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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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[1] Yang C, Peng G, Li Y, et al. 2008 IEEE Trans. Cybernet. 49 2568
[2] Vecsei P M, Choo K, Chang J, et al. 2019 Phys. Rev. B 99 245120
[3] Qin C, Schlemper J, Caballero J, et al. 2019 IEEE Trans. Med. Imaging 18 280
[4] Strukov D B, Snider G S, Stewart D R, et al. 2022 Chin. Phys. B 31 100201
[5] Qiu S B, Liu X G, Wang F X, et al. 2018 Neural Comput. Appl. 30 211
[6] Nagamani G, Adhira B and Soundararajan G 2021 Nonlinear Dynam. 104 451
[7] Chua L 1971 Memristor — The missing circuit element, circuit theory (New York: IEEE) p. 507
[8] Guo T, Sun B, Ranjan S, et al. 2020 ACS Appl. Mater. Interfaces 12 54243
[9] Tsafack N, Iliyasu A M, De Dieu N J, et al. 2021 J. Inf. Secur. Appl. 61 2214
[10] Duan S, Hu X, Dong Z, et al. 2015 IEEE Trans. Neural Netw. Learn. Syst. 13 68
[11] Strukov D B, Snider G S, Stewart D R, et al. 2008 Nature 453 80
[12] Zhang X M, Han Q L and Ge X 2019 Inform. Sci. 478 83
[13] Li J, Dong H, Wang Z, et al. 2020 IEEE Trans. Neural Netw. Learning 31 3747
[14] Wang Z, Ho D W C and Liu X 2005 IEEE Trans. Neural Netw. Learning 16 279
[15] Lee S C M, Lui J C S and Yau D K Y 2004 IEEE Trans. Parall. Distr. 15 385
[16] Tang Z, Park J H and Wang Y 2019 IEEE Trans. Cybernet. 49 3105
[17] Zhang Y and Zhou L 2022 Neural Comput. Appl. 34 2193
[18] Zhou L 2018 Neurocomputing 308 235
[19] Mu C, Liao K and Wang K 2021 Nonlinear Dynam. 103 2645
[20] Zha L, Fang J, Liu J, et al. 2018 Neurocomputing 273 1
[21] Wang F and Yang Y 2017 Int. J. Syst. Sci. 48 571
[22] Huong D C and Trinh H 2022 Nature 32 6267
[23] Deng Y, Mo Z and Lu H 2022 Chin. Phys. B 31 020503
[24] Liu G, Park J H, Xu S, et al. 2019 Nonlinear Anal-Hybrid Systems 32 65
[25] Liu J, Xia J, Cao J, et al. 2018 Neurocomputing 291 35
[26] Wang X, Park J H, Liu Z, et al. 2022 IEEE Trans. Neural Netw. Learning 45 1
[27] Filippov A F 1988 1960 Matematicheskii Sbornik 1 99
[28] Aubin J P and Frankowska H 2009 Set-valued analysis (Berlin: Springer)
[29] Zhang R, Zeng D, Zhong S, et al. 2017 Appl. Math. Comput. 310 57
[30] Wang Z, Ding S, Huang Z and Zhang H 2016 IEEE Trans. Neural Netw. Learn. Syst. 27 2337
[31] Gu Z, Yue D, Liu J, et al. 2017 J. Franklin Inst. 354 3540
[32] Tan Y, Xiong M, Zhang B, et al. 2022 IEEE Trans. Syst. Man Cybernet. A 52 2121
[33] Chen X, Yin L Y, Liu Y T, et al. 2019 Chin. Phys. B 28 090701
[34] Hu S and Yue D 2012 ISA Trans. 51 153
[35] Boyd S, El Ghaoui L, Feron E, et al. 1994 Linear matrix inequalities in system and control theory (Philadelphia: SIAM) p. 106
[36] Park P G, Ko J W and Jeong C 2011 Automatica 47 235
[37] Sakthivel R, Anbuvithya R, Mathiyalagan K, et al. 2015 Neurocomputing 168 1111
[38] Yang F and Li Y 2009 Automatica 45 1896
[39] Wen S, Huang T, Zeng Z, et al. 2015 Neural Netw. 63 48
[40] Qian W, Liu H, Zhao Y, et al. 2022 Appl. Math. Comput. 424 127016 |
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