Special Issues
Table of Content

Emerging Trends in Fuzzy Logic

Submission Deadline: 30 December 2024 View: 2 Submit to Special Issue

Guest Editors

Prof. Yiming Tang, Hefei University of Technology, China
Prof. Xiaohui Yuan, University of North Texas, USA
Prof. Zhaohong Deng, Jiangnan University, China
Prof. Yong Zhang, Shenzhen University, China

Summary

Fuzzy logic is a science which uses fuzzy sets to discover fuzzy thinking, language form and   reasoning mechanism based upon multi-valued logic. Fuzzy logic imitates the uncertain concept judgment and reasoning thinking mode of human brain. Thereinto, fuzzy sets and fuzzy rules are applied to the description system whose model is unknown or uncertain, as well as the control object with strong nonlinear and large lag. As the core of fuzzy logic, fuzzy reasoning does well in solving uncertainty information problems with rules, which are difficult to deal with by conventional methods. Typical fuzzy reasoning strategies incorporate the compositional rule of inference (CRI) algorithm, the Bandler-Kohout subproduct (BKS) algorithm, the triple I method (where “I” denotes implication), the quintuple implication principle (QIP) algorithm, the universal triple I algorithm, the universal quintuple implicational (UQI) algorithm, and so on.

 

Fuzzy logic is good at expressing qualitative knowledge and experience with unclear boundaries. It can distinguish fuzzy sets, and deal with fuzzy relations, so as to simulate human brain to implement rule-based reasoning. Furthermore, it can effectively handle most of the uncertainty problems caused by the logic break of “exclusion law”.

 

The purpose of this special issue is to explore current status and future prospects of fuzzy logic, showcasing the latest advancements, challenges, and potential solutions in this rapidly evolving domain. We are particularly interested in (but not limited to) the following aspects:

1. Overview of the current state of fuzzy logic, including key technologies, methodologies, and applications.

2. Analysis of the challenges faced by fuzzy logic and potential strategies to overcome them.

3. Exploration of emerging trends and future directions in fuzzy logic.

 

From what has been outlined above, this special issue welcomes original research and review papers on all aspects of fuzzy logic.


Keywords

Fuzzy logic; Fuzzy reasoning; Fuzzy systems; Fuzzy control; Collaborative computing; Machine learning; Computer vision; Affective computing; Granular computing; Information security

Published Papers


  • Open Access

    ARTICLE

    Upper and Lower Bounds of the α-Universal Triple I Method for Unified Interval Implications

    Yiming Tang, Jianwei Gao, Yifan Huang
    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1063-1088, 2024, DOI:10.32604/cmc.2024.049341
    (This article belongs to the Special Issue: Emerging Trends in Fuzzy Logic)
    Abstract The α-universal triple I (α-UTI) method is a recognized scheme in the field of fuzzy reasoning, which was proposed by our research group previously. The robustness of fuzzy reasoning determines the quality of reasoning algorithms to a large extent, which is quantified by calculating the disparity between the output of fuzzy reasoning with interference and the output without interference. Therefore, in this study, the interval robustness (embodied as the interval stability) of the α-UTI method is explored in the interval-valued fuzzy environment. To begin with, the stability of the α-UTI method is explored for the… More >

  • Open Access

    ARTICLE

    On Multi-Granulation Rough Sets with Its Applications

    Radwan Abu-Gdairi, R. Mareay, M. Badr
    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1025-1038, 2024, DOI:10.32604/cmc.2024.048647
    (This article belongs to the Special Issue: Emerging Trends in Fuzzy Logic)
    Abstract Recently, much interest has been given to multi-granulation rough sets (MGRS), and various types of MGRS models have been developed from different viewpoints. In this paper, we introduce two techniques for the classification of MGRS. Firstly, we generate multi-topologies from multi-relations defined in the universe. Hence, a novel approximation space is established by leveraging the underlying topological structure. The characteristics of the newly proposed approximation space are discussed. We introduce an algorithm for the reduction of multi-relations. Secondly, a new approach for the classification of MGRS based on neighborhood concepts is introduced. Finally, a real-life More >

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