Existence, uniqueness and global asymptotic stability for a class of complex-valued neutral-type neural networks with time delays

Manchun Tan; Desheng Xu

Kybernetika (2018)

  • Volume: 54, Issue: 4, page 844-863
  • ISSN: 0023-5954

Abstract

top
This paper explores the problem of delay-independent and delay-dependent stability for a class of complex-valued neutral-type neural networks with time delays. Aiming at the neutral-type neural networks, an appropriate function is constructed to derive the existence of equilibrium point. On the basis of homeomorphism theory, Lyapunov functional method and linear matrix inequality techniques, several LMI-based sufficient conditions on the existence, uniqueness and global asymptotic stability of equilibrium point for complex-valued neutral-type neural networks are obtained. Finally, numerical examples are given to illustrate the feasibility and the effectiveness of the proposed theoretical results.

How to cite

top

Tan, Manchun, and Xu, Desheng. "Existence, uniqueness and global asymptotic stability for a class of complex-valued neutral-type neural networks with time delays." Kybernetika 54.4 (2018): 844-863. <http://eudml.org/doc/294754>.

@article{Tan2018,
abstract = {This paper explores the problem of delay-independent and delay-dependent stability for a class of complex-valued neutral-type neural networks with time delays. Aiming at the neutral-type neural networks, an appropriate function is constructed to derive the existence of equilibrium point. On the basis of homeomorphism theory, Lyapunov functional method and linear matrix inequality techniques, several LMI-based sufficient conditions on the existence, uniqueness and global asymptotic stability of equilibrium point for complex-valued neutral-type neural networks are obtained. Finally, numerical examples are given to illustrate the feasibility and the effectiveness of the proposed theoretical results.},
author = {Tan, Manchun, Xu, Desheng},
journal = {Kybernetika},
keywords = {complex-valued neutral-type neural networks; existence and uniqueness of equilibrium; global asymptotic stability; inequality techniques; Lyapunov functional},
language = {eng},
number = {4},
pages = {844-863},
publisher = {Institute of Information Theory and Automation AS CR},
title = {Existence, uniqueness and global asymptotic stability for a class of complex-valued neutral-type neural networks with time delays},
url = {http://eudml.org/doc/294754},
volume = {54},
year = {2018},
}

TY - JOUR
AU - Tan, Manchun
AU - Xu, Desheng
TI - Existence, uniqueness and global asymptotic stability for a class of complex-valued neutral-type neural networks with time delays
JO - Kybernetika
PY - 2018
PB - Institute of Information Theory and Automation AS CR
VL - 54
IS - 4
SP - 844
EP - 863
AB - This paper explores the problem of delay-independent and delay-dependent stability for a class of complex-valued neutral-type neural networks with time delays. Aiming at the neutral-type neural networks, an appropriate function is constructed to derive the existence of equilibrium point. On the basis of homeomorphism theory, Lyapunov functional method and linear matrix inequality techniques, several LMI-based sufficient conditions on the existence, uniqueness and global asymptotic stability of equilibrium point for complex-valued neutral-type neural networks are obtained. Finally, numerical examples are given to illustrate the feasibility and the effectiveness of the proposed theoretical results.
LA - eng
KW - complex-valued neutral-type neural networks; existence and uniqueness of equilibrium; global asymptotic stability; inequality techniques; Lyapunov functional
UR - http://eudml.org/doc/294754
ER -

References

top
  1. Boyd, S., Ghaoui, L. E., Feron, E., Balakrishnam, V., 10.1137/1.9781611970777, SIAM, Philadelphia, 1994. DOI10.1137/1.9781611970777
  2. Cao, J. D., Chen, G. R., Li, P., 10.1109/tsmcb.2007.914705, IEEE Trans. Syst. Man Cybern. B 38 (2008), 2, 488-498. DOI10.1109/tsmcb.2007.914705
  3. Chen, X. F., Song, Q. K., 10.1016/j.neucom.2013.04.040, Neurocomputing 121 (2013), 254-264. DOI10.1016/j.neucom.2013.04.040
  4. Ding, X. S., Cao, J. D., Alsaedi, A., Alsaadi, F. E., Hayat, T., 10.1016/j.neunet.2017.03.006, Neural Netw. 90 (2017), 42-55. DOI10.1016/j.neunet.2017.03.006
  5. Du, B., Liu, Y., Cao, J. D., 10.14736/kyb-2017-3-0513, Kybernetika 53 (2017), 3, 513-529. MR3684683DOI10.14736/kyb-2017-3-0513
  6. Fang, T., Sun, J. T., 10.1109/tnnls.2013.2294638, IEEE Trans. Neural Netw. Learn. Syst. 25 (2014), 9, 1709-1713. MR3453740DOI10.1109/tnnls.2013.2294638
  7. Fang, T., Sun, J. T., 10.1016/j.nahs.2014.04.004, Nonlinear Anal. Hybrid Syst. 14 (2014), 38-46. MR3228049DOI10.1016/j.nahs.2014.04.004
  8. Feng, J. E., Xu, S. Y., Zou, Y., 10.1016/j.neucom.2008.10.018, Neurocomputing 72 (2009), 10-12, 2576-2580. DOI10.1016/j.neucom.2008.10.018
  9. Forti, M., Tesi, A., 10.1109/81.641813, IEEE Trans. Circuits Syst. I 42 (1995), 354-366. MR1351871DOI10.1109/81.641813
  10. Gong, W. Q., Liang, J. L., Zhang, C. J., Cao, J. D., 10.1007/s11063-015-9475-9, Neural Process. Lett. 44 (2015), 539-554. DOI10.1007/s11063-015-9475-9
  11. Guo, R. N., Zhang, Z. Y., Liu, X. P., Lin, C., 10.1016/j.amc.2017.05.021, Appl. Math. Comput. 311 (2017), 100-117. MR3658062DOI10.1016/j.amc.2017.05.021
  12. Hirose, A., 10.1142/9789812791184, World Scientific, Singapore 2003. MR2061862DOI10.1142/9789812791184
  13. Hirose, A., 10.1007/978-3-642-13232-2_6, In: Artif. Intell. Soft. Comput. II, Vol. 6114 (L. Rutkowski et al., eds.), Springer, New York 2010, pp. 42-46. DOI10.1007/978-3-642-13232-2_6
  14. Hu, J., Wang, J., 10.1109/tnnls.2012.2195028, IEEE Trans. Neural Netw. Learn. Syst. 23 (2012), 6, 853-865. MR3453740DOI10.1109/tnnls.2012.2195028
  15. Liao, X. F., Liu, Y. L., Wang, H. W., Huang, T. W., 10.1016/j.neucom.2014.07.048, Neurocomputing 149 (2015), 868-883. MR3593044DOI10.1016/j.neucom.2014.07.048
  16. Liu, X. W., Chen, T. P., 10.1109/tnnls.2015.2415496, IEEE Trans. Neural Netw. Learn. Syst. 27 (2016), 3, 593-606. MR3465659DOI10.1109/tnnls.2015.2415496
  17. Pan, J., Liu, X. Z., Xie, W. C., 10.1016/j.neucom.2015.02.024, Neurocomputing 164 (2015), 293-299. DOI10.1016/j.neucom.2015.02.024
  18. Orman, Z., 10.1016/j.neucom.2012.05.016, Neurocomputing 97 (2012), 141-148. DOI10.1016/j.neucom.2012.05.016
  19. Patan, K., 10.1109/tnn.2007.891199, IEEE Trans. Neural Netw. 18 (2007), 3, 660-673. DOI10.1109/tnn.2007.891199
  20. Park, J. H., Kwon, O. M., 10.1016/j.amc.2008.11.017, Appl. Math. Comput. 208 (2009), 1, 69-75. MR2490770DOI10.1016/j.amc.2008.11.017
  21. Park, J. H., Kwon, O. M., Lee, S. M., 10.1016/j.amc.2007.05.047, Appl. Math. Comput. 196 (2008), 1, 236-244. MR2382607DOI10.1016/j.amc.2007.05.047
  22. Park, J. H., Park, C. H., Kwon, O. M., Lee, S. M., 10.1016/j.amc.2007.10.032, Appl. Math. Comput. 199 (2008), 2, 716-722. MR2420599DOI10.1016/j.amc.2007.10.032
  23. Shi, K. B., Zhong, S. M., Zhu, H., Liu, X. Z., Zeng, Y., 10.1016/j.neucom.2015.05.035, Neurocomputing 168 (2015), 896-907. MR3402310DOI10.1016/j.neucom.2015.05.035
  24. Shu, Y.J., Liu, X.G., Wang, F.X., Qiu, S.B., 10.1002/mma.4281, Math. Method. Appl. Sci. 40 (2017), 11, 4014-4027. MR3668827DOI10.1002/mma.4281
  25. Song, Q. K., 10.1016/j.neucom.2009.04.009, Neurocomputing 72 (2009), 3907-3914. DOI10.1016/j.neucom.2009.04.009
  26. Song, Q. K., Shu, H. Q., Zhao, Z. J., Liu, Y. R., Alsaadi, F. E., 10.1016/j.neucom.2017.03.015, Neurocomputing 244 (2017), 33-41. DOI10.1016/j.neucom.2017.03.015
  27. Song, Q. K., Yan, H., Zhao, Z. J., Liu, Y. R., 10.1016/j.neunet.2016.04.012, Neural Netw. 81 (2016), 1-10. DOI10.1016/j.neunet.2016.04.012
  28. Song, Q. K., Zhao, Z. J., Liu, Y. R., 10.1016/j.neucom.2015.02.015, Neurocomputing 159 (2015), 96-104. DOI10.1016/j.neucom.2015.02.015
  29. Subramanian, K., Muthukumar, P., 10.1007/s11571-017-9429-1, Cogn. Neurodynamics 11 (2017), 3, 293-306. DOI10.1007/s11571-017-9429-1
  30. Tan, M. C., 10.1007/s11063-010-9130-4, Neural Process. Lett. 31 (2010), 2, 147-157. DOI10.1007/s11063-010-9130-4
  31. Tan, M. C., 10.1007/s11063-015-9416-7, Neural Process. Lett. 43 (2016), 1, 255-268. DOI10.1007/s11063-015-9416-7
  32. Tan, Y. X., Jing, K., 10.1002/mma.3732, Math. Method. Appl. Sci. 39 (2016), 11, 2821-2839. MR3512733DOI10.1002/mma.3732
  33. Tan, M. C., Xu, D. S., 10.1016/j.neucom.2017.10.038, Neurocomputing 275 (2018), 2681-2701. DOI10.1016/j.neucom.2017.10.038
  34. Tan, M. C., Zhang, Y. N., 10.1016/j.nonrwa.2008.03.022, Nonlinear Anal. Real World Appl. 10 (2009), 2139-2145. MR2508424DOI10.1016/j.nonrwa.2008.03.022
  35. Tian, X. H., Xu, R., 10.1002/mma.3995, Math. Method. Appl. Sci. 40 (2017), 1, 293-305. MR3583055DOI10.1002/mma.3995
  36. Tu, Z. W., Cao, J. D., Alsaedi, A., Alsaadi, F. E., Hayat, T., 10.1002/cplx.21823, Complexity 21 (2016), S2, 438-450. MR3583097DOI10.1002/cplx.21823
  37. Wang, H. M., Duan, S. K., Huang, T. W., Wang, L. D., Li, C. D., 10.1109/TNNLS.2015.2513001, IEEE Trans. Neural Netw. Learn. Syst. 28 (2017), 3, 766-771. MR3730910DOI10.1109/TNNLS.2015.2513001
  38. Wang, Z. Y., Huang, L. H., 10.1016/j.neucom.2015.09.086, Neurocomputing 173 (2016), 2083-2089. DOI10.1016/j.neucom.2015.09.086
  39. Wang, P., Li, Y. K., Ye, Y., 10.1002/mma.3857, Math. Method. Appl. Sci. 39 (2016), 15, 4297-4310. MR3549393DOI10.1002/mma.3857
  40. Wang, L., Xie, Y., Wei, Z., Peng, J., 10.14736/kyb-2015-5-0800, Kybernetika 51 (2015), 5, 800-813. MR3445985DOI10.14736/kyb-2015-5-0800
  41. Xie, J., Kao, Y. G., Park, J.H., 10.1016/j.nahs.2017.10.002, Nonlinear Anal. Hybrid Syst. 27 (2018), 416-436. MR3729580DOI10.1016/j.nahs.2017.10.002
  42. Xu, C. J., Li, P. L., Pang, Y. C., 10.1002/mma.4132, Math. Method. Appl. Sci. 40 (2017), 6, 2177-2196. MR3624090DOI10.1002/mma.4132
  43. Xu, X. H., Zhang, J. Y., Shi, J. Z., 10.1016/j.neucom.2013.08.014, Neurocomputing 128 (2014), 483-490. DOI10.1016/j.neucom.2013.08.014
  44. Zhang, H. G., Gong, D. W., Wang, Z. S., 10.1007/s11063-011-9202-0, Neural Process. Lett. 35 (2012), 1, 29-45. DOI10.1007/s11063-011-9202-0
  45. Zhang, Z. Y., Liu, X. P., Chen, J., Guo, R. N., Zhou, S. W., 10.1016/j.neucom.2017.04.013, Neurocomputing 251 (2017), 81-89. DOI10.1016/j.neucom.2017.04.013
  46. Zhang, Z. Y., Lin, C., Chen, B., 10.1109/tnnls.2013.2288943, IEEE Trans. Neural Netw. Learn. Syst. 25 (2014), 9, 1704-1708. DOI10.1109/tnnls.2013.2288943
  47. Zhang, Z. Q., Yu, S. H., 10.1016/j.neucom.2015.07.051, Neurocomputing 171 (2016), 1158-1166. DOI10.1016/j.neucom.2015.07.051
  48. Zeng, X., Li, C. D., Huang, T. W., He, X., 10.1016/j.amc.2015.01.006, Appl. Math. Comput. 256 (2015), 75-82. MR3316049DOI10.1016/j.amc.2015.01.006
  49. Zheng, C. D., Shan, Q. H., Zhang, H. G., 10.1109/tnnls.2013.2244613, IEEE Trans. Neural Netw. Learn. Syst. 24 (2013), 800-811. DOI10.1109/tnnls.2013.2244613

NotesEmbed ?

top

You must be logged in to post comments.

To embed these notes on your page include the following JavaScript code on your page where you want the notes to appear.

Only the controls for the widget will be shown in your chosen language. Notes will be shown in their authored language.

Tells the widget how many notes to show per page. You can cycle through additional notes using the next and previous controls.

    
                

Note: Best practice suggests putting the JavaScript code just before the closing </body> tag.