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Periodic dynamics for nonlocal Hopfield neural networks with random initial data. (English) Zbl 1472.93074

Summary: In this paper, a class of nonlocal Hopfield neural networks with random initial data is introduced, where the randomness may be of probability uncertainty. Sufficient conditions are derived to ensure the existence and globally exponential convergence of periodic solution for the addressed system in the frame of nonlinear expectation and linear expectation, respectively. Moreover, numerical examples are given to show the effectiveness of the obtained results.

MSC:

93C15 Control/observation systems governed by ordinary differential equations
34C25 Periodic solutions to ordinary differential equations
93E03 Stochastic systems in control theory (general)
93B70 Networked control
Full Text: DOI

References:

[1] Hopfield, J. J., Neural networks and physical systems with emergent collective computational abilities, Proc. Nat. Acad. Sci. USA, 79, 8, 2554-2558 (1982) · Zbl 1369.92007
[2] Joya, G.; Atencia, M.; Sandoval, F., Hopfield neural networks for optimization: Study of the different dynamics, Neurocomputing, 43, 219-237 (2002) · Zbl 1016.68076
[3] Driessche, P. V.D.; Zou, X. F., Global attractivity in delayed Hopfield neural networks models, SIAM J. Appl. Math., 58, 6, 1878-1890 (1998) · Zbl 0917.34036
[4] Cao, J. D., Global exponential stability of Hopfield neural networks, Int. J. Syst. Sci., 32, 2, 233-236 (2001) · Zbl 1011.93091
[5] Xu, S. Y.; Lam, J.; Ho, D. W.C., A new LMI condition for delay-dependent asymptotic stability of delayed Hopfield neural networks, IEEE Trans. Circuits Syst. II, 53, 3, 230-234 (2006)
[6] Cao, Q.; Long, X., New convergence on inertial neural networks with time-varying delays and continuously distributed delays, AIMS Math., 5, 6, 5955-5968 (2020) · Zbl 1484.34103
[7] Manickama, I.; Ramachandranb, R.; Rajchakitc, G.; Cao, J. D.; Huang, C. X., Novel lagrange sense exponential stability criteria for time-delayed stochastic Cohen-Grossberg neural networks with Markovian jump parameters: a graph-theoretic approach, Nonlinear Anal. Model. Control, 25, 5, 726-744 (2020) · Zbl 1448.93273
[8] Zhang, X. M.; Han, Q. L., Global asymptotic stability analysis for delayed neural networks using a matrix-based quadratic convex approach, Neural Netw., 54, 57-69 (2014) · Zbl 1322.93079
[9] Zhang, X. X.; Li, C. D.; Huang, T. W., Hybrid impulsive and switching Hopfield neural networks with state-dependent impulses, Neural Netw., 93, 176-184 (2017) · Zbl 1432.93120
[10] Bento, A. J.G.; Oliveira, J. J.; Silva, C. M., Nonuniform behavior and stability of Hopfield neural networks with delay, Nonlinearity, 30, 3088-3103 (2017) · Zbl 1380.39020
[11] Sabri, A., A modified Lyapunov functional with application to stability of neutral-type neural networks with time delays, J. Frankl. Inst., 356, 1, 276-291 (2019) · Zbl 1405.93188
[12] Rech, P. C., Chaos and hyperchaos in a Hopfield neural network, Neurocomputing, 74, 17, 3361-3364 (2011)
[13] Mazrooei-Sebdani, R.; Farjami, S., On a discrete-time-delayed Hopfield neural network with ring structures and different internal decays: bifurcations analysis and chaotic behavior, Neurocomputing, 151, 188-195 (2015)
[14] Liu, B.; Lu, W. L.; Chen, T. P., Global almost sure self-synchronization of Hopfield neural networks with randomly switching connections, Neural Netw., 24, 305-310 (2011) · Zbl 1225.93019
[15] Sheng, Y.; Zhang, H.; Zeng, Z. G., Synchronization of reaction-diffusion neural networks with Dirichlet boundary conditions and infinite delays, IEEE Trans. Cybern., 47, 10, 3005-3017 (2017)
[16] A. Kazemy, J. Lam, X.M. Zhang, Event-triggered output feedback synchronization of master-slave neural networks under deception attacks, IEEE Trans. Neural Netw. Learn. Syst.. 10.1109/TNNLS.2020.3030638
[17] Huang, L. H.; Ma, H. L.; Wang, J. F.; Huang, C. X., Global dynamics of a Filippov plant disease model with an economic threshold of infected-susceptible ratio, J. Appl. Anal. Comput., 10, 5, 2263-2277 (2020) · Zbl 1460.92202
[18] Huang, C. X.; Tan, Y. X., Global behavior of a reaction-diffusion model with time delay and Dirichlet condition, J. Differ. Equ., 271, 186-215 (2021) · Zbl 1454.35214
[19] Hu, H. J.; Yi, T. S.; Zou, X. F., On spattal-temporal dynamics of a Fisher-KPP equation with a shifting environment, Proc. Am. Math. Soc., 148, 1, 213-221 (2020) · Zbl 1430.35140
[20] Li, B.; Wang, F.; Zhao, K., Large time dynamics of 2D semi-dissipative Boussinesq equations, Nonlinearity, 33, 2481-2501 (2020) · Zbl 1481.35334
[21] Wang, J. F.; He, S.; Huang, L. H., Limit cycles induced by threshold nonlinearity in planar piecewise linear systems of node-focus or node-center type, Int. J. Bifurc. Chaos Appl. Sci. Eng., 30, 11, 2050160 (2020) · Zbl 1478.34037
[22] Yu, F.; Qian, S.; Chen, X.; Huang, Y. Y.; Liu, L., A new 4D four-wing memristive hyperchaotic system: dynamical analysis, electronic circuit design, shape synchronization and secure communication, Int. J. Bifurc. Chaos Appl. Sci. Eng., 30, 10, 2050147 (2020) · Zbl 1450.37094
[23] Burq, N.; Tzvetkov, N., Random data cauchy theory for supercritical wave equations I: local theory, Invent. Math., 173, 449-475 (2008) · Zbl 1156.35062
[24] Wang, B. X., Weak pullback attractors for mean random dynamical systems in Bochner spaces, J. Dyn. Differ. Equ., 31, 4, 2177-2204 (2019) · Zbl 1428.35052
[25] Shu, R. W.; Jin, S., A study of landau damping with random initial inputs, J. Differ. Equ., 266, 4, 1922-1945 (2019) · Zbl 1416.35305
[26] Tsunoda, Y.; Nakamura, K., Neural network model controlling saccade based on probabilistic expectation, Proceedings of the International Joint INNS-IEEE Conference on Neural Networks, 2, 854-859 (2001)
[27] Kloeden, P. E.; Lorenz, T., Stochastic differential equations with nonlocal sample dependence, Stoch. Anal. Appl., 28, 6, 937-945 (2010) · Zbl 1205.60131
[28] Wu, F. K.; Hu, S. G., On a class of nonlocal stochastic functional differential equations with infinite delay, Stoch. Anal. Appl., 29, 713-721 (2011) · Zbl 1229.60076
[29] Hu, Y. Z., A class of stochastic Hopfield neural networks with expectations in coefficients, Neurocomputing, 141, 188-193 (2014)
[30] Xie, W. J.; Zhu, Q. X.; Jiang, F., Exponential stability of stochastic neural networks with leakage delays and expectations in the coefficients, Neurocomputing, 173, 1268-1275 (2016)
[31] Blythe, S.; Mao, X. R.; Liao, X. X., Stability of stochastic delay neural networks, J. Frankl. Inst., 338, 4, 481-495 (2001) · Zbl 0991.93120
[32] Li, X. F.; Ding, D., Mean square exponential stability of stochastic Hopfield neural networks with mixed delays, Stat. Probab. Lett., 126, 88-96 (2017) · Zbl 1378.92005
[33] Wei, T. D.; Lin, P.; Wang, Y. F.; Wang, L. S., Stability of stochastic impulsive reaction-diffusion neural networks with s-type distributed delays and its application to image encryption, Neural Netw., 116, 35-45 (2019) · Zbl 1441.93332
[34] Rathinasamy, A.; Narayanasamy, J., Mean square stability and almost sure exponential stability of two step Maruyama methods of stochastic delay Hopfield neural networks, Appl. Math. Comput., 348, 126-152 (2019) · Zbl 1429.65021
[35] Huang, H.; Ho, D. W.C.; Cao, J. D., Analysis of global exponential stability and periodic solutions of neural networks with time-varying delays, Neural Netw., 18, 161-170 (2005) · Zbl 1078.68122
[36] Chen, Z.; Ruan, J., Global stability analysis of impulsive Cohen-Grossberg neural networks with delay, Phys. Lett. A, 345, 101-111 (2005) · Zbl 1345.92012
[37] Allegretto, W.; Papini, D., Stability for delayed reaction-diffusion neural networks, Phys. Lett. A, 360, 669-680 (2007) · Zbl 1236.35085
[38] Duan, L.; Huang, L. H.; Guo, Z. Y.; Fang, X. W., Periodic attractor for reaction-diffusion high-order Hopfield neural networks with time-varying delays, Comput. Math. Appl., 73, 2, 233-245 (2017) · Zbl 1386.35159
[39] Aouiti, C.; M’hamdi, M. S.; Chérif, F.; Alimi, A. M., Impulsive generalized high-order recurrent neural networks with mixed delays: stability and periodicity, Neurocomputing, 321, 296-307 (2018)
[40] Lu, J. X.; Ma, Y. C., Mean square exponential stability and periodic solutions of stochastic delay cellular neural networks, Chaos Solitons Fractals, 38, 5, 1323-1331 (2008) · Zbl 1154.34395
[41] Li, X. D., Existence and global exponential stability of periodic solution for delayed neural networks with impulsive and stochastic effect, Neurocomputing, 73, 749-758 (2010)
[42] Li, D. S.; Wang, X. H.; Xu, D. Y., Existence and global \(p\)-exponential stability of periodic solution for impulsive stochastic neural networks with delays, Nonlinear Anal. Hybrid Syst., 6, 847-858 (2012) · Zbl 1244.93169
[43] Yang, L.; Li, Y. K., Existence and exponential stability of periodic solution for stochastic Hopfield neural networks on time scales, Neurocomputing, 167, 543-550 (2015)
[44] Yao, Q.; Wang, L. S.; Wang, Y. F., Periodic solutions to impulsive stochastic reaction-diffusion neural networks with delays, Commun. Nonlinear Sci. Numer. Simul., 78, 104865 (2019) · Zbl 1476.35349
[45] Chen, A. P.; Huang, L. H.; Cao, J. D., Existence and stability of almost periodic solution for BAM neural networks with delays, Appl. Math. Comput., 137, 1, 177-193 (2003) · Zbl 1034.34087
[46] Stamov, G. T.; Stamova, I. M., Almost periodic solutions for impulsive neural networks with delay, Appl. Math. Model., 31, 7, 1263-1270 (2007) · Zbl 1136.34332
[47] Kong, F. C.; Zhu, Q. X.; Wang, K.; Nieto, J. J., Stability analysis of almost periodic solutions of discontinuous BAM neural networks with hybrid time-varying delays and \(D\) operator, J. Frankl. Inst., 356, 18, 11605-11637 (2020) · Zbl 1427.93202
[48] Zhang, T. W.; Han, S. F.; Zhou, J. W., Dynamic behaviours for semi-discrete stochastic Cohen-Grossberg neural networks with time delays, J. Frankl. Inst., 357, 17, 13006-13040 (2020) · Zbl 1454.93293
[49] Huang, C. X.; Yang, H. D.; Cao, J. D., Weighted pseudo almost periodicity of multi-proportional delayed shunting inhibitory cellular neural networks with \(D\) operator, Discret. Contin. Dyn. Syst. Ser. S, 14, 4, 1259-1272 (2021) · Zbl 1475.92016
[50] Peng, S. G., G-expectation, G-Brownian motion and related stochastic calculus of Itô type, Stochastic Analysis and Applications, 541-567 (2007), Springer: Springer Berlin · Zbl 1131.60057
[51] Li, Y. M.; Yan, L. T., Stability of delayed Hopfield neural networks under a sublinear expectation framework, J. Frankl. Inst., 355, 10, 4268-4281 (2018) · Zbl 1390.93838
[52] Ren, Y.; He, Q.; Gu, Y. F.; Sakthivel, R., Mean-square stability of delayed stochastic neural networks with impulsive effects driven by G-Brownian motion, Stat. Probab. Lett., 143, 56-66 (2018) · Zbl 1406.60100
[53] Z. Chen, D.D. Yang, Stability analysis of Hopfield neural networks with unbounded delay driven by G-Brownian motion, Int. J. Control. 10.1080/00207179.2020.1775307 · Zbl 1482.93511
[54] Chen, Z.; Yang, D. D., Nonlocal stochastic functional differential equations driven by G-Brownian motion and mean random dynamical systems, Math. Methods Appl. Sci., 43, 12, 7424-7441 (2020) · Zbl 1453.37050
[55] Denis, L.; Hu, M. S.; Peng, S. G., Function spaces and capacity related to a sublinear expectation: application to G-Brownian motion paths, Potential Anal., 34, 2, 139-161 (2011) · Zbl 1225.60057
[56] Hu, M. S.; Ji, X. J.; Liu, G. M., On the strong Markov property for stochastic differential equations driven by G-Brownian motion, Stoch. Process. Appl., 131, 417-453 (2021)
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