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

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

Sunder M

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

Abishek R

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

Kishore Bingi

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

Nagoor Basha Shaik

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

Watit Benjapolakul

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.