Advanced Research in Fuzzy System and Neural Networks

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Network Science".

Deadline for manuscript submissions: 31 August 2024 | Viewed by 1157

Special Issue Editor


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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

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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

Published Papers (1 paper)

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Research

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 823
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)
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