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On Application of Kertesz Method for Exponential Estimation of Neural Network Model with Discrete Delays

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Engineer of the XXI Century (EngineerXXI 2018)

Part of the book series: Mechanisms and Machine Science ((Mechan. Machine Science,volume 70))

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Abstract

The purpose of this research is to develop method of calculation of exponential decay rate for neural network model based on differential equations with discrete delays. The exponential estimate is obtained using Kertesz method resulting in difference inequality for Lyapunov functional.

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References

  1. Haykin, S.: Neural networks: a comprehensive foundation. Macmillan Coll Div (1994) [Online]. Available: https://www.amazon.com/Neural-Networks-Comprehensive-Simon-Haykin/dp/0023527617%3FSubscriptionId%3D0JYN1NVW651KCA56C102%26tag%3Dtechkie20%26linkCode%3Dxm2%26camp%3D2025%26creative%3D165953%26creativeASIN%3D0023527617

  2. Ali, M.S., Balasubramaniam, P.: Global asymptotic stability of stochastic fuzzy cellular neural networks with multiple discrete and distributed time-varying delays. Commun. Nonlinear Sci. Numer. Simul. 16(7), 2907–2916 (2011). https://doi.org/10.1016/j.cnsns.2010.011 [Online]. Available: http://dx.doi.org/10.1016/j.cnsns.2010.10.011

  3. Wang, H., Yu, Y., Wen, G., Zhang, S., Yu, J.: Global stability analysis of fractional-order hopfield neural networks with time delay. Neurocomputing 154, 15–23 (2015). https://doi.org/10.1016/j.neucom.2014.12.031 [Online]. Available: http://dx.doi.org/10.1016/j.neucom.2014.12.031

  4. Ali, M.S. Arik, S., Saravanakumar, R.: Delay-dependent stability criteria of uncertain markovian jump neural networks with discrete interval and distributed time-varying delays. Neurocomputing 158, 167–173 (2015). https://doi.org/10.1016/j.neucom.2015.01.056 [Online]. Available: http://dx.doi.org/10.1016/j.neucom.2015.01.056

  5. Ali, M.S.: Robust stability of stochastic uncertain recurrent neural networks with markovian jumping parameters and time-varying delays. Int. J. Mach. Learn. Cybern. 5(1), 13–22 (2012). https://doi.org/10.1007/s13042-012-0124-6 [Online]. Available: http://dx.doi.org/10.1007/s13042-012-0124-6

  6. Ali, M.S., Saravanakumar, R., Arik, S.: Novel H∞ state estimation of static neural networks with interval time-varying delays via augmented lyapunov-krasovskii functional. Neurocomputing 171, 949–954 (2016). https://doi.org/10.1016/j.neucom.2015.07.038 [Online]. Available: http://dx.doi.org/10.1016/j.neucom.2015.07.038

  7. Chen, Y., Wu, Y.: Novel delay-dependent stability criteria of neural net- works with time-varying delay. Neurocomputing 72(4–6), 1065–1070 (2009). https://doi.org/10.1016/j.neucom.2008.03.006 [Online]. Available: http://dx.doi.org/10.1016/j.neucom.2008.03.006

  8. Huang, H., Feng, G., Cao, J.: Guaranteed performance state estimation of static neural networks with time-varying delay. Neurocomputing 74(4), 606–616 (2011). https://doi.org/10.1016/j.neucom.2010.09.017 [Online]. Available: http://dx.doi.org/10.1016/j.neucom.2010.09.017

  9. Huang, B., Zhang, H., Gong, D., Wang, J.: Synchronization analysis for static neural networks with hybrid couplings and time delays. Neurocomputing 148, 288–293 (2015). https://doi.org/10.1016/j.neucom.2013.11.053 [Online]. Available: https://doi.org/10.1016/j.neucom.2013.11.053

  10. Bula, I., Radin, M.A., Wilkins, N.: Neuron model with a period three internal decay rate. Electron. J. Qual. Theory Differ. Equ. (46), 1–19 (2017). https://doi.org/10.14232/ejqtde.2017.1.46 [Online]. Available: https://doi.org/10.14232/ejqtde.2017.1.46

  11. Park, J.H.: On global stability criterion for neural networks with discrete and distributed delays. Chaos, Solitons & Fractals 30(4), 897–902 (2006). https://doi.org/10.1016/j.chaos.2005.08.147 [Online]. Available: http://dx.doi.org/10.1016/j.chaos.2005.08.147

  12. Park, J.H., Cho, H.J.: A delay-dependent asymptotic stability crite-rion of cellular neural networks with time-varying discrete and distributed delays. Chaos, Solitons & Fractals 33(2), 436–442 (2007). https://doi.org/10.1016/j.chaos.2006.01.015 [Online]. Available: http://dx.doi.org/10.1016/j.chaos.2006.01.015

  13. Liao, X., Chen, G., Sanchez, E.N.: Delay-dependent exponential sta-bility analysis of delayed neural networks: an LMI approach. Neural Netw. 15(7), 855–866 (2002). https://doi.org/10.1016/s0893-6080(02)00041-2 [Online]. Available: https://doi.org/10.1016/s0893-6080(02)00041-2

  14. He, Y., Wang, Q.-G., Lin, C., Wu, M.: Delay-range-dependent stability for systems with time-varying delay. Automatica 43(2), 371–376 (2007). https://doi.org/10.1016/j.automatica.2006.08.015 [Online]. Available: http://dx.doi.org/10.1016/j.automatica.2006.08.015

  15. Lien, C.-H., Chung, L.-Y.: Global asymptotic stability for cellular neu-ral networks with discrete and distributed time-varying delays. Chaos, Solitons & Fractals 34(4), 1213–1219 (2007). https://doi.org/10.1016/j.chaos.2006.03.121 [Online]. Available: https://doi.org/10.1016/j.chaos.2006.03.121

  16. Zhang, Q., Wei, X., Xu, J.: Stability of delayed cellular neural net-works. Chaos, Solitons & Fractals 31(2), 514–520 (2007). https://doi.org/10.1016/j.chaos.2005.10.003 [Online]. Available: http://dx.doi.org/10.1016/j.chaos.2005.10.003

  17. Singh, V.: New global robust stability results for delayed cellular neural networks based on norm-bounded uncertainties. Chaos, Solitons & Fractals 30(5), 1165–1171 (2006). https://doi.org/10.1016/j.chaos.2005.08.183 [Online]. Available: http://dx.doi.org/10.1016/j.chaos.2005.08.183

  18. Martsenyuk, V.: On an indirect method of exponential estimation for a neural network model with discretely distributed delays. Electron. J. Qual. Theor. Differ. Equ. 23, 1–16 (2017). https://doi.org/10.14232/ejqtde.2017.1.23 [Online]. Available: https://doi.org/10.14232/ejqtde.2017.1.23

  19. Martsenyuk, V.: Indirect method of exponential convergence estimation for neural network with discrete and distributed delays. Electron. J. Differ. Equ. 2017(246), 1–12 (2017) [Online]. Available: https://ejde.math.txstate.edu/Volumes/2017/246/martsenyuk.pdf

  20. Khusainov, D., Marzeniuk, V.: Two-side estimates of solutions of linear systems with delay. Russian, Reports of Ukr.Nat.Acad.Sciences, pp. 8–13, 8 1996

    Google Scholar 

  21. Kertesz, V.: Stability investigations and exponential estimations for functional differential equations of retarded type. Acta Mathematica Hung. 55(3–4), 365–378 (1990)

    Google Scholar 

  22. Hale, J.K., Lunel, S.M.V.: Introduction to functional differential equations, vol. 99. Springer Science & Business Media (2013)

    Google Scholar 

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Nakonechnyi, O., Martsenyuk, V., Sverstiuk, A. (2020). On Application of Kertesz Method for Exponential Estimation of Neural Network Model with Discrete Delays. In: Zawiślak, S., Rysiński, J. (eds) Engineer of the XXI Century. EngineerXXI 2018. Mechanisms and Machine Science, vol 70. Springer, Cham. https://doi.org/10.1007/978-3-030-13321-4_14

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