Home Global dissipativity of non-autonomous BAM neural networks with mixed time-varying delays and discontinuous activations
Article
Licensed
Unlicensed Requires Authentication

Global dissipativity of non-autonomous BAM neural networks with mixed time-varying delays and discontinuous activations

  • Meng Yan , Minghui Jiang EMAIL logo and Kaifang Fei
Published/Copyright: November 30, 2021

Abstract

In this paper, we investigate the dissipativity of a class of BAM neural networks with both time-varying and distributed delays, as well as discontinuous activations. First, the concept of the Filippov solution is extended to functional differential equations with discontinuous right-hand sides via functional differential inclusions. Then, by constructing Lyapunov functional and employing a generalized Halanay inequality, several sufficient easy-to-test conditions are successfully obtained to guarantee the global dissipativity of the Filippov solution of the considered system. The derived results extend and improve some previous publications on conventional BAM neural networks. Meanwhile, the estimations of the positive invariant and globally attractive set are given. Finally, numerical simulations are provided to demonstrate the effectiveness of our proposed results.


Corresponding author: Minghui Jiang, Institute of Nonlinear Complex Systems, China Three Gorges University, YiChang, Hubei 443000, China; and Three Gorges Mathematical Research Center, China Three Gorges University, YiChang, Hubei 443000, China, E-mail:

Funding source: National Natural Science Foundation of China

Award Identifier / Grant number: 61374028 and 61304162

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

  2. Research funding: The research is supported by National Natural Science Foundation of China (Grant Nos. 61374028 and 61304162).

  3. Conflict of interest statement: The authors declare no conflicts of interest regarding this article.

References

[1] J. Corts, “Discontinuous dynamical systems,” IEEE Control Syst. Mag., vol. 28, pp. 36–73, 2008. https://doi.org/10.1109/MCS.2008.919306.Search in Google Scholar

[2] A. F. Filippov, Differential Equations with Discontinuous Right-Hand Side, Soviet Series, Mathematics and Its Applications, Boston, Kluwer Academic, 1988.10.1007/978-94-015-7793-9Search in Google Scholar

[3] A. C. J. Luo, Discontinuous Dynamical Systems on Time-Varying Domains, Beijing, Higher Education Press, 2009.10.1007/978-3-642-00253-3Search in Google Scholar

[4] W. Lu and T. Chen, “Dynamical behaviors of Cohen–Grossberg neural networks with discontinuous activation functions,” Neural Network., vol. 18, pp. 231–242, 2005. https://doi.org/10.1016/j.neunet.2004.09.004.Search in Google Scholar

[5] W. Lu and T. Chen, “Almost periodic dynamics of a class of delayed neural networks with discontinuous activations,” Neural Comput., vol. 20, pp. 1065–1090, 2008. https://doi.org/10.1162/neco.2008.10-06-364.Search in Google Scholar PubMed

[6] L. H. Huang, J. F. Wang, and X. N. Zhou, “Existence and global asymptotic stability of periodic solutions for Hopfield neural networks with discontinuous activations,” Nonlinear Anal. R. World Appl., vol. 10, pp. 1651–1661, 2009. https://doi.org/10.1016/j.nonrwa.2008.02.022.Search in Google Scholar

[7] X. Y. Liu, T. P. Chen, J. D. Cao, and W. L. Lu, “Dissipativity and quasi-synchronization for neural networks with discontinuous activations and parameter mismatches,” Neural Network., vol. 24, pp. 1013–1021, 2011. https://doi.org/10.1016/j.neunet.2011.06.005.Search in Google Scholar PubMed

[8] Z. W. Cai, L. H. Huang, and L. L. Zhang, “New exponential synchronization criteria for time-varying delayed neural networks with discontinuous activations,” Neural Network., vol. 65, pp. 105–114, 2005. https://doi.org/10.1016/j.neunet.2015.02.001.Search in Google Scholar PubMed

[9] 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, pp. 1449–1463, 2005. https://doi.org/10.1109/tnn.2005.852862.Search in Google Scholar PubMed

[10] L. Ferreira, E. Kaszkurewicz, and A. Bhaya, “Solving systems of linear equations via gradient systems with discontinuous right hand sides: application to LSSVM,” IEEE Trans. Neural Network., vol. 16, pp. 501–505, 2005. https://doi.org/10.1109/tnn.2005.844091.Search in Google Scholar

[11] A. F. Filippov, Differential Equations with Discontinuous Right-Hand Side, Mathematics and Its Applications (Soviet Series), Boston, Kluwer Academic Publishers, 1988.10.1007/978-94-015-7793-9Search in Google Scholar

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

[13] A. Abdurahman, H. Jiang, and Z. Teng, “Finite-time synchronization for memristor-based neural networks with time-varying delays,” Neural Network., vol. 69, pp. 20–28, 2015. https://doi.org/10.1016/j.neunet.2015.04.015.Search in Google Scholar PubMed

[14] A. Abdurahman and H. Jiang, “New results on exponential synchronization of memristor-based neural networks with discontinuous neuron activations,” Neural Network., vol. 84, pp. 161–171, 2016. https://doi.org/10.1016/j.neunet.2016.09.003.Search in Google Scholar PubMed

[15] B. Kosko, “Adaptive bi-directional associative memories,” Appl. Opt., vol. 26, pp. 4947–4960, 1987. https://doi.org/10.1364/ao.26.004947.Search in Google Scholar PubMed

[16] B. Kosko, “Bi-directional associative memories,” IEEE Trans. Syst. Man Cybern., vol. 18, pp. 49–60, 1988. https://doi.org/10.1109/21.87054.Search in Google Scholar

[17] G. D. Zhang and Z. G. Zeng, “Exponential stability for a class of memristive neural networks with mixed time-varying delays,” Appl. Math. Comput., vol. 321, pp. 544–554, 2018. https://doi.org/10.1016/j.amc.2017.11.022.Search in Google Scholar

[18] D. S. Wang, L. H. Huang, and L. K. Tang, “Synchronization criteria for discontinuous neural networks with mixed delays via functional differential inclusions,” IEEE Trans. Neural Network. Learn. Syst., vol. 29, pp. 1809–1821, 2018. https://doi.org/10.1109/tnnls.2017.2688327.Search in Google Scholar PubMed

[19] C. J. Xu and Q. M. Zhang, “Existence and global exponential stability of anti-periodic solutions of high-order bidirectional associative memory (BAM) networks with time-varying delays on time scales,” J. Comput. Sci., vol. 8, pp. 48–61, 2015. https://doi.org/10.1016/j.jocs.2015.02.008.Search in Google Scholar

[20] C. J. Xu, Z. X. Liu, M. X. Liao, P. L. Li, Q. M. Xiao, and S. Yuan, “Fractional-order bidirectional associate memory (BAM) neural networks with multiple delays: the case of Hopf bifurcation,” Math. Comput. Simulat., vol. 182, pp. 471–494, 2021. https://doi.org/10.1016/j.matcom.2020.11.023.Search in Google Scholar

[21] C. Marcuss and R. Westervelt, “Stability of analog neural networks with distributed delays,” Phys. Rev., vol. 39, pp. 347–359, 1989. https://doi.org/10.1103/physreva.39.347.Search in Google Scholar

[22] P. P. Civalleri, M. Gilli, and L. Pandolfi, “On stability of cellular neural networks with delay,” IEEE Trans. Circ. Syst. I, vol. 40, pp. 157–165, 1993. https://doi.org/10.1109/81.222796.Search in Google Scholar

[23] C. M. Marcus and R. M. Westervelt, “Stability of analog neural networks with delay,” Phys. Rev., vol. 39, pp. 347–359, 1989. https://doi.org/10.1103/physreva.39.347.Search in Google Scholar

[24] P. L. Venetianer and T. Roska, “Image compression by delayed CNNs,” IEEE Trans. Circ. Syst., vol. 45, pp. 205–215, 1998.10.1109/81.662694Search in Google Scholar

[25] C. H. Li and S. Y. Yang, “Global attractively in delayed Cohen–Grossberg neural network models,” Chaos, Solit. Fractals, vol. 39, pp. 1975–1987, 2009. https://doi.org/10.1016/j.chaos.2007.06.064.Search in Google Scholar

[26] J. Y. Zhang and X. S. Jin, “Global stability analysis in delayed Hopfield neural network models,” Neural Network., vol. 13, pp. 745–753, 2000. https://doi.org/10.1016/s0893-6080(00)00050-2.Search in Google Scholar

[27] C. J. Xu, X. M. Liao and P. L. Li, “Bifurcation control of a fractional-order delayed competition and cooperation model of two enterprises,” Sci. China Technol. Sci., vol. 62, pp. 2130–2143, 2019. https://doi.org/10.1007/s11431-018-9376-2.Search in Google Scholar

[28] C. J. Xu and Q. M. Zhang, “Existence and exponential stability of anti-periodic solutions for a high-order delayed Cohen–Grossberg neural networks with impulsive effects,” Neural Process. Lett., vol. 40, pp. 227–243, 2014. https://doi.org/10.1007/s11063-013-9325-6.Search in Google Scholar

[29] J. F. Wang, L. H. Huang, and Z. Y. Guo, “Dynamical behavior of delayed Hopfield neural networks with discontinuous activations,” Appl. Math. Model., vol. 33, pp. 1793–1802, 2009. https://doi.org/10.1016/j.apm.2008.03.023.Search in Google Scholar

[30] Y. Wang, Y. Zuo, L. Huang, and C. Li, “Global robust stability of delayed neural networks with discontinuous activation functions,” IET Control Theory Appl., vol. 2, pp. 543–553, 2008. https://doi.org/10.1049/iet-cta:20070323.10.1049/iet-cta:20070323Search in Google Scholar

[31] C. J. Xu, X. M. Liao, P. L. Li, Y. Guo, Q. M. Xiao, and S. Yuan, “Influence of multiple time delays on bifurcation of fractional-order neural networks,” Appl. Math. Comput., vol. 361, pp. 565–582, 2019. https://doi.org/10.1016/j.amc.2019.05.057.Search in Google Scholar

[32] C. J. Xu, X. M. Liao, P. L. Li, Z. X. Liu, and S. Yuan, “New results on pseudo almost periodic solutions of quaternion-valued fuzzy cellular neural networks with delays,” Fuzzy Set Syst., vol. 411, p. 25, 2020. https://doi.org/10.1016/j.fss.2020.03.016.Search in Google Scholar

[33] X. Wu, Y. Wang, L. Huang, and Y. Zuo, “Robust exponential stability criterion for uncertain neural networks with discontinuous activation functions and time-varying delays,” Neurocomputing, vol. 73, pp. 1265–1271, 2010. https://doi.org/10.1016/j.neucom.2010.01.002.Search in Google Scholar

[34] Y. Zuo, Y. Wang, L. Huang, Z. Wang, X. Liu, and X. Wu, “Robust stability criterion for delayed neural networks with discontinuous activation functions,” Neural Process. Lett., vol. 29, pp. 29–44, 2009. https://doi.org/10.1007/s11063-008-9093-x.Search in Google Scholar

[35] H. Q. Wu and Y. W. Li, “Existence and stability of periodic solution for BAM neural networks with discontinuous neuron activations,” Comput. Math. Appl., vol. 56, pp. 1981–1993, 2008. https://doi.org/10.1016/j.camwa.2008.04.027.Search in Google Scholar

[36] W. Allegretto, D. Papini, and M. Forti, “Common asymptotic behavior of solutions and almost periodicity for discontinuous, delayed, and impulsive neural networks,” IEEE Trans. Neural Network., vol. 21, pp. 1110–1125, 2010. https://doi.org/10.1109/tnn.2010.2048759.Search in Google Scholar

[37] M. Forti, M. Grazzini, P. Nistri, and L. Pancioni, “Generalized Lyapunov approach for convergence of neural networks with discontinuous or non-Lipschitz activations,” Physica D, vol. 214, pp. 88–89, 2006. https://doi.org/10.1016/j.physd.2005.12.006.Search in Google Scholar

[38] L. H. Huang, Z. W. Cai, L. L. Zhang, and L. Duan, “Dynamical behaviors for discontinuous and delayed neural networks in the framework of Filippov differential inclusions,” Neural Network., vol. 48, pp. 180–194, 2013. https://doi.org/10.1016/j.neunet.2013.08.004.Search in Google Scholar PubMed

[39] C. J. Xu, P. L. Li, and Y. C. Pang, “Exponential stability of almost periodic solutions for memristor-based neural networks with distributed leakage delays,” Neural Comput., vol. 28, pp. 1–31, 2016. https://doi.org/10.1162/neco_a_00895.Search in Google Scholar PubMed

[40] C. J. Xu and Q. M. Zhang, “On antiperiodic solutions for Cohen–Grossberg shunting inhibitory neural networks with time-varying delays and impulses,” Neural Comput., vol. 26, pp. 2328–2349, 2014. https://doi.org/10.1162/neco_a_00642.Search in Google Scholar PubMed

[41] X. X. Liao and J. Wang, “Global dissipativity of continuous-time recurrent neural networks with time delay,” Phys. Rev., vol. 68, p. 016118, 2003. https://doi.org/10.1103/PhysRevE.68.016118.Search in Google Scholar PubMed

[42] Q. K. Song and Z. J. Zhao, “Global dissipativity of neural networks with both variable and unbounded delays,” Chaos, Solit. Fractals, vol. 25, pp. 393–401, 2005. https://doi.org/10.1016/j.chaos.2004.11.035.Search in Google Scholar

[43] J. J. Xing, H. J. Jiang, and C. Hu, “Exponential synchronization for delayed recurrent neural networks via periodically intermittent control,” Neurocomputing, vol. 113, pp. 122–129, 2013. https://doi.org/10.1016/j.neucom.2013.01.041.Search in Google Scholar

[44] D. S. Wang, L. H. Huang, and L. K. Tang, “Dissipativity and synchronization of generalized BAM neural networks with multivariate discontinuous activations,” IEEE Trans. Neural Network. Learn. Syst., vol. 29, pp. 3815–3827, 2018. https://doi.org/10.1109/TNNLS.2017.2741349.Search in Google Scholar PubMed

[45] H. F. Li, C. Li, W. Zhang, and J. Xu, “Global dissipativity of inertial neural networks with proportional delay via new generalized Halanay inequalities,” Neural Process. Lett., vol. 48, pp. 1543–1561, 2018. https://doi.org/10.1007/s11063-018-9788-6.Search in Google Scholar

[46] Z. Y. Guo, J. Wang, and Z. Yan, “Global exponential dissipativity and stabilization of memristor-based recurrent neural networks with time-varying delays,” Neural Network., vol. 48, pp. 158–172, 2013. https://doi.org/10.1016/j.neunet.2013.08.002.Search in Google Scholar PubMed

[47] Z. G. Feng and W. X. Zheng, “On extended dissipativity of discrete-time neural networks with time delay,” IEEE Trans. Neural Network. Learn. Syst., vol. 26, pp. 3293–3300, 2015. https://doi.org/10.1109/tnnls.2015.2399421.Search in Google Scholar PubMed

[48] R. Manivannan, R. Samidurai, J. D. Cao, A. Alsaedi, and F. E. Alsaadi, “Global exponential stability and dissipativity of generalized neural networks with time-varying delay signals,” Neural Network., vol. 87, pp. 149–159, 2017. https://doi.org/10.1016/j.neunet.2016.12.005.Search in Google Scholar PubMed

[49] Z. W. Tu, J. D. Cao, A. Alsaedi, and T. Hayat, “Global dissipativity analysis for delayed quaternion-valued neural networks,” Neural Network., vol. 89, pp. 97–104, 2017. https://doi.org/10.1016/j.neunet.2017.01.006.Search in Google Scholar PubMed

[50] C. Zhao, S. M. Zhong, X. J. Zhang, and K. B. Shi, “Novel results on dissipativity analysis for generalized delayed neural networks,” Neurocomputing, vol. 332, pp. 328–338, 2019. https://doi.org/10.1016/j.neucom.2018.12.013.Search in Google Scholar

[51] C. Baker and A. Tang, “Generalized Halanay inequalities for Volterra functional differential equations and discretized versions,” Proc. Volterra Centen. Meet., vol. 6, pp. 39–55, 1996.Search in Google Scholar

[52] Z. Zhao and J. Jian, “Positive invariant sets and global exponential attractive sets of BAM neural networks with time-varying and infinite distributed delays,” Neurocomputing, vol. 142, pp. 447–457, 2014. https://doi.org/10.1016/j.neucom.2014.03.050.Search in Google Scholar

[53] L. Wen, Y. Yu, and W. Wang, “Generalized Halanay inequalities for dissipativity of Volterra functional differential equations,” J. Math. Anal. Appl., vol. 347, pp. 169–178, 2008. https://doi.org/10.1016/j.jmaa.2008.05.007.Search in Google Scholar

[54] H. Tian, “Numerical and analytic dissipativity of the θ-method for delay differential equation with a bounded variable lag,” Int. J. Bifurcat. Chaos, vol. 14, pp. 1839–1845, 2004. https://doi.org/10.1142/s0218127404010096.Search in Google Scholar

[55] Z. W. Cai and L. H. Huang, “Functional differential inclusions and dynamic behaviors for memristor-based BAM neural networks with time-varying delays,” Commun. Nonlinear Sci. Numer. Simulat., vol. 19, pp. 1279–1300, 2014. https://doi.org/10.1016/j.cnsns.2013.09.004.Search in Google Scholar

[56] Z. W. Tu, L. W. Wang, Z. W. Zha, and J. G. Jian, “Global dissipativity of a class of BAM neural networks with time-varying and unbound delays,” Commun. Nonlinear Sci. Numer. Simulat., vol. 18, pp. 2562–2570, 2013. https://doi.org/10.1016/j.cnsns.2013.01.014.Search in Google Scholar

[57] L. S. Wang, L. Zhang, and X. H. Ding, “Global dissipativity of a class of BAM neural networks with both time-varying and continuously distributed delays,” Neurocomputing, vol. 152, pp. 250–260, 2015. https://doi.org/10.1016/j.neucom.2014.10.070.Search in Google Scholar

[58] L. Duan, L. H. Huang, and Z. Y. Guo, “Global robust dissipativity of interval recurrent neural networks with time-varying delay and discontinuous activations,” Chaos, vol. 26, pp. 073101, 2016. https://doi.org/10.1063/1.4945798.Search in Google Scholar PubMed

[59] L. Duan and L. H. Huang, “Global dissipativity of mixed time-varying delayed neural networks with discontinuous activations,” Commun. Nonlinear Sci. Numer. Simulat., vol. 19, pp. 4122–4134, 2014. https://doi.org/10.1016/j.cnsns.2014.03.024.Search in Google Scholar

[60] L. Duan, M. Shi, and L. H. Huang, “New results on finite-/fixed-time synchronization of delayed diffusive fuzzy HNNs with discontinuous activations,” Fuzzy Set Syst., vol. 416, pp. 141–151, 2020. https://doi.org/10.1016/j.fss.2020.04.016.Search in Google Scholar

[61] C. X. Huang, X. Long, and J. D. Cao, “Stability of antiperiodic recurrent neural networks with multiproportional delays,” Math. Methods Appl. Sci., vol. 43, pp. 6093–6102, 2020. https://doi.org/10.1002/mma.6350.Search in Google Scholar

[62] R. Y. Wei, J. D. Cao, and C. X. Huang, “Lagrange exponential stability of quaternion-valued memristive neural networks with time delays,” Math. Methods Appl. Sci., vol. 43, pp. 7269–7291, 2020. https://doi.org/10.1002/mma.6463.Search in Google Scholar

[63] M. Vidyasagar, Nonlinear System Analysis, Englewood Cliffs, Prentice-Hall, 1993.Search in Google Scholar

[64] J. P. Aubin and A. Cellina, Differential Inclusions, Berlin, Springer-Verlag, 1984.10.1007/978-3-642-69512-4Search in Google Scholar

[65] G. Haddad, “Monotone viable trajectories for functional differential inclusions,” J. Differ. Equ., vol. 42, pp. 1–24, 1981. https://doi.org/10.1016/0022-0396(81)90031-0.Search in Google Scholar

[66] J. P. Aubin, Viability Theory, Boston, Birkhauser, 1991.Search in Google Scholar

Received: 2019-03-05
Revised: 2021-06-10
Accepted: 2021-11-04
Published Online: 2021-11-30
Published in Print: 2022-06-25

© 2021 Walter de Gruyter GmbH, Berlin/Boston

Articles in the same Issue

  1. Frontmatter
  2. Original Research Articles
  3. Dynamics of synthetic drug transmission models
  4. Collision-induced amplitude dynamics of pulses in linear waveguides with the generic nonlinear loss
  5. Global dissipativity of non-autonomous BAM neural networks with mixed time-varying delays and discontinuous activations
  6. On the inverse problem for nonlinear strongly damped wave equations with discrete random noise
  7. A second-order nonlocal regularized variational model for multiframe image super-resolution
  8. Algebro-geometric integration of a modified shallow wave hierarchy
  9. Singularity analysis of a 7-DOF spatial hybrid manipulator for medical surgery
  10. Propagation of diffusing pollutant by kinetic flux-vector splitting method
  11. Limit cycles in a tritrophic food chain model with general functional responses
  12. Local and parallel stabilized finite element methods based on full domain decomposition for the stationary Stokes equations
  13. Stress concentration effect on deflection and stress fields of a master leaf spring through domain decomposition and geometry updation technique
  14. Electrostatically actuated double walled piezoelectric nanoshell subjected to nonlinear van der Waals effect: nonclassical vibrations and stability analysis
  15. Traveling wave solutions of the generalized Rosenau–Kawahara-RLW equation via the sine–cosine method and a generalized auxiliary equation method
  16. A predictor–corrector compact finite difference scheme for a nonlinear partial integro-differential equation
  17. Parameter inference with analytical propagators for stochastic models of autoregulated gene expression
  18. DCSK performance analysis of a chaos-based communication using a newly designed chaotic system
  19. On successive linearization method for differential equations with nonlinear conditions
  20. Comparison of different time discretization schemes for solving the Allen–Cahn equation
  21. The homoclinic breather wave solution, rational wave and n-soliton solution to a nonlinear differential equation
  22. Diversity of interaction phenomenon, cross-kink wave, and the bright-dark solitons for the (3 + 1)-dimensional Kadomtsev–Petviashvili–Boussinesq-like equation
Downloaded on 23.11.2025 from https://www.degruyterbrill.com/document/doi/10.1515/ijnsns-2019-0078/html
Scroll to top button