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Event-based nonfragile state estimation for memristive recurrent neural networks with stochastic cyber-attacks and sensor saturations |
| Xiao-Guang Shao(邵晓光)1, Jie Zhang(张捷)1,†, and Yan-Juan Lu(鲁延娟)2 |
1 School of Automation, Nanjing University of Science and Technology, Nanjing 210094, China;
2 School of Electronics and Information Engineering, Changchun University of Science and Technology, Changchun 130022, China |
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Abstract This paper addresses the issue of nonfragile state estimation for memristive recurrent neural networks with proportional delay and sensor saturations. In practical engineering, numerous unnecessary signals are transmitted to the estimator through the networks, which increases the burden of communication bandwidth. A dynamic event-triggered mechanism, instead of a static event-triggered mechanism, is employed to select useful data. By constructing a meaningful Lyapunov-Krasovskii functional, a delay-dependent criterion is derived in terms of linear matrix inequalities for ensuring the global asymptotic stability of the augmented system. In the end, two numerical simulations are employed to illustrate the feasibility and validity of the proposed theoretical results.
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Received: 23 January 2024
Revised: 28 February 2024
Accepted manuscript online: 12 April 2024
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PACS:
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02.30.Yy
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(Control theory)
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02.30.Ks
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(Delay and functional equations)
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07.05.Dz
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(Control systems)
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Corresponding Authors:
Jie Zhang
E-mail: jie_zhang_njust@163.com
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Cite this article:
Xiao-Guang Shao(邵晓光), Jie Zhang(张捷), and Yan-Juan Lu(鲁延娟) Event-based nonfragile state estimation for memristive recurrent neural networks with stochastic cyber-attacks and sensor saturations 2024 Chin. Phys. B 33 070203
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