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Dynamics in Complex Neural Networks, 2nd Edition

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Complexity".

Deadline for manuscript submissions: 15 July 2025 | Viewed by 846

Special Issue Editors


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Guest Editor
Department of Mathematics, University of Texas at San Antonio, One UTSA Circle, San Antonio, TX 78249, USA
Interests: applied mathematics; dynamical systems; differential equations; qualitative properties (almost periodicity, invariant manifolds, asymptotic properties, stability); impulsive perturbations; delays; fractional differential equations; neural networks; economic models
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Engineering Design, Technical University of Sofia, 1000 Sofia, Bulgaria
Interests: engineering design; emotional design; neural network models
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Mathematical Physics, Technical University of Sofia, 8800 Sliven, Bulgaria
Interests: dynamical systems; differential equations; neural network models

Special Issue Information

Dear Colleagues,

Complex neural network systems are essential tools investigated and applied by academic researchers and industry. Recent advances in computer sciences, robotics, and mathematics have introduced new technologies and expanded the opportunities for neural network applications. The knowledge and understanding of these technologies have led to the development of new models, novel methods, and extending the existing techniques for analysis of the neural network dynamics.

Original research articles that will contribute to the development of the theory of complex neural network systems are invited. The focus will be on models as well as methods that explore aspects of dynamics in complex neural networks. Experimental and applied research results are also welcomed. Potential topics of the Special Issue include, but are not limited to, the following:

  • Stability theory and strategies;
  • Control theory;
  • Extended stability strategies;
  • Delayed neural networks;
  • Impulsive neural networks;
  • Fractional-order neural networks;
  • Cohen–Grossberg neural networks;
  • Reaction–diffusion neural networks;
  • Biological neural network models;
  • Bifurcation strategies;
  • Entropy in complex neural networks;
  • Asymptotic behavior;
  • Applications in science and engineering.

Dr. Ivanka Stamova
Dr. Gani Stamov
Dr. Trayan Stamov
Dr. Cvetelina Spirova
Guest Editors

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. Entropy is an international peer-reviewed open access monthly 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

  • stability theory and strategies
  • control theory
  • extended stability strategies
  • delayed neural networks
  • impulsive neural networks
  • fractional-order neural networks

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Published Papers (1 paper)

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Research

21 pages, 335 KiB  
Article
On the Global Practical Exponential Stability of h-Manifolds for Impulsive Reaction–Diffusion Cohen–Grossberg Neural Networks with Time-Varying Delays
by Gani Stamov, Trayan Stamov, Ivanka Stamova and Cvetelina Spirova
Entropy 2025, 27(2), 188; https://doi.org/10.3390/e27020188 - 12 Feb 2025
Viewed by 612
Abstract
In this paper, we focus on h-manifolds related to impulsive reaction–diffusion Cohen–Grossberg neural networks with time-varying delays. By constructing a new Lyapunov-type function and a comparison principle, sufficient conditions that guarantee the global practical exponential stability of specific states are established. The [...] Read more.
In this paper, we focus on h-manifolds related to impulsive reaction–diffusion Cohen–Grossberg neural networks with time-varying delays. By constructing a new Lyapunov-type function and a comparison principle, sufficient conditions that guarantee the global practical exponential stability of specific states are established. The states of interest are determined by the so-called h-manifolds, i.e., manifolds defined by a specific function h, which is essential for various applied problems in imposing constraints on their dynamics. The established criteria are less restrictive for the variable domain and diffusion coefficients. The effect of some uncertain parameters on the stability behavior is also considered and a robust practical stability analysis is proposed. In addition, the obtained h-manifolds’ practical stability results are applied to a bidirectional associative memory (BAM) neural network model with impulsive perturbations and time-varying delays. Appropriate examples are discussed. Full article
(This article belongs to the Special Issue Dynamics in Complex Neural Networks, 2nd Edition)
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