Synchronisation of chaotic neural networks with unknown parameters and random time-varying delays based on adaptive sampled-data control and parameter identification

Access Full Text

Synchronisation of chaotic neural networks with unknown parameters and random time-varying delays based on adaptive sampled-data control and parameter identification

For access to this article, please select a purchase option:

Buy article PDF
£12.50
(plus tax if applicable)
Add to cart
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)
Add to cart

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Control Theory & Applications — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

© The Institution of Engineering and Technology
Published

This study investigates the synchronisation problem of chaotic neural networks with unknown parameters and random time-varying delays. By introducing a stochastic variable with Bernoulli distribution, the neural networks with random time-varying delays is transformed into one with deterministic varying delays and stochastic parameters. A simple and robust adaptive sampled-data controller is designed such that the response system can be synchronised with a drive system with unknown parameters by using suitable parameter identification and the Lyapunov stability theory. The proposed synchronisation criteria are easily verified and do not need to solve any linear matrix inequality. Numerical simulations are carried out to demonstrate the effectiveness of the established synchronisation laws.

Inspec keywords: linear matrix inequalities; parameter estimation; synchronisation; Lyapunov methods; time-varying systems; stochastic processes; delays; neural nets; adaptive control; robust control

Other keywords: stochastic variable; deterministic varying delays; robust adaptive sampled-data controller; Bernoulli distribution; synchronisation problem; linear matrix inequality; parameter identification; chaotic neural networks; unknown parameters; Lyapunov stability theory; random time-varying delays

Subjects: Time-varying control systems; Distributed parameter control systems; Linear algebra (numerical analysis); Other topics in statistics; Self-adjusting control systems; Stability in control theory; Neural nets (theory)

References

    1. 1)
    2. 2)
      • Gu, K.: `An integral inequality in the stability problem of time-delay system', Proc. 39th IEEE Conf. Decision and Control, 2000, p. 2805–2810.
    3. 3)
    4. 4)
    5. 5)
    6. 6)
    7. 7)
    8. 8)
    9. 9)
    10. 10)
    11. 11)
    12. 12)
    13. 13)
    14. 14)
    15. 15)
    16. 16)
      • S. Boyd , L.E. Ghaoui , E. Feron , V. Balakrishnan . (1994) Linear matrix inequalities in system and control theory.
    17. 17)
    18. 18)
    19. 19)
    20. 20)
    21. 21)
      • C. Zhang , H. Zhang , Z. Wang . Improved robust stability criteria for delayed cellular neural networks via the LMI approach. IEEE Trans. Neural Netw. , 41 - 45
    22. 22)
    23. 23)
    24. 24)
    25. 25)
    26. 26)
    27. 27)
    28. 28)
    29. 29)
    30. 30)
    31. 31)
    32. 32)
    33. 33)
    34. 34)
    35. 35)
    36. 36)
    37. 37)
    38. 38)
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-cta.2011.0426
Loading

Related content

content/journals/10.1049/iet-cta.2011.0426
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading