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Mathematics
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Fuzzy Logic in Artificial Intelligence Systems
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Fuzzy Logic in Artificial Intelligence Systems

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: closed (31 May 2024) | Viewed by 9029

Special Issue Editor

Prof. Dr. Danilo Pelusi

E-Mail Website
Guest Editor
Department of Computer Science, University of Teramo, 64100 Teramo, Italy
Interests: fuzzy logic; machine learning; evolutionary algorithms; computational intelligence; information theory
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The capability to learn from data is a proper feature of machine learning (ML). MLs are based on the idea that systems can learn from data whether suitably trained. However, such systems do not provide the expected results because the degree of knowledge is poor or even missing. For designing suitable artificial intelligent systems, either learning and knowledge are needed. Thus, the task of fuzzy logic is to add knowledge to the systems for improving their performances. The combination between knowledge and learning has given rise to the design of hybrid intelligent systems. The optimization of this kind of systems is also based on nature-inspired optimization algorithms. Therefore, ML nature-inspired systems can be enhanced by adding fuzzy techniques. This Special Issue provides a platform for researchers from academia and industry to present their novel and unpublished works in the domain of intelligent control, robotics, intelligent transportation systems, pattern recognition, medical diagnosis, decision systems, and optimization of complex problems.  

Dr. Danilo Pelusi
Guest Editor

Manuscript Submission Information

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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 controllers
  • neuro-fuzzy systems
  • soft computing
  • fuzzy-evolutionary systems
  • pattern recognition
  • intelligent health systems
  • expert systems
  • intelligent agents
  • machine learning
  • intelligent transportation systems
  • data mining
  • intelligent optimization
  • decision systems

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Published Papers (3 papers)

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Research

22 pages, 4640 KiB  
Article
Stability Analysis for Digital Redesign of Discrete-Time Switched Systems Using H∞ Linear Matrix Inequality
by Nien-Tsu Hu
Mathematics 2023, 11(11), 2468; https://doi.org/10.3390/math11112468 - 27 May 2023
Cited by 1 | Viewed by 1208
Abstract
In this paper, the stability problem for the digital redesign of discrete-time switched systems using H∞ linear matrix inequality (LMI) is investigated. We propose the switching time approach for digital redesign between controller work and failure, and this switching time will limit [...] Read more.
In this paper, the stability problem for the digital redesign of discrete-time switched systems using H∞ linear matrix inequality (LMI) is investigated. We propose the switching time approach for digital redesign between controller work and failure, and this switching time will limit the system output within the system capacity. When the controller fails, the overall system will be unstable. Therefore, if the digital redesign controller is not restored in a certain period of time, the system output will exceed the system capacity. To solve this problem, we propose a switching law to determine the switching time between the stable mode (controller work) and the unstable (controller failure) mode; this will limit the overall system states in the unstable mode. In addition, the digital redesign controller has the advantage of faster tracking. After we propose a discrete-time switching system with stable and unstable modes, we use H∞ linear matrix inequality (LMI) and Lyapunov functions to prove the stability in detail. Finally, the numerical example illustrates the feasibility of the proposed approach. Full article
(This article belongs to the Special Issue Fuzzy Logic in Artificial Intelligence Systems)
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18 pages, 439 KiB  
Article
Social Sustainability and Resilience in Supply Chains of Latin America on COVID-19 Times: Classification Using Evolutionary Fuzzy Knowledge
by Miguel Reyna-Castillo, Alejandro Santiago, Salvador Ibarra Martínez and José Antonio Castán Rocha
Mathematics 2022, 10(14), 2371; https://doi.org/10.3390/math10142371 - 6 Jul 2022
Cited by 11 | Viewed by 3534
Abstract
The number of research papers interested in studying the social dimension of supply chain sustainability and resilience is increasing in the literature. However, the social dimension is complex, with several uncertainty variables that cannot be expressed with a traditional Boolean logic of totally [...] Read more.
The number of research papers interested in studying the social dimension of supply chain sustainability and resilience is increasing in the literature. However, the social dimension is complex, with several uncertainty variables that cannot be expressed with a traditional Boolean logic of totally true or false. To cope with uncertainty, Fuzzy Logic allows the development of models to obtain crisp values from the concept of fuzzy linguistic variables. Using the Structural Equation Model by Partial Least Squares (SEM-PLS) and Evolutionary Fuzzy Knowledge, this research aims to analyze the predictive power of social sustainability characteristics on supply chain resilience performance in the context of the COVID-19 pandemic with representative cases from Mexico and Chile. We validate our approach using the Chile database for training our model and the Mexico database for testing. The fuzzy knowledge database has a predictive power of more than 80%, using social sustainability features as inputs regarding supply chain resilience in the context of the COVID-19 pandemic disruption. To our knowledge, no works in the literature use fuzzy evolutionary knowledge to study social sustainability in correlation with resilience. Moreover, our proposed approach is the only one that does not require a priori expert knowledge or a systematic mathematical setup. Full article
(This article belongs to the Special Issue Fuzzy Logic in Artificial Intelligence Systems)
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18 pages, 655 KiB  
Article
Improving Facial Emotion Recognition Using Residual Autoencoder Coupled Affinity Based Overlapping Reduction
by Sankhadeep Chatterjee, Asit Kumar Das, Janmenjoy Nayak and Danilo Pelusi
Mathematics 2022, 10(3), 406; https://doi.org/10.3390/math10030406 - 27 Jan 2022
Cited by 7 | Viewed by 2403
Abstract
Emotion recognition using facial images has been a challenging task in computer vision. Recent advancements in deep learning has helped in achieving better results. Studies have pointed out that multiple facial expressions may present in facial images of a particular type of emotion. [...] Read more.
Emotion recognition using facial images has been a challenging task in computer vision. Recent advancements in deep learning has helped in achieving better results. Studies have pointed out that multiple facial expressions may present in facial images of a particular type of emotion. Thus, facial images of a category of emotion may have similarity to other categories of facial images, leading towards overlapping of classes in feature space. The problem of class overlapping has been studied primarily in the context of imbalanced classes. Few studies have considered imbalanced facial emotion recognition. However, to the authors’ best knowledge, no study has been found on the effects of overlapped classes on emotion recognition. Motivated by this, in the current study, an affinity-based overlap reduction technique (AFORET) has been proposed to deal with the overlapped class problem in facial emotion recognition. Firstly, a residual variational autoencoder (RVA) model has been used to transform the facial images to a latent vector form. Next, the proposed AFORET method has been applied on these overlapped latent vectors to reduce the overlapping between classes. The proposed method has been validated by training and testing various well known classifiers and comparing their performance in terms of a well known set of performance indicators. In addition, the proposed AFORET method is compared with already existing overlap reduction techniques, such as the OSM, ν-SVM, and NBU methods. Experimental results have shown that the proposed AFORET algorithm, when used with the RVA model, boosts classifier performance to a greater extent in predicting human emotion using facial images. Full article
(This article belongs to the Special Issue Fuzzy Logic in Artificial Intelligence Systems)
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