Distinguished EPJ Referees

EPJ ST Special Issue: Chaotic variable-order fractional neural networks

Neural networks have an inevitable effect on world life. The role of neural networks can be observed in various subjects of sciences and engineering including associative memory, image processing, solving linear and nonlinear programming, computing technology and so forth. It has been demonstrated that if the parameters of networks are appropriately chosen, the neural networks can show chaotic behaviors and complicated dynamics. As it is obvious, due to the broadband application of chaotic neural networks as well as their random-like behavior, the development of novel methods, new models, and extending the existing techniques for analysis of these systems is of crucial importance, and more studies must be carried out in this field of study.

Using trustable tools for modeling of real-world phenomena can be considered as one of the most critical challenges for scientists. Fractional calculus provides a suitable facility to reach this goal. This way, mathematical modeling, dynamical investigation, and applications of fractional-order chaotic systems have recently attracted a lot of attention. In comparison with integer-order derivatives, the constant-order fractional ones have benefits in describing the long memory of systems. However, recently, some studies have proven that several engineering and scientific phenomena cannot be precisely illustrated by means of constant fractional derivatives. For instance, in some particular physical models, systems exhibit the change of memory property over time. Therefore, By using a bounded function depends on time, we could expand the conventional fractional derivative to be the so-called variable-order fractional derivative (VOFD).

This way, the current special issue aims to explore recent trends and developments in the modeling, analysis, synchronization, and practical application of chaotic variable-order fractional neural networks.

The topics of interest include – but not limited to– the following:

  • Modeling and analysis of new chaotic variable-order fractional neural networks
  • Applications of chaotic variable-order fractional neural networks in all areas of engineering and physics
  • Chaotic neural networks with special features
  • Novel synchronization and anti-synchronization techniques for variable-order fractional neural networks
  • Experimental validation of chaotic variable-order fractional neural networks

Manuscripts should be prepared following the instructions for authors using the latex template of EPJ ST, which can be downloaded here. Articles may be one of four types: (i) minireviews (10-15 pages); (ii) tutorial reviews (15+ pages), (iii) original paper v1 (5-10 pages), or (iv) original paper v2 (3-5 pages). Articles should be submitted to the Editorial Office of EPJ ST via the submission system and clearly mentioning the title of the special issue.

Submission Deadline: March 30, 2021.

Guest Editors

Oscar Castillo, Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana, Mexico, This email address is being protected from spambots. You need JavaScript enabled to view it.

Hadi Jahanshahi, Department of Mechanical Engineering, University of Manitoba, Winnipeg R3T 5V6, Canada, This email address is being protected from spambots. You need JavaScript enabled to view it.

Amin Yousefpour, School of Mechanical Engineering, College of Engineering, University of Tehran, Tehran, 11155¬4563, Iran, This email address is being protected from spambots. You need JavaScript enabled to view it.

Open calls for papers