doi.org/10.5391/IJFIS.2024.24.3.215

Copyright © The Korean Institute of Intelligent Systems.

On the Relationship among L-Fuzzy Relational Structures

Sutapa Mahato1 and S. P. Tiwari2

1Centre for Data Science, ITER Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar, India
2Department of Mathematics and Computing, Indian Institute of Technology (ISM), Dhanbad, India

Correspondence to:Sutapa Mahato (sutapaiitdhanbad@gmail.com)

Received: September 25, 2023; Revised: August 2, 2024; Accepted: September 9, 2024

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted noncommercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This paper establishes the relationship among L-fuzzy approximation spaces, L-fuzzy formal context analysis, and L-fuzzy Chu spaces, where L is a residuated lattice. Specifically, three types of L-fuzzy concept lattices are constructed based on generalized L-fuzzy rough approximation operators and linked to the classical L-fuzzy concept lattice, which is generated using Birkhoff operators. We further show that each L-fuzzy approximation space can be represented as an L-fuzzy Chu space, and this representation preserves products and co-products. Establishing these relationships not only provides a unified framework for different L-fuzzy concepts but also opens the way for further advancements in the theoretical foundation of fuzzy logic. This work bridges the gap between different mathematical approaches to fuzziness, offering new insights and tools in the field.

Keywords: L-fuzzy formal context, Concept lattice, L-fuzzy Chu space, L-fuzzy approximation spaces, Category, Product, Co-product

Fig 1.

Figure 1.

Diagram for Theorem 3.1.

The International Journal of Fuzzy Logic and Intelligent Systems 2024; 24: 215-230https://doi.org/10.5391/IJFIS.2024.24.3.215

Fig 2.

Figure 2.

Diagram for Theorem 3.3.

The International Journal of Fuzzy Logic and Intelligent Systems 2024; 24: 215-230https://doi.org/10.5391/IJFIS.2024.24.3.215

Fig 3.

Figure 3.

Diagram for Theorem 3.3.

The International Journal of Fuzzy Logic and Intelligent Systems 2024; 24: 215-230https://doi.org/10.5391/IJFIS.2024.24.3.215

Fig 4.

Figure 4.

Diagram for Definition 5.4.

The International Journal of Fuzzy Logic and Intelligent Systems 2024; 24: 215-230https://doi.org/10.5391/IJFIS.2024.24.3.215

Fig 5.

Figure 5.

Diagram for Proposition 5.2.

The International Journal of Fuzzy Logic and Intelligent Systems 2024; 24: 215-230https://doi.org/10.5391/IJFIS.2024.24.3.215

Fig 6.

Figure 6.

Diagram for Theorem 5.1.

The International Journal of Fuzzy Logic and Intelligent Systems 2024; 24: 215-230https://doi.org/10.5391/IJFIS.2024.24.3.215

Fig 7.

Figure 7.

Diagram for Proposition 5.3.

The International Journal of Fuzzy Logic and Intelligent Systems 2024; 24: 215-230https://doi.org/10.5391/IJFIS.2024.24.3.215

Fig 8.

Figure 8.

Diagram for Theorem 5.2.

The International Journal of Fuzzy Logic and Intelligent Systems 2024; 24: 215-230https://doi.org/10.5391/IJFIS.2024.24.3.215

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