Advertisement

Global Stabilization for Delayed Fuzzy Inertial Neural Networks

  • Qiang Xiao
  • Tingwen Huang
  • Zhigang ZengEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11555)

Abstract

This paper studies the global stabilization problem for a class of fuzzy inertial neural networks (FINN) with time delays and deals with the FINN directly by non-reduced order method. By Lyapunov theory and some analytical techniques, some criteria of global asymptotic and exponential stabilization for the considered FINN are obtained. An example is given to show the effectiveness and validity of the theoretical results.

Keywords

Asymptotic and exponential stabilization Inertial neural networks Non-reduced order method T-S fuzzy logic Time delay 

References

  1. 1.
    Liu, X., Zeng, Z., Wen, S.: Implementation of memristive neural networks with full-function Pavlov associative memory. IEEE Trans. Circuits Syst. I Reg. Papers 63(9), 1454–1463 (2016)Google Scholar
  2. 2.
    Hayakawa, Y., Nakajima, K.: Design of the inverse function delayed neural networks for solving combinatorial optimization problems. IEEE Trans. Neural Netw. 21, 224–237 (2010)Google Scholar
  3. 3.
    Cheng, G., Zhou, P., Han, J.: Learning rotation-invariant convolutional neural networks for object detection in VHR optical remote sensing images. IEEE Trans. Geosci. Remote Sens. 54, 7405–7415 (2016)Google Scholar
  4. 4.
    Zhao, X., Shi, P., Zheng, X., Zhang, J.: Intelligent tracking control for a class of uncertain high-order nonlinear systems. IEEE Trans. Neural Netw. Learn. Syst. 27, 1976–1982 (2016)Google Scholar
  5. 5.
    Guo, Z., Yang, S., Wang, J.: Global synchronization of memristive neural networks subject to random disturbances via distributed pinning control. Neural Netw. 84, 67–79 (2016)Google Scholar
  6. 6.
    Yang, W., Yu, W., Cao, J., Alsaadi, F., Hayat, T.: Global exponential stability and lag synchronization for delayed memristive fuzzy Cohen-Grossberg BAM neural networks with impulses. Neural Netw. 98, 122–153 (2018)Google Scholar
  7. 7.
    Zhou, Y., Li, C., Chen, L., Huang, T.: Global exponential stability of memristive Cohen-Grossberg neural networks with mixed delays and impulse time window. Neurocomputing 31, 2384–2391 (2018)Google Scholar
  8. 8.
    Wen, S., Zeng, Z., Huang, T., Yu, X.: Noise cancellation of memristive neural networks. Neural Netw. 60, 74–83 (2014)Google Scholar
  9. 9.
    Takagi, T., Sugeno, M.: Fuzzy identification of systems and its applications to modelling and control. IEEE Trans. Syst. Man Cybern. 15, 116–132 (1985)Google Scholar
  10. 10.
    Zhao, X., Yin, Y., Niu, B., Zheng, X.: Stabilization for a class of switched nonlinear systems with novel average dwell time switching by T-S fuzzy modeling. IEEE Trans. Cybern. 46, 1952–1957 (2016)Google Scholar
  11. 11.
    Li, Y., Liu, L., Feng, G.: Adaptive finite-time controller design for T-S fuzzy systems. IEEE Trans. Cybern. 47(9), 2425–2436 (2017).  https://doi.org/10.1109/TCYB.2017.2671902Google Scholar
  12. 12.
    Babcock, K., Westervelt, R.: Stability and dynamics of simple electronic neural networks with added inertia. Physica D 23, 464–469 (1986)Google Scholar
  13. 13.
    Cao, J., Wan, Y.: Matrix measure strategies for stability and synchronization of inertial BAM network with time delays. Neural Netw. 53, 165–172 (2014)Google Scholar
  14. 14.
    Tu, Z., Cao, J., Hayat, T.: Matrix measure based dissipativity analysis for inertial delayed uncertain neural networks. Neural Netw. 75, 47–55 (2016)Google Scholar
  15. 15.
    Li, X., Li, X., Hu, C.: Some new results on stability and synchronization for delayed inertial neural networks based on non-reduced order method. Neural Netw. 96, 91–100 (2017)Google Scholar
  16. 16.
    Zhang, G., Zeng, Z.: Exponential stability for a class of memristive neural networks with mixed time-varying delays. Appl. Math. Comput. 321, 544–554 (2018)Google Scholar
  17. 17.
    Xiao, Q., Huang, Z., Zeng, Z.: Passivity analysis for memristor-based inertial neural networks with discrete and distributed delays. IEEE Trans. Syst. Man Cybern: Syst. 49, 375–385 (2019)Google Scholar
  18. 18.
    Xiao, Q., Huang, T., Zeng, Z.: Passivity and passification of fuzzy memristive inertial neural networks on time scales. IEEE Trans. Fuzzy Syst. 26, 3342–3355 (2018)Google Scholar
  19. 19.
    Xiao, Q., Huang, T., Zeng, Z.: Global exponential stability and synchronization for a class of generalized discrete-time inertial neural networks with time delays. IEEE Trans. Neural Netw. Learn. Syst.  https://doi.org/10.1109/TNNLS.2018.2874982
  20. 20.
    Gong, S., Yang, S., Guo, Z., Huang, T.: Global exponential synchronization of inertial memristive neural networks with time-varying delay via nonlinear controller. Neural Netw. 102, 138–148 (2018)Google Scholar
  21. 21.
    Guo, Z., Gong, S., Huang, T.: Finite-time synchronization of inertial memristive neural networks with time delay via delay-dependent control. Neurocomputing 293, 100–107 (2018)Google Scholar
  22. 22.
    Popov, V.: Hyperstability of Control Systems. Springer, New York (1973)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.School of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhanChina
  2. 2.Key Laboratory of Image Processing and Intelligent Control of Education Ministry of ChinaWuhanChina
  3. 3.Department of MathematicsTexas A&M University at QatarDohaQatar

Personalised recommendations