Abstract
The main aim of this paper is to study the stability problem for neutral-type Hopfield neural networks possessing multiple time delays in the states of the neurons and multiple neutral delays in time derivative of states of the neurons. By using a suitable Lyapunov functional, a novel sufficient stability condition is obtained for global asymptotic stability of neutral-type neural networks with multiple delays. The derived stability criterion can be expressed in terms of the parameters of the neural network model which totally relies on some simple relationships established between the network parameters and it is completely independent of time delays and neutral delays. Hence, this new global asymptotic stability condition can be easily tested and verified by using some algebraic mathematical properties. We will also make a comparison between the result of this paper and previously published corresponding results. This comparison will indicate the advantages of our proposed stability condition over the previously reported stability conditions. Since obtaining stability conditions for neutral type neural networks with multiple delays is a difficult task to achieve due to the insufficient mathematical methods and techniques, the result given in this paper can be considered an important and alternative result for this class of neutral type neural systems.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Chua, L.O., Yang, L.: Cellular neural networks: applications. IEEE Trans. Circ. Syst. Part-I 35, 1273–1290 (1988)
Cohen, M., Grossberg, S.: Absolute stability of global pattern formation and parallel memory storage by competitive neural networks. IEEE Trans. Syst. Man Cybern. 13(5), 815–826 (1983)
Hopfield, J.: Neural networks and physical systems with emergent collective computational abilities. Proc. Nat. Acad. Sci. 79, 2554–2558 (1982)
Tong, S.C., Li, Y.M., Zhang, H.G.: Adaptive neural network decentralized backstepping output-feedback control for nonlinear large-scale systems with time delays. IEEE Trans. Neural Netw. 22(7), 1073–1086 (2011)
Galicki, M., Witte, H., Dorschel, J., Eiselt, M., Griessbach, G.: Common optimization of adaptive preprocessing units and a neural network during the learning period. Application in EEG pattern recognition. Neural Netw. 10, 1153–1163 (1997)
Kosko, B.: Bi-directional associative memories. IEEE Trans. Syst. Man Cybern. 18, 49–60 (1988)
Niculescu, S.I.: Delay Effects on Stability: A Robust Control Approach. Springer, Berlin (2001)
Kolmanovskii, V.B., Nosov, V.R.: Stability of Functional Differential Equations. Academic Press, London (1986)
Kuang, Y.: Delay Differential Equations with Applications in Population Dynamics. Academic Press, Boston (1993)
Samidurai, S., Marshal, A., Balachandran, R.K.: Global exponential stability of neutral-type impulsive neural networks with discrete and distributed delays. Nonlinear Anal. Hybrid Syst. 4(1), 103–112 (2010)
Shi, K., Zhu, H., Zhong, S., Zeng, Y., Zhang, Y.: New stability analysis for neutral type neural networks with discrete and distributed delays using a multiple integral approac. J. Franklin Inst. 352(1), 155–176 (2015)
Liu, P.L.: Further improvement on delay-dependent robust stability criteria for neutral-type recurrent neural networks with time-varying delays. ISA Trans. 55, 92–99 (2015)
Lee, S.M., Kwon, O.M., Park, J.H.: A novel delay-dependent criterion for delayed neural networks of neutral type. Phys. Lett. A 374(17–18), 1843–1848 (2010)
Chen, H., Zhang, Y., Hu, P.: Novel delay-dependent robust stability criteria for neutral stochastic delayed neural networks. Neurocomputing 73(13–15), 2554–25561 (2010)
Zhang, Z., Liu, K., Yang, Y.: New LMI-based condition on global asymptotic stability concerning BAM neural networks of neutral type. Neurocomputing 81(1), 24–32 (2012)
Lakshmanan, S., Park, J.H., Jung, H.Y., Kwon, O.M., Rakkiyappan, R.: A delay partitioning approach to delay-dependent stability analysis for neutral type neural networks with discrete and distributed delays. Neurocomputing 111, 81–89 (2013)
Dharani, S., Rakkiyappan, R., Cao, J.: New delay-dependent stability criteria for switched Hopfield neural networks of neutral type with additive time-varying delay components. Neurocomputing 151, 827–834 (2015)
Shi, K., Zhong, S., Zhu, H., Liu, X., Zeng, Y.: New delay-dependent stability criteria for neutral-type neural networks with mixed random time-varying delays. Neurocomputing 168, 896–907 (2015)
Zhang, G., Wang, T., Li, T., Fei, S.: Multiple integral Lyapunov approach to mixed-delay-dependent stability of neutral neural networks. Neurocomputing 275, 1782–1792 (2018)
Liu, P.L.: Improved delay-dependent stability of neutral type neural networks with distributed delays. ISA Trans. 52(6), 717–724 (2013)
Liao, X., Liu, Y., Wang, H., Huang, T.: Exponential estimates and exponential stability for neutral-type neural networks with multiple delays. Neurocomputing 149(3), 868–883 (2015)
Jian, J., Wang, B.: Stability analysis in Lagrange sense for a class of BAM neural networks of neutral type with multiple time-varying delays. Neurocomputing 149, 930–939 (2015)
Arik, S.: An analysis of stability of neutral-type neural systems with constant time delays. J. Franklin Inst. 351(11), 4949–4959 (2014)
Lien, C.H., Yu, K.W., Lin, Y.F., Chung, Y.J., Chung, L.Y.: Global exponential stability for uncertain delayed neural networks of neutral type with mixed time delays. IEEE Trans. Syst. Man Cybern. Part B Cybern. 38(3), 709–720 (2008)
Yang, Y., Liang, T., Xu, X.: Almost sure exponential stability of stochastic Cohen-Grossberg neural networks with continuous distributed delays of neutral type. Optik Int. J. Light Electron. Opt. 126(23), 4628–4635 (2015)
Samli, R., Arik, S.: New results for global stability of a class of neutral-type neural systems with time delays. Appl. Math. Comput. 210(2), 564–570 (2009)
Orman, Z.: New sufficient conditions for global stability of neutral-type neural networks with time delays. Neurocomputing 97, 141–148 (2012)
Cheng, C.J., Liao, T.L., Yan, J.J., Hwang, C.C.: Globally asymptotic stability of a class of neutral-type neural networks with delays. IEEE Trans. Syst. Man Cybern. Part B Cybern. 36(5), 1191–1195 (2008)
Akca, H., Covachev, V., Covacheva, Z.: Global asymptotic stability of Cohen-Grossberg neural networks of neutral type. J. Math. Sci. 205(6), 719–732 (2015)
Ozcan, N.: New conditions for global stability of neutral-type delayed Cohen-Grossberg neural networks. Neural Netw. 106, 1–7 (2018)
Arik, S.: New criteria for stability of neutral-type neural networks with multiple time delays. IEEE Trans. Neural Netw. Learn. Syst. (2019). https://doi.org/10.1109/TNNLS.2019.2920672
Arik, S.: A modified Lyapunov functional with application to stability of neutral-type neural networks with time delays. J. Franklin Inst. 356, 276–291 (2019)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Faydasicok, O. (2020). Stability Analysis of Neutral-Type Hopfield Neural Networks with Multiple Delays. In: Iliadis, L., Angelov, P., Jayne, C., Pimenidis, E. (eds) Proceedings of the 21st EANN (Engineering Applications of Neural Networks) 2020 Conference. EANN 2020. Proceedings of the International Neural Networks Society, vol 2. Springer, Cham. https://doi.org/10.1007/978-3-030-48791-1_44
Download citation
DOI: https://doi.org/10.1007/978-3-030-48791-1_44
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-48790-4
Online ISBN: 978-3-030-48791-1
eBook Packages: Computer ScienceComputer Science (R0)