Fixed-time synchronization of complex-valued memristive BAM neural network and 
applications in image encryption and decryption

Guo, Y.; Luo, Y.; Wang, W.; Luo, X.; Ge, C.; Kurths, Jürgen; Yuan, M.; Gao, Y.

doi:10.1007/s12555-018-0676-7

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Fixed-time synchronization of complex-valued memristive BAM neural network and applications in image encryption and decryption

Authors

Guo, Y.
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Luo, Y.
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Wang, W.
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Luo, X.
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Ge, C.
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/persons/resource/Juergen.Kurths

Kurths, Jürgen
Potsdam Institute for Climate Impact Research;

Yuan, M.
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Gao, Y.
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Citation

Guo, Y., Luo, Y., Wang, W., Luo, X., Ge, C., Kurths, J., Yuan, M., Gao, Y. (2020): Fixed-time synchronization of complex-valued memristive BAM neural network and applications in image encryption and decryption. - International Journal of Control, Automation and Systems, 18, 2, 462-476.
https://doi.org/10.1007/s12555-018-0676-7

Cite as: https://publications.pik-potsdam.de/pubman/item/item_23455

Abstract

This paper focuses on the dynamical characteristics of complex-valued memristor-based BAM neural network (CVMBAMNN) with leakage time-varying delay. With two different controllers, we have obtained fixedtime and finite-time synchronization criteria respectively in complex domain for our special model, which few work has studied before. Since fixed-time synchronous system can improve communication security, we designed a scheme for RGB image encryption and decryption. In order to satisfy the requirement of much lower error in image secure communication, our approach can get the error of fixed-time synchronization to about 1×10−13. Due to our highly consistent system, we do get good encryption and decryption effect with encryption and decryption scheme. Finally, numerical simulations are included to demonstrate the correctness of our theoretical results.