Theory and Applications of Fuzzy Systems and Neural Networks: 2nd Edition

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 15 May 2025 | Viewed by 1335

Special Issue Editors


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Guest Editor
School of Computing, University of Portsmouth, Portsmouth PO1 2UP, UK
Interests: artificial intelligence; machine learning methods using fuzzy systems; neural networks and evolutionary algorithms
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
School of Computing, University of Portsmouth, Portsmouth PO1 2UP, UK
Interests: fuzzy logic; artificial intelligence; machine learning; AI and ML applications; intelligent transportation systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Fuzzy systems and neural networks are the main theoretical approaches in computational intelligence. These approaches have been successfully applied in a wide range of fields such as information science, mathematics, control engineering, image processing, pattern recognition, robotics, mechatronics, consumer electronics, and system optimisation. They provide an effective tool for data and knowledge-based modelling as well as dealing with many real-world problems with quantitative and qualitative complexity in terms of dimensionality and uncertainty.

Fuzzy systems and neural networks complement each other very well and can also be combined with other computational and artificial-intelligence-based techniques such as evolutionary algorithms and machine learning to solve complex real-world problems. The integration of fuzzy systems and neural networks, in particular, can bring out the best of both approaches and usually provides better system performance in terms of modelling efficiency and accuracy.

This Special Issue aims to publish original research of the highest scientific quality related to the theory and applications of fuzzy systems and neural networks. We invite original and unpublished submissions that feature innovative methods for enhancing fuzzy systems and neural networks. The scope includes theoretical and experimental studies that contribute to novel developments in fundamental research and its applications.

Potential topics for submissions include, but are not limited to, the following:

  • Fuzzy systems for modelling and simulation;
  • Neural networks for modelling and simulation;
  • Fuzzy systems for classification and recognition;
  • Neural networks for classification and recognition;
  • Neuro fuzzy systems;
  • Fuzzy neural networks;
  • PD fuzzy and neural control;
  • PID fuzzy and neural control;
  • Model based fuzzy and neural control.

The technical program committee member is as follows:

Dr. Sina Razvarz
Departamento de  Control Automatico, CINVESTAV-IPN (National Polytechnic Institute), Mexico City 07360, Mexico

Dr. Raheleh Jafari
Dr. Alexander Gegov
Dr. Farzad Arabikhan
Guest Editors

Manuscript Submission Information

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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. Electronics 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 2400 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 systems
  • neural networks
  • intelligent systems
  • computational intelligence
  • artificial intelligence
  • machine learning
 

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Published Papers (1 paper)

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33 pages, 2210 KiB  
Article
Online Three-Dimensional Fuzzy Reinforcement Learning Modeling for Nonlinear Distributed Parameter Systems
by Xianxia Zhang, Runbin Yan, Gang Zhou, Lufeng Wang and Bing Wang
Electronics 2024, 13(21), 4217; https://doi.org/10.3390/electronics13214217 - 27 Oct 2024
Viewed by 567
Abstract
Distributed parameter systems (DPSs) frequently appear in industrial manufacturing processes, with complex characteristics such as time–space coupling, nonlinearity, infinite dimension, uncertainty and so on, which is full of challenges to the modeling of the system. At present, most DPS modeling methods are offline. [...] Read more.
Distributed parameter systems (DPSs) frequently appear in industrial manufacturing processes, with complex characteristics such as time–space coupling, nonlinearity, infinite dimension, uncertainty and so on, which is full of challenges to the modeling of the system. At present, most DPS modeling methods are offline. When the internal parameters or external environment of DPS change, the offline model is incapable of accurately representing the dynamic attributes of the real system. Establishing an online model for DPS that accurately reflects the real-time dynamics of the system is very important. In this paper, the idea of reinforcement learning is creatively integrated into the three-dimensional (3D) fuzzy model and a reinforcement learning-based 3D fuzzy modeling method is proposed. The agent improves the strategy by continuously interacting with the environment, so that the 3D fuzzy model can adaptively establish the online model from scratch. Specifically, this paper combines the deterministic strategy gradient reinforcement learning algorithm based on an actor critic framework with a 3D fuzzy system. The actor function and critic function are represented by two 3D fuzzy systems and the critic function and actor function are updated alternately. The critic function uses a TD (0) target and is updated via the semi-gradient method; the actor function is updated by using the chain derivation rule on the behavior value function and the actor function is the established DPS online model. Since DPS modeling is a continuous problem, this paper proposes a TD (0) target based on average reward, which can effectively realize online modeling. The suggested methodology is implemented on a three-zone rapid thermal chemical vapor deposition reactor system and the simulation results demonstrate the efficacy of the methodology. Full article
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