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Mathematics
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Intelligent and Fuzzy Systems in Engineering and Technology
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Intelligent and Fuzzy Systems in Engineering and Technology

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Fuzzy Sets, Systems and Decision Making".

Deadline for manuscript submissions: 31 March 2025 | Viewed by 2620

Special Issue Editor

Prof. Dr. Xiaodong Liu

E-Mail Website
Guest Editor
College of Control Science and Engineering, Dalian University of Technology, Dalian 116024, China
Interests: fuzzy system; AFS theory and its applications; knowledge discovery and representations; data mining; pattern recognition; intelligent control systems

Special Issue Information

Dear Colleagues,

The development of fuzzy systems has reached various areas, such as financial trade, medicine, transportation, telecommunications, and many other engineering and technology dimensions. Interpretability is one of the most appreciated advantages of fuzzy systems, especially in situations with high human interaction, where it benefits by representing and incorporating knowledge in artificial intelligent systems.

Many extended models are improving the practicality and effectiveness of fuzzy systems in engineering and technology. They offer more powerful “human center systems” to analyze uncertainty and extract semantic represented knowledge from big heterogeneous data.

This Special Issue aims to provide a platform for researchers to discuss research, developments, and innovations in fuzzy systems in engineering and technology, interpretable algorithms, and semantic learning. The topics of interest include, but are not limited to, the following:

  • Fuzzy system and fuzzy inference;
  • Semantic learning;
  • Uncertain knowledge reasoning;
  • Granular computing;
  • Uncertain decision making;
  • Fuzzy dynamic data analysis;
  • Fuzzy rough sets;
  • Cognitive computation;
  • Human-computer interaction;
  • Machine learning.

Advances in interpretable artificial intelligence based on fuzzy theory and its applications are particularly welcome in this Special Issue.

Prof. Dr. Xiaodong Liu
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • fuzzy sets
  • fuzzy system
  • semantic learning
  • granular computing
  • interpretability

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (2 papers)

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Research

24 pages, 1284 KiB  
Article
An Integrated T-Spherical Fuzzy Einstein Interaction Aggregator Group Decision-Making Approach: A Case Study of Concrete 3D Printing Robot Application in Vietnam
by Nhat-Luong Nhieu and Tri Dung Dang
Mathematics 2024, 12(13), 2086; https://doi.org/10.3390/math12132086 - 3 Jul 2024
Viewed by 441
Abstract
This study introduces the integrated T-spherical fuzzy Einstein interaction aggregator group decision-making approach, a novel framework designed to enhance multi-criteria decision-making (MCDM). Implementing the case study of concrete 3D printing technology in Vietnam, this approach integrates T-spherical fuzzy sets with Einstein aggregation operators [...] Read more.
This study introduces the integrated T-spherical fuzzy Einstein interaction aggregator group decision-making approach, a novel framework designed to enhance multi-criteria decision-making (MCDM). Implementing the case study of concrete 3D printing technology in Vietnam, this approach integrates T-spherical fuzzy sets with Einstein aggregation operators to handle the complexities of uncertain and subjective expert judgments effectively. The methodology provides a robust mechanism for evaluating and prioritizing the barriers and strategies associated with the implementation of concrete 3D printing. Findings from this study underline the significance of technological advancements and strategic financial incentives, with R&D strategy emerging as the top priority. This research contributes to both theoretical advancements in decision-making frameworks and offers practical insights for industries looking to integrate emerging technologies. Moreover, it demonstrates the application of advanced fuzzy set theories in real-world settings, providing a valuable tool for decision-makers facing similar technological adoption challenges. Full article
(This article belongs to the Special Issue Intelligent and Fuzzy Systems in Engineering and Technology)
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16 pages, 727 KiB  
Article
Stochastic Configuration Based Fuzzy Inference System with Interpretable Fuzzy Rules and Intelligence Search Process
by Wei Zhou, Hongxing Li and Menghong Bao
Mathematics 2023, 11(3), 614; https://doi.org/10.3390/math11030614 - 26 Jan 2023
Cited by 2 | Viewed by 1392
Abstract
In this paper, a stochastic configuration based fuzzy inference system with interpretable fuzzy rules (SCFS-IFRs) is proposed to improve the interpretability and performance of the fuzzy inference system and determine autonomously an appropriate model structure. The proposed SCFS-IFR first accomplishes a fuzzy system [...] Read more.
In this paper, a stochastic configuration based fuzzy inference system with interpretable fuzzy rules (SCFS-IFRs) is proposed to improve the interpretability and performance of the fuzzy inference system and determine autonomously an appropriate model structure. The proposed SCFS-IFR first accomplishes a fuzzy system through interpretable linguistic fuzzy rules (ILFRs), which endows the system with clear semantic interpretability. Meanwhile, using an incremental learning method based on stochastic configuration, the appropriate architecture of the system is determined by incremental generation of ILFRs under a supervision mechanism. In addition, the particle swarm optimization (PSO) algorithm, an intelligence search technique, is used in the incremental learning process of ILFRs to obtain better random parameters and improve approximation accuracy. The performance of SCFS-IFRs is verified by regression and classification benchmark datasets. Regression experiments show that the proposed SCFS-IFRs perform best on 10 of the 20 data sets, statistically significantly outperforming the other eight state-of-the-art algorithms. Classification experiments show that, compared with the other six fuzzy classifiers, SCFS-IFRs achieve higher classification accuracy and better interpretation with fewer rules. Full article
(This article belongs to the Special Issue Intelligent and Fuzzy Systems in Engineering and Technology)
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