TY - JOUR
T1 - Projective Multi-Synchronization of Fractional-order Complex-valued Coupled Multi-stable Neural Networks with Impulsive Control
AU - Udhayakumar, K.
AU - Rakkiyappan, R.
AU - Rihan, Fathalla A.
AU - Banerjee, Santo
N1 - Funding Information:
The authors would like to thank the editor and reviewers for their valuable and constructive comments which improved the quality of this manuscript. This work was funded by the project of fund -UPAR, UAE University (UAE).
Publisher Copyright:
© 2021 Elsevier B.V.
PY - 2022/1/7
Y1 - 2022/1/7
N2 - In this paper, we study a projective multi-synchronization problem for fractional-order complex-valued coupled multi-stable neural networks (FCVCMNNs) with time-delays. Using a complex decomposition approach, FCVCMNNs are divided into their real and imaginary components. Our method uses certain conditions for each subnetwork to achieve the multiple local equilibrium points or stable periodic orbits that are exponentially stable, which, when combined with the Lyapunov functions method, result in FCVCMNNs that are projectively multi-synchronized. The FCVCMNNs, on the other hand, are examined directly through the use of the Lyapunov functional method and linear matrix inequality (LMI). Various new sufficient conditions in the form of complex-valued LMIs are presented for the projective multi-synchronization of the considered FCVCMNNs. As a final step, we provide two numerical simulations to verify the effectiveness of the main results derived in this paper.
AB - In this paper, we study a projective multi-synchronization problem for fractional-order complex-valued coupled multi-stable neural networks (FCVCMNNs) with time-delays. Using a complex decomposition approach, FCVCMNNs are divided into their real and imaginary components. Our method uses certain conditions for each subnetwork to achieve the multiple local equilibrium points or stable periodic orbits that are exponentially stable, which, when combined with the Lyapunov functions method, result in FCVCMNNs that are projectively multi-synchronized. The FCVCMNNs, on the other hand, are examined directly through the use of the Lyapunov functional method and linear matrix inequality (LMI). Various new sufficient conditions in the form of complex-valued LMIs are presented for the projective multi-synchronization of the considered FCVCMNNs. As a final step, we provide two numerical simulations to verify the effectiveness of the main results derived in this paper.
KW - Complex-valued neural networks
KW - Coupled neural networks
KW - Fractional-order
KW - Impulsive control
KW - Multi-synchronization
KW - Projective synchronization
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U2 - 10.1016/j.neucom.2021.10.003
DO - 10.1016/j.neucom.2021.10.003
M3 - Article
AN - SCOPUS:85117374441
SN - 0925-2312
VL - 467
SP - 392
EP - 405
JO - Neurocomputing
JF - Neurocomputing
ER -