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Pinning synchronization of stochastic neutral memristive neural networks with reaction-diffusion terms. (English) Zbl 1525.93475

Summary: This paper investigates the pinning synchronization of stochastic neutral memristive neural networks with reaction-diffusion terms. Firstly, two novel pinning controllers, which contain both current state and past state, are designed. Subsequently, in terms of Green’s theorem, inequality technology, stochastic analysis theory and pinning control technology, two easy-to-test sufficient conditions based on algebraic inequalities are obtained to ensure the mean-square asymptotic synchronization of stochastic memristive neural networks with neutral delays and reaction-diffusion terms by providing a new Lyapunov-Krasovskii functional. In addition, some existing results can be regarded as special cases of our work. Finally, illustrative examples further verify the correctness and validity of the derived results.

MSC:

93E15 Stochastic stability in control theory
93B70 Networked control
93C20 Control/observation systems governed by partial differential equations
Full Text: DOI

References:

[1] Anand, K. S.; Yogambigai, J.; B. G., Harish; Ali, M. S.; Padmanabhan, S., Synchronization of singular Markovian jumping neutral complex dynamical networks with time-varying delays via pinning control, Acta Mathematica Scientia, 40, 3, 863-886 (2020) · Zbl 1499.93077
[2] Barbarossa, M. V.; Hadeler, K. P.; Kuttler, C., State-dependent neutral delay equations from population dynamics, Journal of Mathematical Biology, 69, 4, 1027-1056 (2014) · Zbl 1308.34106
[3] Chen, S. Y.; Feng, J. W.; Wang, J. Y.; Zhao, Y., Almost sure exponential synchronization of drive-response stochastic memristive neural networks, Applied Mathematics and Computation, 383, Article 125360 pp. (2020) · Zbl 1508.93315
[4] Chen, C.; Mi, L.; Liu, Z. Q.; Qiu, B. L.; Zhao, H.; Xu, L. J., Predefined-time synchronization of competitive neural networks, Neural Networks, 142, 492-499 (2021) · Zbl 1526.93218
[5] Chen, H. B.; Shi, P.; Lim, C. C., Cluster synchronization for neutral stochastic delay networks via intermittent adaptive control, IEEE Transactions on Neural Networks and Learning Systems, 30, 11, 3246-3259 (2019)
[6] Chen, Huabin; Shi, Peng; Lim, Cheng-Chew, Synchronization control for neutral stochastic delay Markov networks via single pinning impulsive strategy, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 50, 12, 5406-5419 (2020)
[7] Chua, L., Memristor-The missing circuit element, IEEE Transactions on Circuit Theory, 18, 5, 507-519 (1971)
[8] Dai, A. D.; Zhou, W. N.; Xu, Y. H.; Xiao, C., Adaptive exponential synchronization in mean square for Markovian jumping neutral-type coupled neural networks with time-varying delays by pinning control, Neurocomputing, 173, 809-818 (2016)
[9] Hong, D. S.; Xiong, Z. L.; Yang, C. P., Analysis of adaptive synchronization for stochastic neutral-type memristive neural networks with mixed time-varying delays, Discrete Dynamics in Nature and Society, 2018 (2018) · Zbl 1417.93168
[10] Hu, X. F.; Feng, G.; Duan, S. K.; Liu, L., A memristive multilayer cellular neural network with applications to image processing, IEEE Transactions on Neural Networks and Learning Systems, 28, 8, 1889-1901 (2017)
[11] Jo, S. H.; Chang, T.; Ebong, I.; Bhadviya, B. B.; Mazumder, P.; Lu, W., Nanoscale memristor device as synapse in neuromorphic systems, Nano Letters, 10, 4, 1297-1301 (2010)
[12] Lakshmanan, S.; Prakash, M.; Lim, C. P.; Rakkiyappan, R.; Balasubramaniam, P.; Nahavandi, S., Synchronization of an inertial neural network with time-varying delays and its application to secure communication, IEEE Transactions on Neural Networks and Learning Systems, 29, 1, 195-207 (2018)
[13] Lammie, C.; Eshraghian, J. K.; Lu, W. D.; Azghadi, M. R., Memristive stochastic computing for deep learning parameter optimization, IEEE Transactions on Circuits and Systems II: Express Briefs, 68, 5, 1650-1654 (2021)
[14] Li, X. F.; Fang, J. A.; Li, H. Y., Exponential stabilization of stochastic memristive neural networks under intermittent adaptive control, IET Control Theory & Applications, 11, 15, 2432-2439 (2017)
[15] Li, C.; Hu, M.; Li, Y. N.; Jiang, H.; Ge, N.; Montgomery, E., Analogue signal and image processing with large memristor crossbars, Nature Electronics, 1, 1, 52-59 (2018)
[16] Li, C.; Lian, J.; Wang, Y., Stability of switched memristive neural networks with impulse and stochastic disturbance, Neurocomputing, 275, 2565-2573 (2018)
[17] Li, R. X.; Wei, H. Z., Synchronization of delayed Markovian jump memristive neural networks with reaction-diffusion terms via sampled data control, International Journal of Machine Learning and Cybernetics, 7, 157-169 (2016)
[18] Lv, Y. J.; Hu, C.; Yu, J.; Jiang, H. J.; Huang, T. W., Edge-based fractional-order adaptive strategies for synchronization of fractional-order coupled networks with reaction-diffusion Terms, IEEE Transactions on Cybernetics, 50, 4, 1582-1594 (2020)
[19] Mao, X. R., Stochastic differential equations and applications (2007), Elsevier · Zbl 1138.60005
[20] Payvand, M.; Fouda, M. E.; Kurdahi, F.; Eltawil, A. M.; Neftci, E. O., On-chip error-triggered learning of multi-layer memristive spiking neural networks, IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 10, 4, 522-535 (2020)
[21] Qing, Z.; Yin, G. G.; Boukas, E. K., Optimal control of a marketing-production system, IEEE Transactions on Automatic Control, 46, 3, 416-427 (2001) · Zbl 1017.90035
[22] Snider, G. S. 0000. Cortical computing with memristive nanodevices, SciDAC Review.
[23] Song, X. N.; Man, J. T.; Song, S.; Ahn, C. K., Gain-scheduled finite-time synchronization for reaction-diffusion memristive neural networks subject to inconsistent Markov chains, IEEE Transactions on Neural Networks and Learning Systems, 32, 7, 2952-2964 (2020)
[24] Song, X. N.; Man, J. T.; Song, S.; Ahn, C. K., Finite/fixed-time anti-synchronization of inconsistent Markovian quaternion-valued memristive neural networks with reaction-diffusion terms, IEEE Transactions on Circuits and Systems. I. Regular Papers, 68, 1, 363-375 (2021)
[25] Song, X. N.; Man, J. T.; Song, S.; Zhang, Y. J.; Ning, Z. K., Finite/fixed-time synchronization for Markovian complex-valued memristive neural networks with reaction-diffusion terms and its application, Neurocomputing, 414, 131-142 (2020)
[26] Strukov, D. B.; Snider, G. S.; Stewart, D. R.; Williams, R. S., The missing memristor found, Nature, 453 (2008)
[27] Sun, X. J.; Feng, Z. H.; Liu, X. L., Pinning adaptive synchronization of neutral-type coupled neural networks with stochastic perturbation, Advances in Difference Equations, 2014, 1, 1-13 (2014) · Zbl 1417.34112
[28] Tong, D. B.; Chen, Q. Y.; Zhou, W. N.; Zhou, J.; Xu, Y. H., Multi-delay-dependent exponential synchronization for neutral-type stochastic complex networks with Markovian jump parameters via adaptive control, Neural Processing Letters, 49, 3, 1611-1628 (2019)
[29] Wang, C. J.; Xiong, Z. L.; Liang, M.; Yin, H. W., Stability analysis for stochastic neutral-type memristive neural networks with time-varying delay and s-type distributed delays, Mathematical Problems in Engineering, 2017 (2017) · Zbl 1426.93357
[30] Wen, S. P.; Zeng, Z. G.; Huang, T. W.; Meng, Q. G.; Yao, W., Lag synchronization of switched neural networks via neural activation function and applications in image encryption, IEEE Transactions on Neural Networks and Learning Systems, 26, 7, 1493-1502 (2015)
[31] Wu, X.; Liu, S. T.; Wang, H. Y., Asymptotic stability and synchronization of fractional delayed memristive neural networks with algebraic constraints, Communications in Nonlinear Science and Numerical Simulation, Article 106694 pp. (2022) · Zbl 1503.34147
[32] Wu, X.; Liu, S.; Yang, R.; Zhang, Y. J.; Li, X., Global synchronization of fractional complex networks with non-delayed and delayed couplings, Neurocomputing, 290, 43-49 (2018)
[33] Wu, T.; Xiong, L. L.; Cao, J. D.; Xie, X. Q., Almost surely asymptotic synchronization for stochastic neural networks of neutral type with Markovian jumping parameters, International Journal of Adaptive Control and Signal Processing, 33, 10, 1524-1551 (2019) · Zbl 1429.93409
[34] Yao, W.; Wang, C. H.; Sun, Y. C.; Zhou, C.; Lin, H. R., Exponential multistability of memristive Cohen-Grossberg neural networks with stochastic parameter perturbations, Applied Mathematics and Computation, 386, Article 125483 pp. (2020) · Zbl 1497.93194
[35] Yue, D., & Han, Q. L. (2004). A delay-dependent stability criterion of neutral systems and its application to a partial element equivalent circuit model. In Proceedings of the 2004 american control conference, vol. 6 (pp. 5438-5442).
[36] Zhang, Y. J.; Gu, D. W.; Xu, S. Y., Global exponential adaptive synchronization of complex dynamical networks with neutral-type neural network nodes and stochastic disturbances, IEEE Transactions on Circuits and Systems. I. Regular Papers, 60, 10, 2709-2718 (2013)
[37] Zhang, Y. L.; Zhuang, J. S.; Xia, Y. H.; Bai, Y. Z.; Cao, J. D.; Gu, L. F., Fixed-time synchronization of the impulsive memristor-based neural networks, Communications in Nonlinear Science and Numerical Simulation, 77, 40-53 (2019) · Zbl 1524.34125
[38] Zheng, C. D.; Wang, Z. S., Stochastic synchronization of neutral-type chaotic impulse neural networks with leakage delay and Markovian jumping parameters, International Journal of Intelligent Computing and Cybernetics, 9, 3, 237-254 (2016)
[39] Zheng, C. D.; Wei, Z. P.; Wang, Z. S., Robustly adaptive synchronization for stochastic Markovian neural networks of neutral type with mixed mode-dependent delays, Neurocomputing, 171, 1254-1264 (2016)
[40] Zhou, J.; Ding, X. W.; Zhou, L. W.; Zhou, W. N.; Yang, J.; Tong, D. B., Almost sure adaptive asymptotically synchronization for neutral-type multi-slave neural networks with Markovian jumping parameters and stochastic perturbation, Neurocomputing, 214, 44-52 (2016)
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