Recent Advances and Applications of Fractional-Order Neural Networks

Authors

  • Monalisa Maiti Vellore Institute of Technology
  • Sunder M Vellore Institute of Technology
  • Abishek R Vellore Institute of Technology
  • Kishore Bingi Vellore Institute of Technology
  • Nagoor Basha Shaik Chulalongkorn University
  • Watit Benjapolakul Chulalongkorn University

DOI:

https://doi.org/10.4186/ej.2022.26.7.49

Keywords:

cellular networks, control systems, fractional calculus, Hopfield networks, identification, memristive neural networks

Abstract

This paper focuses on the growth, development, and future of various forms of fractional-order neural networks. Multiple advances in structure, learning algorithms, and methods have been critically investigated and summarized. This also includes the recent trends in the dynamics of various fractional-order neural networks. The multiple forms of fractional-order neural networks considered in this study are Hopfield, cellular, memristive, complex, and quaternion-valued based networks. Further, the application of fractional-order neural networks in various computational fields such as system identification, control, optimization, and stability have been critically analyzed and discussed.

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Author Biographies

Monalisa Maiti
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School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India

Sunder M
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School of Electrical Engineering, Vellore Institute of Technology, Vellore, India

Abishek R
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School of Electrical Engineering, Vellore Institute of Technology, Vellore, India

Kishore Bingi
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School of Electrical Engineering, Vellore Institute of Technology, Vellore, India

Nagoor Basha Shaik
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Artificial Intelligence, Machine Learning, and Smart Grid Technology Research Unit, Department of Electrical Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok 10330, Thailand

Watit Benjapolakul
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Artificial Intelligence, Machine Learning, and Smart Grid Technology Research Unit, Department of Electrical Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok 10330, Thailand

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Published In
Vol 26 No 7, Jul 31, 2022
How to Cite
[1]
M. Maiti, S. M, A. R, K. Bingi, N. B. Shaik, and W. Benjapolakul, “Recent Advances and Applications of Fractional-Order Neural Networks”, Eng. J., vol. 26, no. 7, pp. 49-67, Jul. 2022.
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