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Mathematics
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Advances in Fuzzy Logic and Artificial Neural Networks, 2nd Edition
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Advances in Fuzzy Logic and Artificial Neural Networks, 2nd Edition

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "D2: Operations Research and Fuzzy Decision Making".

Deadline for manuscript submissions: 31 August 2026 | Viewed by 3093

Special Issue Editor

Prof. Dr. Francisco Rodrigues Lima-Junior

E-Mail Website
Guest Editor
Department of Management and Economics, Federal Technological University of Paraná, Curitiba 80230-901, Brazil
Interests: fuzzy logic; artificial neural network; decision making; operations management
Special Issues, Collections and Topics in MDPI journals

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 250 words) can be sent to the Editorial Office for assessment.

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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Related Special Issue

Published Papers (3 papers)

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Research

32 pages, 1506 KB  
Article
A Fuzzy Satisfaction-Based Intelligent Framework for Multiobjective Design of a Buck DC-DC Converter Under Uncertain Operating Conditions
by Nikolay Hinov, Reni Kabakchieva and Plamen Stanchev
Mathematics 2026, 14(7), 1115; https://doi.org/10.3390/math14071115 - 26 Mar 2026
Viewed by 428
Abstract
This paper presents a fuzzy satisfaction-based intelligent framework for early-stage multiobjective sizing of a buck DC–DC converter under uncertain operating conditions. Lightweight closed-form estimators are used to evaluate inductor current ripple, output voltage ripple, and efficiency, including an explicit decomposition of ripple into [...] Read more.
This paper presents a fuzzy satisfaction-based intelligent framework for early-stage multiobjective sizing of a buck DC–DC converter under uncertain operating conditions. Lightweight closed-form estimators are used to evaluate inductor current ripple, output voltage ripple, and efficiency, including an explicit decomposition of ripple into capacitive and ESR-induced components to distinguish capacitance-dominated and ESR-dominated regimes. Engineering targets for ripple, efficiency, and passive size/cost pressure are mapped to reproducible piecewise membership functions and aggregated into a bounded overall satisfaction score using a weighted geometric operator; alternative non-compensatory and OWA-type aggregators are considered for sensitivity analysis. The resulting nonconvex design problem is solved via a compact two-stage derivative-free strategy that combines global screening with an interpretable Takagi–Sugeno (TSK) rule-based refinement layer, which generates bounded, physics-consistent updates of the design variables and supports rapid feasibility restoration followed by preference-driven tuning. Uncertainty in operating conditions and parameter drift is addressed through scenario evaluation and worst-case or average-case aggregation of satisfaction, linking the fuzzy decision objective to robust scenario design. Numerical studies for a 24 ± 4 V to 12 V converter illustrate regime-dependent adaptation: in low-ESR conditions, ripple improvement is driven mainly by capacitance/frequency adjustments, while in high-ESR conditions, the rule base shifts corrections toward inductor and frequency choices that reduce ESR-dominated ripple. Full article
(This article belongs to the Special Issue Advances in Fuzzy Logic and Artificial Neural Networks, 2nd Edition)
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25 pages, 2562 KB  
Article
Mathematically Grounded Neuro-Fuzzy Control of IoT-Enabled Irrigation Systems
by Nikolay Hinov, Reni Kabakchieva, Daniela Gotseva and Plamen Stanchev
Mathematics 2026, 14(2), 314; https://doi.org/10.3390/math14020314 - 16 Jan 2026
Viewed by 522
Abstract
This paper develops a mathematically grounded neuro-fuzzy control framework for IoT-enabled irrigation systems in precision agriculture. A discrete-time, physically motivated model of soil moisture is formulated to capture the nonlinear water dynamics driven by evapotranspiration, irrigation, and drainage in the crop root zone. [...] Read more.
This paper develops a mathematically grounded neuro-fuzzy control framework for IoT-enabled irrigation systems in precision agriculture. A discrete-time, physically motivated model of soil moisture is formulated to capture the nonlinear water dynamics driven by evapotranspiration, irrigation, and drainage in the crop root zone. A Mamdani-type fuzzy controller is designed to approximate the optimal irrigation strategy, and an equivalent Takagi–Sugeno (TS) representation is derived, enabling a rigorous stability analysis based on Input-to-State Stability (ISS) theory and Linear Matrix Inequalities (LMIs). Online parameter estimation is performed using a Recursive Least Squares (RLS) algorithm applied to real IoT field data collected from a drip-irrigated orchard. To enhance prediction accuracy and long-term adaptability, the fuzzy controller is augmented with lightweight artificial neural network (ANN) modules for evapotranspiration estimation and slow adaptation of membership-function parameters. This work provides one of the first mathematically certified neuro-fuzzy irrigation controllers integrating ANN-based estimation with Input-to-State Stability (ISS) and LMI-based stability guarantees. Under mild Lipschitz continuity and boundedness assumptions, the resulting neuro-fuzzy closed-loop system is proven to be uniformly ultimately bounded. Experimental validation in an operational IoT setup demonstrates accurate soil-moisture regulation, with a tracking error below 2%, and approximately 28% reduction in water consumption compared to fixed-schedule irrigation. The proposed framework is validated on a real IoT deployment and positioned relative to existing intelligent irrigation approaches. Full article
(This article belongs to the Special Issue Advances in Fuzzy Logic and Artificial Neural Networks, 2nd Edition)
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33 pages, 4143 KB  
Article
An Approach for Sustainable Supplier Segmentation Using Adaptive Network-Based Fuzzy Inference Systems
by Ricardo Antonio Saugo, Francisco Rodrigues Lima Junior, Luiz Cesar Ribeiro Carpinetti, Ana Paula Duarte and Jurandir Peinado
Mathematics 2025, 13(19), 3058; https://doi.org/10.3390/math13193058 - 23 Sep 2025
Cited by 1 | Viewed by 924
Abstract
Due to the globalization of supply chains and the resulting increase in the quantity and diversity of suppliers, the segmentation of suppliers has become fundamental to improving relationship management and supplier performance. Moreover, given the need to incorporate sustainability into supply chain management, [...] Read more.
Due to the globalization of supply chains and the resulting increase in the quantity and diversity of suppliers, the segmentation of suppliers has become fundamental to improving relationship management and supplier performance. Moreover, given the need to incorporate sustainability into supply chain management, criteria based on economic, environmental, and social performance have been adopted for evaluating suppliers. However, few studies present sustainable supplier segmentation models in the literature, and none of them make it possible to predict individual supplier performance for each TBL dimension in a non-compensatory manner. Moreover, none of them permits the use of historical performance data to adapt the model to the usage environment. Given this, this study aims to propose a decision-making model for sustainable supplier segmentation using an adaptive network-based fuzzy inference system (ANFIS). Our approach combines three ANFIS computational models in a tridimensional quadratic matrix, based on diverse criteria associated with the triple bottom line (TBL) dimensions. A pilot application of this model in a sugarcane mill was performed. We implemented 108 candidate topologies using the Neuro-Fuzzy Designer of the MATLAB® software (R2014a). The cross-validation method was applied to select the best topologies. The accuracy of the selected topologies was confirmed using statistical tests. The proposed model can be adopted for supplier segmentation processes in companies that wish to monitor and/or improve the sustainability performance of their suppliers. This study may also be helpful to researchers in developing computational solutions for segmenting suppliers, mainly regarding ANFIS modeling and providing appropriate topological parameters to obtain accurate results. Full article
(This article belongs to the Special Issue Advances in Fuzzy Logic and Artificial Neural Networks, 2nd Edition)
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