New results on stability of periodic solution for CNNs with proportional delays and D operator

Bo Du

Kybernetika (2019)

  • Volume: 55, Issue: 5, page 852-869
  • ISSN: 0023-5954

Abstract

top
The problems related to periodic solutions of cellular neural networks (CNNs) involving D operator and proportional delays are considered. We shall present Topology degree theory and differential inequality technique for obtaining the existence of periodic solution to the considered neural networks. Furthermore, Laypunov functional method is used for studying global asymptotic stability of periodic solutions to the above system.

How to cite

top

Du, Bo. "New results on stability of periodic solution for CNNs with proportional delays and $D$ operator." Kybernetika 55.5 (2019): 852-869. <http://eudml.org/doc/295065>.

@article{Du2019,
abstract = {The problems related to periodic solutions of cellular neural networks (CNNs) involving $D$ operator and proportional delays are considered. We shall present Topology degree theory and differential inequality technique for obtaining the existence of periodic solution to the considered neural networks. Furthermore, Laypunov functional method is used for studying global asymptotic stability of periodic solutions to the above system.},
author = {Du, Bo},
journal = {Kybernetika},
keywords = {periodic solution; $D$ operator; existence; stability},
language = {eng},
number = {5},
pages = {852-869},
publisher = {Institute of Information Theory and Automation AS CR},
title = {New results on stability of periodic solution for CNNs with proportional delays and $D$ operator},
url = {http://eudml.org/doc/295065},
volume = {55},
year = {2019},
}

TY - JOUR
AU - Du, Bo
TI - New results on stability of periodic solution for CNNs with proportional delays and $D$ operator
JO - Kybernetika
PY - 2019
PB - Institute of Information Theory and Automation AS CR
VL - 55
IS - 5
SP - 852
EP - 869
AB - The problems related to periodic solutions of cellular neural networks (CNNs) involving $D$ operator and proportional delays are considered. We shall present Topology degree theory and differential inequality technique for obtaining the existence of periodic solution to the considered neural networks. Furthermore, Laypunov functional method is used for studying global asymptotic stability of periodic solutions to the above system.
LA - eng
KW - periodic solution; $D$ operator; existence; stability
UR - http://eudml.org/doc/295065
ER -

References

top
  1. Aouiti, C., all., I. B. Gharbia at, 10.1016/j.jfranklin.2019.01.028, J. Franklin Inst. 356 (2019), 2294-2324. MR3925987DOI10.1016/j.jfranklin.2019.01.028
  2. Arik, S., 10.1016/j.jfranklin.2018.11.002, J. Franklin Inst. 356 (2019), 276-291. MR3906098DOI10.1016/j.jfranklin.2018.11.002
  3. Askari, E., Setarehdan, S., Mohammadi, A. Sheikhani A. M., Teshnehlab, H., 10.3233/jin-180075, J. Integr. Neurosci. 17 (2018), 391-411. DOI10.3233/jin-180075
  4. Barbalat, I., Systems d'equations differential d'oscillationsn onlinearities., Rev. Rounmaine Math. Pure Appl. 4 (1959), 267-270. MR0111896
  5. Cheng, Z., Li, F., 10.1007/s00009-018-1184-y, Mediterr. J. Math. 15 (2018), 134-153. MR3808561DOI10.1007/s00009-018-1184-y
  6. Chua, L., Yang, L., 10.1109/31.7601, IEEE. Trans. Circuits Syst. 35 (1988), 1273-1290. MR0960778DOI10.1109/31.7601
  7. Dharani, S., Rakkiyappan, R., Cao, J., 10.1016/j.neucom.2014.10.014, Neurocomputing 151 (2015), 827-834. DOI10.1016/j.neucom.2014.10.014
  8. Ding, H., Liang, J., Xiao, T., 10.1016/j.physleta.2008.06.042, Physics Lett. A 372 (2008), 5411-5416. MR2438234DOI10.1016/j.physleta.2008.06.042
  9. Gaines, R., Mawhin, J., 10.1007/bfb0089538, Springer, Berlin 1977. MR0637067DOI10.1007/bfb0089538
  10. Gang, Y., 10.1007/s00521-014-1662-5, Neural Computing Appl. 25 (2014), 1709-1715. MR2907168DOI10.1007/s00521-014-1662-5
  11. Guan, K., 10.1016/j.neucom.2018.01.027, Neurocomputing 283 (2018), 256-265. DOI10.1016/j.neucom.2018.01.027
  12. Guo, R., Ge, W., all., Z. Zhang at, 10.1007/s12555-018-0542-7, Int. J. Control, Automat. Systems 17 (2019), 3, 801-809. DOI10.1007/s12555-018-0542-7
  13. Huang, Z., 10.1007/s13042-016-0507-1, Int. J. Machine Learning Cybernet. 8 (2017), 1323-1331. DOI10.1007/s13042-016-0507-1
  14. Li, Y., Li, B., Yao, S., Xiong, L., 10.1016/j.neucom.2018.04.044, Neurocomputing 303 (2018), 75-87. DOI10.1016/j.neucom.2018.04.044
  15. Li, X., Huang, L., Zhou, H., 10.1016/s0362-546x(02)00176-1, Nonlinear Anal. TMA 53 (2003), 319-333. MR1964329DOI10.1016/s0362-546x(02)00176-1
  16. Liu, B., 10.1002/mma.3976, Math. Methods App. Sci. 40 (2017), 167-174. MR3583044DOI10.1002/mma.3976
  17. Manivannan, R., Samidurai, R., Cao, J., Alsaedi, A., 10.1007/s11571-016-9396-y, Cognit. Neurodyn. 10 (2016), 6, 543-562. DOI10.1007/s11571-016-9396-y
  18. Ozcan, N., 10.1016/j.neunet.2019.01.017, Neural Networks 113 (2019), 20-27. DOI10.1016/j.neunet.2019.01.017
  19. Rakkiyappan, R., Balasubramaniam, P., 10.1016/j.neucom.2007.11.002, Neurocomputing 71 (2008), 1039-1045. MR2458370DOI10.1016/j.neucom.2007.11.002
  20. Samidurai, R., Rajavel, S., Sriraman, R., Cao, J., Alsaedi, A., Alsaadi, F. E., 10.1007/s12555-016-9483-1, Int. J. Control Automat. Syst. 15 (2017), 4, 1888-1900. DOI10.1007/s12555-016-9483-1
  21. all., R. Saml et, 10.1016/j.neunet.2019.04.023, Neural Networks 116 (2019), 198-207. DOI10.1016/j.neunet.2019.04.023
  22. Shi, K., Zhu, H., Zhong, S., Zeng, Y., Zhang, Y., 10.1016/j.jfranklin.2014.10.005, J. Frankl. Inst. 352 (2015), 1, 155-176. MR3292322DOI10.1016/j.jfranklin.2014.10.005
  23. Singh, V., 10.1016/j.chaos.2005.09.050, Chao. Solit. Fract. 31 (2007), 224-229. MR2263282DOI10.1016/j.chaos.2005.09.050
  24. Singh, V., 10.1016/j.chaos.2006.01.121, Chao. Solit. Fract. 33 (2007), 1183-1188. MR2318906DOI10.1016/j.chaos.2006.01.121
  25. Xiao, S., 10.1007/s11063-018-9817-5, Neural Process. Lett. 49 (2019), 347-356. DOI10.1007/s11063-018-9817-5
  26. Xin, Y., Cheng, Z. B., 10.1186/1687-1847-2014-273, Adv. Diff. Equ. 273 (2014), 1-18. MR3359150DOI10.1186/1687-1847-2014-273
  27. Yao, L., 10.1007/s00521-016-2403-8, Neural Comput. Appl. 29 (2018), 105-109. DOI10.1007/s00521-016-2403-8
  28. Yu, Y., 10.1002/mma.3880, Math. Methods Appl. Sci. 39 (2016), 4520-4525. MR3549409DOI10.1002/mma.3880
  29. Yu, Y., 10.1016/j.amc.2016.03.018, Appl. Math. Comput. 285 (2016), 1-7. MR3494408DOI10.1016/j.amc.2016.03.018
  30. Zhang, X., Han, Q., 10.1016/j.neunet.2014.02.012, Neural Networks 54 (2014), 57-69. DOI10.1016/j.neunet.2014.02.012
  31. Zhang, X., Han, Q., 10.1109/tcyb.2017.2690676, IEEE Trans. Cybernetics 47 (2017), 3184-3194. DOI10.1109/tcyb.2017.2690676
  32. Zhang, X., Han, Q., Wang, L., 10.1109/tnnls.2018.2797279, IEEE Trans. Neural Networks Learning Systems 29 (2018), 5319-5329. MR3867847DOI10.1109/tnnls.2018.2797279
  33. Zhang, X., Han, Q., Zeng, Z., 10.1109/tcyb.2017.2776283, IEEE Trans. Cybernetics 48 (2018), 1660-1671. DOI10.1109/tcyb.2017.2776283
  34. Zhang, H., all., T. Ma et, 10.1109/tsmcb.2009.2030506, IEEE Trans. Systems Man Cybernet. 40 (2010), 831-844. MR2904149DOI10.1109/tsmcb.2009.2030506
  35. Zhang, H., Qiu, Z., Xiong, L., 10.1016/j.neucom.2018.12.028, Neurocomputing 333 (2019), 395-406. DOI10.1016/j.neucom.2018.12.028
  36. Zheng, M., all., L. Li et, 10.1016/j.cnsns.2017.11.025, Comm. Nonlinear Sci. Numer. Simul. 59 (2018), 272-291. MR3758388DOI10.1016/j.cnsns.2017.11.025
  37. Zhou, Q., 10.1002/asjc.1468, Asian J. Control 19 (2017), 1557-1563. MR3685941DOI10.1002/asjc.1468
  38. Zhou, Q., Shao, J., 10.1007/s00521-016-2582-3, Neural Computing Appl. 29 (2018), 272-291. DOI10.1007/s00521-016-2582-3

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.