Advanced Research in Fuzzy System and Neural Networks

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E: Applied Mathematics".

Deadline for manuscript submissions: 20 June 2025 | Viewed by 2132

Special Issue Editor


E-Mail Website
Guest Editor
Facultad de Ingeniería, Universidad Autónoma de Chihuahua, Chihuahua 31125, Mexico
Interests: neural network; fuzzy logic; hybrid intelligent systems; soft computing; optimization bio-inspired algorithms

Special Issue Information

Dear Colleagues,

The development of fuzzy logic systems is still little-known, but there have been numerous advances in this area. Researchers, academics and practitioners have granted considerable attention to fuzzy logic systems, which has been applied in a variety of fields. Furthermore, fuzzy logic is capable of working with uncertain data, and its several extensions can be applied to a wide range of evaluation and decision-making problems. Many domains, including the sciences, engineering, economics, and management, have also incorporated fuzzy logic systems and their extensions into their evaluation and decision-making processes.

Neural networks, on the other hand, are a relatively novel paradigm that offers models and techniques for handling incomplete and/or uncertain information regarding real-world problems. The area of neural networks has been widely researched, generating the development of multiple variations in the original model of the perceptron neural network, such as the feed forward neural network, convolutional neural network, recurrent neural network, neuro-fuzzy networks, and more; these approaches offer a wide array of theoretical and practical tools for advanced research.

This Special Issue aims to publish original or review articles that focus on the current advances, methodologies, and applications of fuzzy systems and neural networks. Special attention will be paid to research works that address practical problems regarding the application of evaluation and decision-making methods in fuzzy modeling, neural networks and neuro-fuzzy networks. Contributions that attend to the following topics are particularly welcome.

Prof. Dr. Fernando Gaxiola
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 logic
  • fuzzy systems
  • feed forward neural network
  • multilayer perception
  • convolutional neural network
  • recurrent neural network
  • LSTM—long short-term memory network
  • sequence to sequence models
  • modular neural network
  • neuro-fuzzy networks
  • application of soft computing models

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (2 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

22 pages, 1296 KiB  
Article
Interpretable Process Monitoring Using Data-Driven Fuzzy-Based Models for Wastewater Treatment Plants
by Rodrigo Salles, Miguel Proença, Rui Araújo, Jorge S. S. Júnior and Jérôme Mendes
Mathematics 2025, 13(10), 1691; https://doi.org/10.3390/math13101691 - 21 May 2025
Abstract
Digital transformation of industry has gained emphasis in recent years in academia and industry. Organizations need to be more competitive and efficient and improve their processes and performance to cope with changes in environmental legislation, efficient management of resources and energy, and the [...] Read more.
Digital transformation of industry has gained emphasis in recent years in academia and industry. Organizations need to be more competitive and efficient and improve their processes and performance to cope with changes in environmental legislation, efficient management of resources and energy, and the trend toward zero waste. These factors have led to the emergence of a new concept. This paper studies data-driven fuzzy-based models for process monitoring focused on Wastewater Treatment Plants (WWTPs). This work aims to study interpretable industrial process monitoring models, which must be easily interpretable by expert process operators. For this purpose, different fuzzy-based models were studied. Exhaustive validations are performed. The studied models employ 16 key variables at 14 different points throughout the waterline of a treatment plant. The learning and testing of each model for every key variable at each involved point use distinct sets of input variables and varied learning model parameters. The impact of the selected input variables and the learning parameters on the model accuracy, and the accuracy versus interpretability tradeoff are analyzed. The best model for each key variable is developed based on the accuracy versus interpretability tradeoff. Full article
(This article belongs to the Special Issue Advanced Research in Fuzzy System and Neural Networks)
Show Figures

Figure 1

18 pages, 8473 KiB  
Article
Self-Evolving Chebyshev Radial Basis Function Neural Complementary Sliding Mode Control
by Lei Zhang, Xiangguo Li and Juntao Fei
Mathematics 2023, 11(14), 3231; https://doi.org/10.3390/math11143231 - 22 Jul 2023
Viewed by 1355
Abstract
A novel intelligent complementary sliding mode control (ICSMC) method is proposed for nonlinear systems with unknown uncertainties in this paper. A self-evolving Chebyshev radial basis function neural network (RBFNN) (SECRBFNN) with self-learning parameters and structure is proposed and combined with complementary sliding mode [...] Read more.
A novel intelligent complementary sliding mode control (ICSMC) method is proposed for nonlinear systems with unknown uncertainties in this paper. A self-evolving Chebyshev radial basis function neural network (RBFNN) (SECRBFNN) with self-learning parameters and structure is proposed and combined with complementary sliding mode control (CSMC). CSMC not only has the advantages of the strong robustness of traditional SMC but also has certain advantages in reducing chattering and control accuracy. The SECRBFNN, which combines the advantages of the Chebyshev network (CN) and an RBFNN, is used to estimate unknown uncertainties in nonlinear systems. Meanwhile, a node self-evolution mechanism is proposed to avoid redundancy in the number of neurons. Eventually, the detailed simulation results demonstrate the feasibility and superiority of the proposed method. Full article
(This article belongs to the Special Issue Advanced Research in Fuzzy System and Neural Networks)
Show Figures

Figure 1

Back to TopTop