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Mathematics
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Advances in Fuzzy Logic and Artificial Neural Networks
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Advances in Fuzzy Logic and Artificial Neural Networks

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: 30 November 2024 | Viewed by 19650

Special Issue Editor

Prof. Dr. Francisco Rodrigues Lima Junior

E-Mail Website
Guest Editor
Department of Management and Economics, Federal Technological University of Paraná, Av. Sete de Setembro, 3165, Rebouças, Curitiba 80230-901, Paraná, Brazil
Interests: fuzzy logic; artificial neural network; decision making; operations management

Special Issue Information

Dear Colleagues,

Fuzzy logic and artificial neural networks are among the most used artificial intelligence approaches for solving problems involving decision making, pattern classification, functional approximation, and image processing, among other things. We invite researchers to contribute original articles that present new theoretical and practical developments on neural networks, fuzzy logic and their recent extensions. Studies that propose new methods, theoretical advances, comparative analyses, and innovative applications in various fields will be accepted. Survey articles on current trends related to these methods are also welcome. Advanced fuzzy theory applications may involve hesitant fuzzy sets and their extensions, fuzzy 2-tuple, and spherical fuzzy sets, among other things. The scope of this Special Issue also includes studies involving advanced and hybrid neural networks, such as deep neural networks, probabilistic neural networks, neuro-fuzzy systems, fuzzy ART, and fuzzy ARTMAP neural networks.

Prof. Dr. Francisco Rodrigues Lima Junior
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 set theory and extensions
  • neural networks and extensions
  • neuro-fuzzy systems
  • deep neural networks
  • learning algorithms
  • comparative studies
  • theoretical reviews
  • engineering and scientific applications
  • operations research

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Published Papers (10 papers)

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Research

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22 pages, 3035 KiB  
Article
Neural Network-Based Design of a Buck Zero-Voltage-Switching Quasi-Resonant DC–DC Converter
by Nikolay Hinov and Bogdan Gilev
Mathematics 2024, 12(21), 3305; https://doi.org/10.3390/math12213305 - 22 Oct 2024
Viewed by 616
Abstract
In this paper, a design method using a neural network of a zero-voltage-switching buck quasi-resonant DC–DC converter is presented. The use of this innovative approach is justified because the design of quasi-resonant DC–DC converters is more complex compared to that of classical DC–DC [...] Read more.
In this paper, a design method using a neural network of a zero-voltage-switching buck quasi-resonant DC–DC converter is presented. The use of this innovative approach is justified because the design of quasi-resonant DC–DC converters is more complex compared to that of classical DC–DC converters. The converter is a piecewise linear system mathematically described by Kirchhoff’s laws and represented through switching functions. In this way, a mathematical model is used to generate data on the behavior of the state variables obtained under various design parameters. The obtained data are appropriately normalized, and a neural network is trained with them, which in practice serves as the inverse model of the device. An example is considered to demonstrate how this network can be used to design the converter. The key advantages of the proposed methodology include reducing the development time, improving energy efficiency, and the ability to automatically adapt to different loads and input conditions. This approach offers new opportunities for the design of advanced DC–DC converters in industries with high efficiency and performance requirements, such as the automotive industry and renewable energy sources. Full article
(This article belongs to the Special Issue Advances in Fuzzy Logic and Artificial Neural Networks)
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27 pages, 7268 KiB  
Article
Integrating Fuzzy C-Means Clustering and Explainable AI for Robust Galaxy Classification
by Gabriel Marín Díaz, Raquel Gómez Medina and José Alberto Aijón Jiménez
Mathematics 2024, 12(18), 2797; https://doi.org/10.3390/math12182797 - 10 Sep 2024
Viewed by 801
Abstract
The classification of galaxies has significantly advanced using machine learning techniques, offering deeper insights into the universe. This study focuses on the typology of galaxies using data from the Galaxy Zoo project, where classifications are based on the opinions of non-expert volunteers, introducing [...] Read more.
The classification of galaxies has significantly advanced using machine learning techniques, offering deeper insights into the universe. This study focuses on the typology of galaxies using data from the Galaxy Zoo project, where classifications are based on the opinions of non-expert volunteers, introducing a degree of uncertainty. The objective of this study is to integrate Fuzzy C-Means (FCM) clustering with explainability methods to achieve a precise and interpretable model for galaxy classification. We applied FCM to manage this uncertainty and group galaxies based on their morphological characteristics. Additionally, we used explainability methods, specifically SHAP (SHapley Additive exPlanations) values and LIME (Local Interpretable Model-Agnostic Explanations), to interpret and explain the key factors influencing the classification. The results show that using FCM allows for accurate classification while managing data uncertainty, with high precision values that meet the expectations of the study. Additionally, SHAP values and LIME provide a clear understanding of the most influential features in each cluster. This method enhances our classification and understanding of galaxies and is extendable to environmental studies on Earth, offering tools for environmental management and protection. The presented methodology highlights the importance of integrating FCM and XAI techniques to address complex problems with uncertain data. Full article
(This article belongs to the Special Issue Advances in Fuzzy Logic and Artificial Neural Networks)
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26 pages, 2071 KiB  
Article
Simulations and Bisimulations between Weighted Finite Automata Based on Time-Varying Models over Real Numbers
by Predrag S. Stanimirović, Miroslav Ćirić, Spyridon D. Mourtas, Pavle Brzaković and Darjan Karabašević
Mathematics 2024, 12(13), 2110; https://doi.org/10.3390/math12132110 - 5 Jul 2024
Viewed by 828
Abstract
The zeroing neural network (ZNN) is an important kind of continuous-time recurrent neural network (RNN). Meanwhile, the existence of forward and backward simulations and bisimulations for weighted finite automata (WFA) over the field of real numbers has been widely investigated. Two types of [...] Read more.
The zeroing neural network (ZNN) is an important kind of continuous-time recurrent neural network (RNN). Meanwhile, the existence of forward and backward simulations and bisimulations for weighted finite automata (WFA) over the field of real numbers has been widely investigated. Two types of quantitative simulations and two types of bisimulations between WFA are determined as solutions to particular systems of matrix and vector inequations over the field of real numbers R. The approach used in this research is unique and based on the application of a ZNN dynamical evolution in solving underlying matrix and vector inequations. This research is aimed at the development and analysis of four novel ZNN dynamical systems for addressing the systems of matrix and/or vector inequalities involved in simulations and bisimulations between WFA. The problem considered in this paper requires solving a system of two vector inequations and a couple of matrix inequations. Using positive slack matrices, required matrix and vector inequations are transformed into corresponding equations and then the derived system of matrix and vector equations is transformed into a system of linear equations utilizing vectorization and the Kronecker product. The solution to the ZNN dynamics is defined using the pseudoinverse solution of the generated linear system. A detailed convergence analysis of the proposed ZNN dynamics is presented. Numerical examples are performed under different initial state matrices. A comparison between the ZNN and linear programming (LP) approach is presented. Full article
(This article belongs to the Special Issue Advances in Fuzzy Logic and Artificial Neural Networks)
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17 pages, 1268 KiB  
Article
A Fuzzy Multi-Criteria Evaluation System for Share Price Prediction: A Tesla Case Study
by Simona Hašková, Petr Šuleř and Róbert Kuchár
Mathematics 2023, 11(13), 3033; https://doi.org/10.3390/math11133033 - 7 Jul 2023
Cited by 1 | Viewed by 2097
Abstract
The article presents the predictive capabilities of a fuzzy multi-criteria evaluation system that operates on the basis of a non-fuzzy neural approach, but also one that is capable of implementing a learning paradigm and working with vague concepts. Within this context, the necessary [...] Read more.
The article presents the predictive capabilities of a fuzzy multi-criteria evaluation system that operates on the basis of a non-fuzzy neural approach, but also one that is capable of implementing a learning paradigm and working with vague concepts. Within this context, the necessary elements of fuzzy logic are identified and the algebraic formulation of the fuzzy system is presented. It is with the help of the aforementioned that the task of predicting the short-term trend and price of the Tesla share is solved. The functioning of a fuzzy system and fuzzy neural network in the field of time series value prediction is discussed. The authors are inclined to the opinion that, despite the fact that a fuzzy neural network reacts in terms of applicability and effectiveness when solving prediction problems in relation to input data with a faster output than a fuzzy system, and is more “user friendly”, a sufficiently knowledgeable and experienced solver/expert could, by using a fuzzy system, achieve a higher speed of convergence in the learning process than a fuzzy neural network using the minimum range of input data carrying the necessary information. A fuzzy system could therefore be a possible alternative to a fuzzy neural network from the point of view of prediction. Full article
(This article belongs to the Special Issue Advances in Fuzzy Logic and Artificial Neural Networks)
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20 pages, 3445 KiB  
Article
A New Method for Commercial-Scale Water Purification Selection Using Linguistic Neural Networks
by Saleem Abdullah, Alaa O. Almagrabi and Nawab Ali
Mathematics 2023, 11(13), 2972; https://doi.org/10.3390/math11132972 - 3 Jul 2023
Cited by 5 | Viewed by 1162
Abstract
A neural network is a very useful tool in artificial intelligence (AI) that can also be referred to as an ANN. An artificial neural network (ANN) is a deep learning model that has a broad range of applications in real life. The combination [...] Read more.
A neural network is a very useful tool in artificial intelligence (AI) that can also be referred to as an ANN. An artificial neural network (ANN) is a deep learning model that has a broad range of applications in real life. The combination and interrelationship of neurons and nodes with each other facilitate the transmission of information. An ANN has a feed-forward neural network. The neurons are arranged in layers, and each layer performs a particular calculation on the incoming data. Up until the output layer, which generates the network’s ultimate output, is reached, each layer’s output is transmitted as an input to the subsequent layer. A feed-forward neural network (FFNN) is a method for finding the output of expert information. In this research, we expand upon the concept of fuzzy neural network systems and introduce feed-forward double-hierarchy linguistic neural network systems (FFDHLNNS) using Yager–Dombi aggregation operators. We also discuss the desirable properties of Yager–Dombi aggregation operators. Moreover, we describe double-hierarchy linguistic term sets (DHLTSs) and discuss the score function of DHLTSs and the distance between any two double-hierarchy linguistic term elements (DHLTEs). Here, we discuss different approaches to choosing a novel water purification technique on a commercial scale, as well as some variables influencing these approaches. We apply a feed-forward double-hierarchy linguistic neural network (FFDHLNN) to select the best method for water purification. Moreover, we use the extended version of the Technique for Order Preference by Similarity to Ideal Solution (extended TOPSIS) method and the grey relational analysis (GRA) method for the verification of our suggested approach. Remarkably, both approaches yield almost the same results as those obtained using our proposed method. The proposed models were compared with other existing models of decision support systems, and the comparison demonstrated that the proposed models are feasible and valid decision support systems. The proposed technique is more reliable and accurate for the selection of large-scale water purification methods. Full article
(This article belongs to the Special Issue Advances in Fuzzy Logic and Artificial Neural Networks)
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15 pages, 1341 KiB  
Article
Applying Neural Networks on Biometric Datasets for Screening Speech and Language Deficiencies in Child Communication
by Eugenia I. Toki, Giorgos Tatsis, Vasileios A. Tatsis, Konstantinos Plachouras, Jenny Pange and Ioannis G. Tsoulos
Mathematics 2023, 11(7), 1643; https://doi.org/10.3390/math11071643 - 29 Mar 2023
Cited by 4 | Viewed by 2320
Abstract
Screening and evaluation of developmental disorders include complex and challenging procedures, exhibit uncertainties in the diagnostic fit, and require high clinical expertise. Although typically, clinicians’ evaluations rely on diagnostic instrumentation, child observations, and parents’ reports, these may occasionally result in subjective evaluation outcomes. [...] Read more.
Screening and evaluation of developmental disorders include complex and challenging procedures, exhibit uncertainties in the diagnostic fit, and require high clinical expertise. Although typically, clinicians’ evaluations rely on diagnostic instrumentation, child observations, and parents’ reports, these may occasionally result in subjective evaluation outcomes. Current advances in artificial intelligence offer new opportunities for decision making, classification, and clinical assessment. This study explores the performance of different neural network optimizers in biometric datasets for screening typically and non-typically developed children for speech and language communication deficiencies. The primary motivation was to give clinicians a robust tool to help them identify speech disorders automatically using artificial intelligence methodologies. For this reason, in this study, we use a new dataset from an innovative, recently developed serious game collecting various data on children’s speech and language responses. Specifically, we employed different neural network approaches such as Artificial Neural Networks (ANNs), K-Nearest Neighbor (KNN), Support Vector Machines (SVM), along with state-of-the-art Optimizers, namely the Adam, the Broyden–Fletcher–Goldfarb–Shanno (BFGS), Genetic algorithm (GAs), and Particle Swarm Optimization algorithm (PSO). The results were promising, while Integer-bounded Neural Network proved to be the best competitor, opening new inquiries for future work towards automated classification supporting clinicians’ decisions on neurodevelopmental disorders. Full article
(This article belongs to the Special Issue Advances in Fuzzy Logic and Artificial Neural Networks)
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16 pages, 3291 KiB  
Article
FADS: An Intelligent Fatigue and Age Detection System
by Mohammad Hijji, Hikmat Yar, Fath U Min Ullah, Mohammed M. Alwakeel, Rafika Harrabi, Fahad Aradah, Faouzi Alaya Cheikh, Khan Muhammad and Muhammad Sajjad
Mathematics 2023, 11(5), 1174; https://doi.org/10.3390/math11051174 - 27 Feb 2023
Cited by 6 | Viewed by 2310
Abstract
Nowadays, the use of public transportation is reducing and people prefer to use private transport because of its low cost, comfortable ride, and personal preferences. However, personal transport causes numerous real-world road accidents due to the conditions of the drivers’ state such as [...] Read more.
Nowadays, the use of public transportation is reducing and people prefer to use private transport because of its low cost, comfortable ride, and personal preferences. However, personal transport causes numerous real-world road accidents due to the conditions of the drivers’ state such as drowsiness, stress, tiredness, and age during driving. In such cases, driver fatigue detection is mandatory to avoid road accidents and ensure a comfortable journey. To date, several complex systems have been proposed that have problems due to practicing hand feature engineering tools, causing lower performance and high computation. To tackle these issues, we propose an efficient deep learning-assisted intelligent fatigue and age detection system (FADS) to detect and identify different states of the driver. For this purpose, we investigated several neural computing-based methods and selected the most appropriate model considering its feasibility over edge devices for smart surveillance. Next, we developed a custom convolutional neural network-based system that is efficient for drowsiness detection where the drowsiness information is fused with age information to reach the desired output. The conducted experiments on the custom and publicly available datasets confirm the superiority of the proposed system over state-of-the-art techniques. Full article
(This article belongs to the Special Issue Advances in Fuzzy Logic and Artificial Neural Networks)
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16 pages, 4091 KiB  
Article
M-Polar Fuzzy Graphs and Deep Learning for the Design of Analog Amplifiers
by Malinka Ivanova and Mariana Durcheva
Mathematics 2023, 11(4), 1001; https://doi.org/10.3390/math11041001 - 15 Feb 2023
Viewed by 1661
Abstract
The design of analog circuits is a complex and repetitive process aimed at finding the best design variant. It is characterized by uncertainty and multivariate approaches. The designer has to make different choices to satisfy a predefined specification with required parameters. This paper [...] Read more.
The design of analog circuits is a complex and repetitive process aimed at finding the best design variant. It is characterized by uncertainty and multivariate approaches. The designer has to make different choices to satisfy a predefined specification with required parameters. This paper proposes a method for facilitating the design of analog amplifiers based on m-polar fuzzy graphs theory and deep learning. M-polar fuzzy graphs are used because of their flexibility and the possibility to model different real-life multi-attribute problems. Deep learning is applied to solve a regression task and to predict the membership functions of the m-polar fuzzy graph vertices (the solutions), taking on the role of domain experts. The performance of the learner is high since the obtained errors are very small: Root Mean Squared Error is from 0.0032 to 0.0187, Absolute Error is from 0.022 to 0.098 and Relative Error is between 0.27% and 1.57%. The proposed method is verified through the design of three amplifiers: summing amplifier, subtracting amplifier, and summing/subtracting amplifier. The method can be used for improving the design process of electronic circuits with the possibility of automating some tasks. Full article
(This article belongs to the Special Issue Advances in Fuzzy Logic and Artificial Neural Networks)
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17 pages, 761 KiB  
Article
Analysis of the Level of Adoption of Business Continuity Practices by Brazilian Industries: An Exploratory Study Using Fuzzy TOPSIS
by Vitor Amado de Oliveira Bobel, Tiago F. A. C. Sigahi, Izabela Simon Rampasso, Gustavo Hermínio Salati Marcondes de Moraes, Lucas Veiga Ávila, Walter Leal Filho and Rosley Anholon
Mathematics 2022, 10(21), 4041; https://doi.org/10.3390/math10214041 - 31 Oct 2022
Cited by 10 | Viewed by 1969
Abstract
The COVID-19 outbreak caused several negative effects in industries of all sizes and in all parts of the world, leading academic and practitioners to ask whether organizations could have been better prepared to face disruptive situations. This paper aims to analyze business continuity [...] Read more.
The COVID-19 outbreak caused several negative effects in industries of all sizes and in all parts of the world, leading academic and practitioners to ask whether organizations could have been better prepared to face disruptive situations. This paper aims to analyze business continuity practices performed by Brazilian industries. A survey was conducted with academics who work in the field of organizational resilience and business continuity and are familiar with the reality of Brazilian companies in the industrial sector. The participants assessed 16 practices (P) proposed by the ISO 22301:2020, considering two categories: large industries (LI) and small and medium-sized industries (SMI). Data analysis was performed using Hierarchical Cluster Analysis, frequency analysis, Fuzzy TOPSIS and sensitivity analysis. For LIs, P4 (leaders conduct periodic critical analyses of practices) was considered the practice with the best application rate, while for SMIs, P2 (understand stakeholders’ needs and expectations, and use information in business continuity management) was chosen. In all scenarios tested for LIs and SMIs, P8 (well-structured systematic processes to analyze the impact of abnormal situations on their business and the potential risks of a disruption) and P16 (periodic audits of their business continuity management activities to identify opportunities for improvement, and information record) are in the bottom quartile. When compared to LIs in the Brazilian context, SMIs exhibit more profound deficiencies in terms of applying business continuity practices. The findings of this study can be of great value to assist managers in improving organizational resilience. Organizations should be better prepared to face future disruptive events, whether biological, social, technological, or economic. Full article
(This article belongs to the Special Issue Advances in Fuzzy Logic and Artificial Neural Networks)
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Review

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40 pages, 4978 KiB  
Review
An Overview of Applications of Hesitant Fuzzy Linguistic Term Sets in Supply Chain Management: The State of the Art and Future Directions
by Francisco Rodrigues Lima-Junior, Mery Ellen Brandt de Oliveira and Carlos Henrique Lopes Resende
Mathematics 2023, 11(13), 2814; https://doi.org/10.3390/math11132814 - 23 Jun 2023
Cited by 8 | Viewed by 1651
Abstract
Supply chain management (SCM) encompasses a wide variety of decision-making problems that affect business and supply chain performance. Since most of these problems involve uncertainty and hesitation on the part of decision makers (DMs), various studies have emerged recently that present SCM applications [...] Read more.
Supply chain management (SCM) encompasses a wide variety of decision-making problems that affect business and supply chain performance. Since most of these problems involve uncertainty and hesitation on the part of decision makers (DMs), various studies have emerged recently that present SCM applications of techniques based on Hesitant Fuzzy Linguistic Term Sets (HFLTSs) and HFLTS extensions. Given the relevance of this subject and the lack of literature review studies, this study presents a systematic review of HFLTS and HFLTS extension applications to SCM decision-making problems. In order to answer a set of research questions, the selected papers were classified in accordance with a group of factors that are pertinent to the origins of these studies, SCM, HFLTSs, and decision making. The results demonstrated that the Source and Enable processes have been studied with greater frequency, while the most common problems have to do with supplier selection, failure evaluation, and performance evaluation. The companies of the automotive sector predominated in the analyzed studies. Even though most of the studies used techniques based on HFLTSs, we identified applications of seven distinct HFLTS extensions. The main contribution of this study consists of presenting an overview of the use of HFLTSs and their extensions in practical examples of SCM, highlighting trends and research opportunities. It is the first study to analyze applications of decision-making techniques that deal with hesitation in SCM. Therefore, the results can help researchers and practitioners develop new studies that involve the use of HFLTSs and HFLTS extensions in decision-making problems, given that this study systematizes elements that should be considered in the modeling, application, and validation of these methods. Full article
(This article belongs to the Special Issue Advances in Fuzzy Logic and Artificial Neural Networks)
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