Startseite Global exponential stability of periodic solution of delayed discontinuous Cohen–Grossberg neural networks and its applications
Artikel
Lizenziert
Nicht lizenziert Erfordert eine Authentifizierung

Global exponential stability of periodic solution of delayed discontinuous Cohen–Grossberg neural networks and its applications

  • Yiyuan Chai , Jiqiang Feng , Sitian Qin EMAIL logo und Xinyu Pan
Veröffentlicht/Copyright: 4. Juni 2021
Veröffentlichen auch Sie bei De Gruyter Brill

Abstract

This paper is concerned with the existence and global exponential stability of the periodic solution of delayed Cohen–Grossberg neural networks (CGNNs) with discontinuous activation functions. The activations considered herein are non-decreasing but not required to be Lipschitz or continuous. Based on differential inclusion theory, Lyapunov functional theory and Leary–Schauder alternative theorem, some sufficient criteria are derived to ensure the existence and global exponential stability of the periodic solution. In order to show the superiority of the obtained results, an application and some detailed comparisons between some existing related results and our results are presented. Finally, some numerical examples are also illustrated.


Corresponding author: Sitian Qin, Department of Mathematics, Harbin Institute of Technology, Weihai 264209, China, E-mail:

Award Identifier / Grant number: 11871178

Award Identifier / Grant number: 61773136

  1. Author contribution: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

  2. Research funding: This research is supported by the National Science Foundation of China (61773136, 11871178).

  3. Conflict of interest statement: The authors declare that they have no conflict of interest.

References

[1] M. Cohen and S. Grossberg, “Absolute stability of global pattern formation and parallel memory storage by competitive neural networks,” IEEE Trans. Syst. Man Cybern. Syst., vol. 5, pp. 815–826, 1983. https://doi.org/10.1109/tsmc.1983.6313075.Suche in Google Scholar

[2] P. Balasubramaniam and V. Vembarasan, “Asymptotic stability of BAM neural networks of neutral-type with impulsive effects and time delay in the leakage term,” Int. J. Comput. Math., vol. 88, no. 15, pp. 3271–3291, 2011. https://doi.org/10.1080/00207160.2011.591388.Suche in Google Scholar

[3] Z. Cai and L. Huang, “Finite-time synchronization by switching state-feedback control for discontinuous Cohen–Grossberg neural networks with mixed delays,” Int. J. Mach. Learn. Cybern., vol. 9, no. 10, pp. 1683–1695, 2018.10.1007/s13042-017-0673-9Suche in Google Scholar

[4] L. Chua, “Resistance switching memories are memristors,” Appl. Phys. A, vol. 102, no. 4, pp. 765–783, 2011. https://doi.org/10.1007/s00339-011-6264-9.Suche in Google Scholar

[5] C. Aouiti and I. Ben Gharbia, “Dynamics behavior for second-order neutral Clifford differential equations: inertial neural networks with mixed delays,” Comput. Appl. Math., vol. 39, no. 2, p. 120, 2020. https://doi.org/10.1007/s40314-020-01148-0.Suche in Google Scholar

[6] M. Forti, P. Nistri, and D. Papini, “Global exponential stability and global convergence in finite time of delayed neural networks with infinite gain,” IEEE Trans. Neural Network., vol. 16, no. 6, pp. 1449–1463, 2005. https://doi.org/10.1109/tnn.2005.852862.Suche in Google Scholar PubMed

[7] X. Xue and Y. Wang, “Using memetic algorithm for instance coreference resolution,” IEEE Trans. Knowl. Data Eng., vol. 28, no. 2, pp. 580–591, 2016. https://doi.org/10.1109/tkde.2015.2475755.Suche in Google Scholar

[8] M. Yan, J. Qiu, X. Chen, X. Chen, C. Yang, and A. Zhang, “Almost periodic dynamics of the delayed complex-valued recurrent neural networks with discontinuous activation functions,” Neural Comput. Appl., vol. 30, pp. 3339–3352, 2018. https://doi.org/10.1007/s00521-017-2911-1.Suche in Google Scholar

[9] W. Yang, “Existence of periodic solutions with minimal period for fourth-order discrete systems via variational methods,” Int. J. Nonlinear Sci. Numer. Stimul., vol. 21, no. 6, pp. 635–640, 2020. https://doi.org/10.1515/ijnsns-2019-0077.Suche in Google Scholar

[10] M. Ye, Y. Wang, C. Dai, and X. Wang, “A hybrid genetic algorithm for the minimum exposure path problem of wireless sensor networks based on a numerical functional extreme model,” IEEE Trans. Veh. Technol., vol. 65, no. 10, pp. 8644–8657, 2016. https://doi.org/10.1109/tvt.2015.2508504.Suche in Google Scholar

[11] F. Kong, Q. Zhu, K. Wang, and J. Nieto, “Stability analysis of almost periodic solutions of discontinuous BAM neural networks with hybrid time-varying delays and D operator,” J. Franklin Inst., vol. 356, no. 18, pp. 11605–11637, 2019. https://doi.org/10.1016/j.jfranklin.2019.09.030.Suche in Google Scholar

[12] C. Aouiti and F. Dridi, “Weighted pseudo almost automorphic solutions for neutral type fuzzy cellular neural networks with mixed delays and D operator in Clifford algebra,” Int. J. Syst. Sci., vol. 51, no. 10, pp. 1759–1781, 2020. https://doi.org/10.1080/00207721.2020.1777345.Suche in Google Scholar

[13] S. A. Karthick, R. Sakthivel, F. Alzahrani, and A. Leelamani, “Synchronization of semi-Markov coupled neural networks with impulse effects and leakage delay,” Neurocomputing, vol. 386, pp. 221–231, 2020. https://doi.org/10.1016/j.neucom.2019.12.097.Suche in Google Scholar

[14] X. Li, W. Zhang, J.-a. Fang, and H. Li, “Finite-time synchronization of memristive neural networks with discontinuous activation functions and mixed time-varying delays,” Neurocomputing, vol. 340, pp. 99–109, 2019. https://doi.org/10.1016/j.neucom.2019.02.051.Suche in Google Scholar

[15] J. P. Richard, “Time-delay systems: an overview of some recent advances and open problems,” Automatica, vol. 39, no. 10, pp. 1667–1694, 2003. https://doi.org/10.1016/s0005-1098(03)00167-5.Suche in Google Scholar

[16] L. Wang, Y. Shen, Q. Yin, and G. Zhang, “Adaptive synchronization of memristor-based neural networks with time-varying delays,” IEEE Trans. Neural Netw. Learn. Syst, vol. 26, no. 9, pp. 2033–2042, 2015. https://doi.org/10.1109/tnnls.2014.2361776.Suche in Google Scholar

[17] C. Aouiti and F. Dridi, “New results on interval general Cohen–Grossberg BAM neural networks,” J. Syst. Sci. Complex., vol. 33, no. 4, pp. 944–967, 2020. https://doi.org/10.1007/s11424-020-8048-9.Suche in Google Scholar

[18] H. Bao, “Existence and exponential stability of periodic solution for BAM fuzzy Cohen–Grossberg neural networks with mixed delays,” Neural Process. Lett., vol. 43, no. 3, pp. 871–885, 2016. https://doi.org/10.1007/s11063-015-9455-0.Suche in Google Scholar

[19] Y. Chen and S. Jia, “Multiple stability and instability of Cohen–Grossberg neural network with unbounded time-varying delays,” J. Inequalities Appl., vol. 111, no. 1, p. 178, 2019.10.1186/s13660-019-2129-0Suche in Google Scholar

[20] X. Fu and F. Kong, “Global exponential stability analysis of anti-periodic solutions of discontinuous bidirectional associative memory (BAM) neural networks with time-varying delays,” Int. J. Nonlinear Sci. Numer. Stimul., vol. 21, no. 7, pp. 807–820, 2020. https://doi.org/10.1515/ijnsns-2019-0220.Suche in Google Scholar

[21] P. Jiang, Z. Zeng, and J. Chen, “Almost periodic solutions for a memristor-based neural networks with leakage, time-varying and distributed delays,” Neural Network., vol. 68, pp. 34–45, 2015. https://doi.org/10.1016/j.neunet.2015.04.005.Suche in Google Scholar PubMed

[22] H. Kang, X. Fu, and Z. Sun, “Global exponential stability of periodic solutions for impulsive Cohen–Grossberg neural networks with delays,” Appl. Math. Model., vol. 39, no. 5, pp. 1526–1535, 2015. https://doi.org/10.1016/j.apm.2014.09.015.Suche in Google Scholar

[23] B. Li and Q. Song, “Some new results on periodic solution of Cohen–Grossberg neural network with impulses,” Neurocomputing, vol. 177, pp. 401–408, 2016. https://doi.org/10.1016/j.neucom.2015.11.038.Suche in Google Scholar

[24] Y. Li, J. Xiang, and B. Li, “Almost periodic solutions of quaternion-valued neutral type high-order Hopfield neural networks with state-dependent delays and leakage delays,” Appl. Intell., vol. 50, no. 7, pp. 2067–2078, 2020. https://doi.org/10.1007/s10489-020-01634-2.Suche in Google Scholar

[25] F. Meng, K. Li, Q. Song, Y. Liu, and F. Alsaadi, “Periodicity of Cohen–Grossberg-type fuzzy neural networks with impulses and time-varying delays,” Neurocomputing, vol. 325, pp. 254–259, 2019. https://doi.org/10.1016/j.neucom.2018.10.038.Suche in Google Scholar

[26] C. Xu and Q. Zhang, “Existence and global exponential stability of anti-periodic solutions for BAM neural networks with inertial term and delay,” Neurocomputing, vol. 153, pp. 108–116, 2015. https://doi.org/10.1016/j.neucom.2014.11.047.Suche in Google Scholar

[27] Y. Xu, “Exponential stability of pseudo almost periodic solutions for neutral type cellular neural networks with D operator,” Neural Process. Lett., vol. 16, pp. 1–14, 2017.10.1007/s11063-017-9584-8Suche in Google Scholar

[28] M. Forti and P. Nistri, “Global convergence of neural networks with discontinuous neuron activations,” IEEE Trans. Circuits Syst. I, vol. 50, pp. 1421–1435, 2003. https://doi.org/10.1109/tcsi.2003.818614.Suche in Google Scholar

[29] X. Chen and Q. Song, “Global exponential stability of the periodic solution of delayed Cohen–Grossberg neural networks with discontinuous activations,” Neurocomputing, vol. 73, no. 16, pp. 3097–3104, 2010. https://doi.org/10.1016/j.neucom.2010.06.010.Suche in Google Scholar

[30] L. Duan, X. Fang, X. Yi, and Y. Fu, “Finite-time synchronization of delayed competitive neural networks with discontinuous neuron activations,” Int. J. Mach. Learn. Cybern., vol. 9, no. 10, pp. 1649–1661, 2018. https://doi.org/10.1007/s13042-017-0670-z.Suche in Google Scholar

[31] L. Duan, L. Huang, and Z. Cai, “Existence and stability of periodic solution for mixed time-varying delayed neural networks with discontinuous activations,” Neurocomputing, vol. 123, pp. 255–265, 2014. https://doi.org/10.1016/j.neucom.2013.06.038.Suche in Google Scholar

[32] S. Qin, X. Xue, and P. Wang, “Global exponential stability of almost periodic solution of delayed neural networks with discontinuous activations,” Inf. Sci., vol. 220, pp. 367–378, 2013. https://doi.org/10.1016/j.ins.2012.07.040.Suche in Google Scholar

[33] D. Wang and L. Huang, “Periodicity and multi-periodicity of generalized Cohen–Grossberg neural networks via functional differential inclusions,” Nonlinear Dynam., vol. 85, no. 1, pp. 67–86, 2016. https://doi.org/10.1007/s11071-016-2667-7.Suche in Google Scholar

[34] Z. Wang, J. Cao, Z. Cai, and L. Huang, “Periodicity and finite-time periodic synchronization of discontinuous complex-valued neural networks,” Neural Network., vol. 119, pp. 249–260, 2019. https://doi.org/10.1016/j.neunet.2019.08.021.Suche in Google Scholar PubMed

[35] H. Wu, L. Wang, Y. Wang, P. Niu, and B. Fang, “Exponential state estimation for Markovian jumping neural networks with mixed time-varying delays and discontinuous activation functions,” Int. J. Mach. Learn. Cybern., vol. 7, no. 4, pp. 641–652, 2016. https://doi.org/10.1007/s13042-015-0447-1.Suche in Google Scholar

[36] D. Wang and L. Huang, “Periodicity and global exponential stability of generalized Cohen–Grossberg neural networks with discontinuous activations and mixed delays,” Neural Network., vol. 51, pp. 80–95, 2014. https://doi.org/10.1016/j.neunet.2013.12.005.Suche in Google Scholar PubMed

[37] S. A. Karthick, R. Sakthivel, C. Wang, and Y. Ma, “Synchronization of coupled memristive neural networks with actuator saturation and switching topology,” Neurocomputing, vol. 383, pp. 138–150, 2020. https://doi.org/10.1016/j.neucom.2019.11.034.Suche in Google Scholar

[38] S. Qin, J. Wang, and X. Xue, “Convergence and attractivity of memristor-based cellular neural networks with time delays,” Neural Network., vol. 63, pp. 223–233, 2015. https://doi.org/10.1016/j.neunet.2014.12.002.Suche in Google Scholar PubMed

[39] H. Wu, R. Li, S. Ding, X. Zhang, and R. Yao, “Complete periodic adaptive antisynchronization of memristor-based neural networks with mixed time-varying delays,” Can. J. Phys., vol. 92, no. 11, pp. 1337–1349, 2014. https://doi.org/10.1139/cjp-2013-0456.Suche in Google Scholar

[40] A. Granas and J. Dugundji, Fixed Point Theory, New York, Springer Science & Business Media, 2003 .10.1007/978-0-387-21593-8Suche in Google Scholar

[41] J. P. Aubin, Viability Theory, Boston, Birkauser, 2011.10.1007/978-3-642-16684-6Suche in Google Scholar

[42] J. Chen, B. Chen, and Z. Zeng, “o(t−α)-synchronization and Mittag–Leffler synchronization for the fractional-order memristive neural networks with delays and discontinuous neuron activations,” Neural Network., vol. 100, pp. 10–24, 2018. https://doi.org/10.1016/j.neunet.2018.01.004.Suche in Google Scholar PubMed

[43] M. Itoh and L. O. Chua, “Memristor cellular automata and memristor discrete-time cellular neural networks,” Int. J. Bifurc. Chaos, vol. 19, no. 11, pp. 3605–3656, 2009. https://doi.org/10.1142/s0218127409025031.Suche in Google Scholar

[44] S. Wen, G. Bao, Z. Zeng, Y. Chen, and T. Huang, “Global exponential synchronization of memristor-based recurrent neural networks with time-varying delays,” Neural Network., vol. 48, pp. 195–203, 2013. https://doi.org/10.1016/j.neunet.2013.10.001.Suche in Google Scholar PubMed

[45] P. Liu, Z. Zeng, and J. Wang, “Multistability analysis of a general class of recurrent neural networks with non-monotonic activation functions and time-varying delays,” Neural Network., vol. 79, pp. 117–127, 2016. https://doi.org/10.1016/j.neunet.2016.03.010.Suche in Google Scholar PubMed

Received: 2020-07-13
Revised: 2021-04-20
Accepted: 2021-05-12
Published Online: 2021-06-04
Published in Print: 2023-02-23

© 2021 Walter de Gruyter GmbH, Berlin/Boston

Artikel in diesem Heft

  1. Frontmatter
  2. Original Research Articles
  3. Modeling and assessment of the flow and air pollutants dispersion during chemical reactions from power plant activities
  4. Stochastic dynamics of dielectric elastomer balloon with viscoelasticity under pressure disturbance
  5. Unsteady MHD natural convection flow of a nanofluid inside an inclined square cavity containing a heated circular obstacle
  6. Fractional-order generalized Legendre wavelets and their applications to fractional Riccati differential equations
  7. Battery discharging model on fractal time sets
  8. Adaptive neural network control of second-order underactuated systems with prescribed performance constraints
  9. Optimal control for dengue eradication program under the media awareness effect
  10. Shifted Legendre spectral collocation technique for solving stochastic Volterra–Fredholm integral equations
  11. Modeling and simulations of a Zika virus as a mosquito-borne transmitted disease with environmental fluctuations
  12. Mathematical analysis of the impact of vaccination and poor sanitation on the dynamics of poliomyelitis
  13. Anti-sway method for reducing vibrations on a tower crane structure
  14. Stable soliton solutions to the time fractional evolution equations in mathematical physics via the new generalized G / G -expansion method
  15. Convergence analysis of online learning algorithm with two-stage step size
  16. An estimative (warning) model for recognition of pandemic nature of virus infections
  17. Interaction among a lump, periodic waves, and kink solutions to the KP-BBM equation
  18. Global exponential stability of periodic solution of delayed discontinuous Cohen–Grossberg neural networks and its applications
  19. An efficient class of fourth-order derivative-free method for multiple-roots
  20. Numerical modeling of thermal influence to pollutant dispersion and dynamics of particles motion with various sizes in idealized street canyon
  21. Construction of breather solutions and N-soliton for the higher order dimensional Caudrey–Dodd–Gibbon–Sawada–Kotera equation arising from wave patterns
  22. Delay-dependent robust stability analysis of uncertain fractional-order neutral systems with distributed delays and nonlinear perturbations subject to input saturation
  23. Construction of complexiton-type solutions using bilinear form of Hirota-type
  24. Inverse estimation of time-varying heat transfer coefficients for a hollow cylinder by using self-learning particle swarm optimization
  25. Infinite line of equilibriums in a novel fractional map with coexisting infinitely many attractors and initial offset boosting
  26. Lump solutions to a generalized nonlinear PDE with four fourth-order terms
  27. Quantum motion control for packaging machines
Heruntergeladen am 23.11.2025 von https://www.degruyterbrill.com/document/doi/10.1515/ijnsns-2020-0157/html
Button zum nach oben scrollen