Skip to main content
Log in

Stability Analysis of Quaternion-Valued Neutral Neural Networks with Generalized Activation Functions

  • Published:
Cognitive Computation Aims and scope Submit manuscript

Abstract

Stability is a central issue in the study of dynamical systems, and quaternion-valued neural networks (QVNNs) perform well in handling the problem involving high-dimension date. The paper is dedicated to investigating the stability problem of QVNNs with neutral delay. In order to accurately estimate the derivative of Lyapunov functional, both reciprocally convex inequality and Wirtinger-based inequality are extended to the quaternion domain. And the direct quaternion method is used to analyze the quaternion-valued neutral neural networks (QVNNNs). Based on the generalized inequalities, the existence, uniqueness, and global stability criteria for QVNNS with several freedom matrices are derived. Concision and compact stability criteria of QVNNNs are established in the form of quaternion-valued LMIs, and the correctness of the theoretical results was verified through a numerical example.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1

Similar content being viewed by others

Data Availability

All data generated or analyzed during this study are included in this article.

References

  1. Yang X, Li C, Song Q, Chen J, Huang J. Global Mittag-Leffler stability and synchronization analysis of fractional-order quaternion-valued neural networks with linear threshold neurons. Neural Netw. 2018;105:88–103.

    Article  Google Scholar 

  2. Wan A, Hong Q, Peng J, Wang M. Delay-independent criteria for exponential stability of generalized Cohen-Grossberg neural networks with discrete delays. Phys Lett A. 2006;353(2):151–7.

    Article  Google Scholar 

  3. Zhang W, Yang S, Li C, Zhang W, Yang X. Stochastic exponential synchronization of memristive neural networks with time-varying delays via quantized control. Neural Netw. 2018;104:93–103.

    Article  Google Scholar 

  4. Li R, Cao J, Alsaedi A, Alsaadi F. Exponential and fixed-time synchronization of Cohen-Grossberg neural networks with time-varying delays and reaction-diffusion terms. Appl Math Comput. 2017;313:37–51.

    MathSciNet  Google Scholar 

  5. Sheng Y, Huang T, Zeng Z. Exponential stabilization of fuzzy memristive neural networks with multiple time delays via intermittent control. IEEE Trans Syst Man Cybern: Syst. 2022;52(5):3092–101.

    Article  Google Scholar 

  6. Xu C, Liao M, Li P, Guo Y, Liu Z. Bifurcation properties for fractional order delayed BAM neural networks. Cogn Comput. 2021;13(2):322–56.

    Article  Google Scholar 

  7. Wei R, Cao J, Gorbachev S. Fixed-time control for memristor-based quaternion-valued neural networks with discontinuous activation functions. Cogn Comput. 2023;15(1):50–60.

    Article  Google Scholar 

  8. Zhang W, Huang J. Stability analysis of stochastic delayed differential systems with state-dependent-delay impulses: application of neural networks. Cogn Comput. 2022;14(2):805–13.

    Article  Google Scholar 

  9. Arena P, Baglio S, Fortuna L, Xibilia M. Chaotic time series prediction via quaternionic multilayer perceptrons. IEEE International Conference on Systems, Man and Cybernetics, Intelligent Systems for the 21st Century. 1995;1790–4.

  10. Kong G, Guo L. Stability analysis of delayed neural networks based on improved quadratic function condition. Neurocomputing. 2023;524:158–66.

    Article  Google Scholar 

  11. Chen J, Park J, Xu S. Stability analysis for delayed neural networks with an improved general free-matrix-based integral inequality. IEEE Trans Neural Netw Learn Syst. 2019;31(2):675–84.

    Article  MathSciNet  Google Scholar 

  12. Zhang Y, Zhou L. Novel global polynomial stability criteria of impulsive complex-valued neural networks with multi-proportional delays. Neural Comput Appl. 2022;34:2913–24.

    Article  Google Scholar 

  13. Song Q, Chen Y, Zhao Z, Liu Y, Alsaadi F. Robust stability of fractional-order quaternion-valued neural networks with neutral delays and parameter uncertainties. Neurocomputing. 2021;420:70–81.

    Article  Google Scholar 

  14. Zhang W, Huang J. Stability analysis of stochastic delayed differential systems with state-dependent-delay impulses: application of neural networks. Cogn Comput. 2022;14(2):805–13.

    Article  Google Scholar 

  15. Liu Y, Zhang D, Lu J, Cao J. Gobal μ-stability criteria for quaternion-valued neural networks with unbounded time-varying delays. Inf Sci. 2016;360:273–88.

    Article  Google Scholar 

  16. You X, Song Q, Liang J, Liu Y, Alsaadi F. Global μ-stability of quaternion-valued neural networks with mixed time-varying delays. Neurocomputing. 2018;290:12–25.

    Article  Google Scholar 

  17. Wu A, Zeng Z, Zhu X, Zhang J. Exponential synchronization of memristor-based recurrent neural networks with time delays. Neurocomputing. 2011;74(17):3043–50.

    Article  Google Scholar 

  18. Dhamala M, Jirsa V, Ding M. Enhancement of neural synchrony by time delay. Phys Rev Lett. 2004;92:074104.

    Article  Google Scholar 

  19. Wu A, Zeng Z. Exponential stabilization of memristive neural networks with time delays. IEEE Trans Neural Netw Learn Syst. 2012;23(12):1919–29.

    Article  Google Scholar 

  20. Yang X, Feng Y, Yiu K, Song Q, Alsaadi F. Synchronization of coupled neural networks with infinite-time distributed delays via quantized intermittent pinning control. Nonlinear Dyn. 2018;94(3):2289–303.

    Article  Google Scholar 

  21. Chen J, Zhang X, Park J, Xu S. Improved stability criteria for delayed neural networks using a quadratic function negative-definiteness approach. IEEE Trans Neural Netw Learn Syst. 2020;33(3):1348–54.

    Article  MathSciNet  Google Scholar 

  22. Du F, Lu J. New results on finite-time stability of fractional-order Cohen-Grossberg neural networks with time delays. Asian J Control. 2022;438:107–20.

    MathSciNet  Google Scholar 

  23. Hu X, Wang L, Zeng Z, Zhu S, Hu J. Settling-time estimation for finite-time stabilization of fractional-order quaternion-valued fuzzy NNs. IEEE Trans Fuzzy Syst. 2022;30(12):5460–72.

    Article  Google Scholar 

  24. Brayton R. Bifurcation of periodic solutions in a nonlinear difference-differential equation of neutral type. Q Appl Math. 1966;24:215–24.

    Article  MathSciNet  Google Scholar 

  25. Zhang Z, Zhang X, Yu T. Global exponential stability of neutral-type Cohen-Grossberg neural networks with multiple time-varying neutral and discrete delays. Neurocomputing. 2022;490:124–31.

    Article  Google Scholar 

  26. Wu X, Liu S, Wang H. Pinning synchronization of fractional memristor-based neural networks with neutral delays and reaction-diffusion terms. Commun Nonlinear Sci Numer Simul. 2023. https://doi.org/10.1016/j.cnsns.2022.107039.

    Article  MathSciNet  Google Scholar 

  27. Arik S. New criteria for stability of neutral-type neural networks with multiple time delays. IEEE Trans Neural Netw Learn Syst. 2019;31(5):1504–13.

    Article  MathSciNet  Google Scholar 

  28. Jian J, Wang B. Global Lagrange stability for neutral-type Cohen-Grossberg BAM neural networks with mixed time-varying delays. Math Comput Simul. 2015;116:1–25.

    Article  MathSciNet  Google Scholar 

  29. Xu D, Tan M. Delay-independent stability criteria for complex-valued BAM neutral-type neural networks with time delays. Nonlinear Dyn. 2017;89:819–32.

    Article  Google Scholar 

  30. Tu Z, Cao J, Alsaedi A, Alsaadi F, Hayat T. Global Lagrange stability of complex-valued neural networks of neutral type with time-varying delays. Complexity. 2016;21:438–50.

    Article  MathSciNet  Google Scholar 

  31. Shu J, Xiong L, Wu T, Lu Z. Stability analysis of quaternion-valued neutral-type neural networks with time-varying delay. Mathematics. 2019;7(1):1–23.

    Article  Google Scholar 

  32. Tu Z, Jian J, Wang B. Positive invariant and global exponential attractive sets of a class of neural networks with unbounded time-delays. Commun Nonlinear Sci Numer Simul. 2011;16:3738–45.

    Article  MathSciNet  Google Scholar 

  33. Liu Y, Wang Z, Liu X. Global exponential stability of generalized recurrent neural networks with discrete and distributed delays. Neural Netw. 2006;19(5):667–75.

    Article  Google Scholar 

  34. Kwon O, Park M, Lee S, Park J, Cha E. Stability for neural networks with time-varying delays via some new approaches. IEEE Trans Neural Netw Learn Syst. 2013;24(2):181–93.

    Article  Google Scholar 

  35. Zhang C, He Y, Jiang L, Wu M, Zeng H. Stability analysis of systems with time-varying delay via relaxed integral inequalities. Syst Control Lett. 2016;92:52–61.

    Article  MathSciNet  Google Scholar 

  36. Seuret A, Gouaisbaut F. Wirtinger-based integral inequality: application to time-delay systems. Automatica. 2013;49(9):2860–6.

    Article  MathSciNet  Google Scholar 

  37. Zhang C, He Y, Jiang L, Lin W, Wu M. Delay-dependent stability analysis of neural networks with time-varying delay: a generalized free-weighting-matrix approach. Appl Math Comput. 2017;294:102–20.

    MathSciNet  Google Scholar 

  38. Lin H, Zeng H, Zhang X, Wang W. Stability analysis for delayed neural networks via a generalized reciprocally convex inequality. IEEE Trans Neural Netw Learn Syst. 2023. https://doi.org/10.1109/TNNLS.2022.3144032.

    Article  MathSciNet  Google Scholar 

  39. Tu Z, Cao J, Alsaedi A, Hayat T. Global dissipativity analysis for delayed quaternion-valued neural networks. Neural Netw. 2017;89:97–104.

    Article  Google Scholar 

  40. Seuret A, Gouaisbaut F. Wirtinger-based integral inequality: application to time-delay systems. Automatica. 2013;49(9):2860–6.

    Article  MathSciNet  Google Scholar 

  41. Chen X, Li Z, Song Q, Hu J, Tan Y. Robust stability analysis of quaternion-valued neural networks with time delays and parameter uncertainties. Neural Netw. 2017;91:55–65.

    Article  Google Scholar 

  42. Boyd S, Ghaoui L, Feron E, Balakrishnan V. Linear matrix inequalities in system and control theory. Philadelphia: Society for industrial and applied mathematics; 1994.

    Book  Google Scholar 

  43. Isokawa T, Kusakabe T, Matsui N, Peper F. Quaternion neural network and its application, Knowledge-Based Intelligent Information and Engineering Systems. 7th International Conference, KES. Oxford, UK, September 2003. Proceedings, Part II, Springer, Berlin. 2003;2003:318–24.

  44. Isokawa T, Matsui N, Nishimura H. Quaternionic neural networks: fundamental properties and applications, in Complex-Valued Neural Networks: Utilizing High-Dimensional Parameters. Information Science Reference: Hershey, New York; 2009. p. 411–39.

    Google Scholar 

  45. Wei R, Cao J. Fixed-time synchronization of quaternion-valued memristive neural networks with time delays. Neural Netw. 2019;113:1–10.

    Article  Google Scholar 

  46. Wei R, Cao J. Synchronization control of quaternion-valued memristive neural networks with and without event-triggered scheme. Cogn Neurodyn. 2019;13(5):489–502.

    Article  Google Scholar 

  47. Tu Z, Cao J, Alsaedi A, Ahmad B. Stability analysis for delayed quaternion-valued neural networks via nonlinear measure approach. Nonlinear Anal: Model Control. 2018;23(3):361–79.

    Article  MathSciNet  Google Scholar 

Download references

Funding

This work was jointly supported by the National Natural Science Foundation of China under Grant No. 11601047, the Natural Science Foundation Project of Chongqing under grant Nos. cstc2021jcyj-msxmX0051, cstc2021jcyj-msxm2025, the Science and Technology Innovation Project of Economic Circle Construction in Chengdu-Chongqing Area under Grant No. KJCX2020047, the Science and Technology Research Program of Chongqing Municipal Education Commission under grant Nos. KJZD-K202201202, KJQN202101228, KJQN202101220, KJZD-M202001201.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Zhengwen Tu.

Ethics declarations

Ethics Approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Conflict of Interest

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wu, Y., Tu, Z., Dai, N. et al. Stability Analysis of Quaternion-Valued Neutral Neural Networks with Generalized Activation Functions. Cogn Comput 16, 392–403 (2024). https://doi.org/10.1007/s12559-023-10212-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12559-023-10212-w

Keywords

Navigation