Skip to main content
Top
Published in:

05-06-2024

Effect of Leakage Delays on Bifurcation in Fractional-Order Bidirectional Associative Memory Neural Networks with Five Neurons and Discrete Delays

Authors: Yangling Wang, Jinde Cao, Chengdai Huang

Published in: Cognitive Computation | Issue 5/2024

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

As is well known that time delays are inevitable in practice due to the finite switching speed of amplifiers and information transmission between neurons. So the study on the Hopf bifurcation of delayed neural networks has aroused extensive attention in recent years. However, it’s worth mentioning that only the communication delays between neurons were generally considered in most existing relevant literatures. Actually, it has been proven that a kind of so-called leakage delays cannot be ignored because the self-decay process of a neuron’s action potential is not instantaneous in hardware implementation of neural networks. Though leakage delays have been taken into account in a few more recent works concerning the Hopf bifurcation of fractional-order bidirectional associative memory neural networks, the addressed neural networks were low-dimension or the involved time delays were single. In this paper, we propose a five-neuron fractional-order bidirectional associative memory neural network model, which includes leakage delays and discrete communication delays to meet the characteristics of real neural networks better. Then we use the stability theory of fractional differential equations and Hopf bifurcation theory to investigate its dynamic behavior of Hopf bifurcation. The Hopf bifurcation of the proposed model are studied by taking the involved two different leakage delays as the bifurcation parameter respectively, and two kinds of sufficient conditions for Hopf bifurcation are obtained. A numerical example as well as its simulation plots and phase portraits are given at last. Our results indicate that a Hopf bifurcation rises near the zero equilibrium point when the leakage delay reaches its critical value which is given by an explicit formula. Particularly, the results of numerical simulations show that the leakage delay would narrow the stability region of the proposed system and make the Hopf bifurcation occur earlier.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Literature
1.
go back to reference Kosko B. Adaptive bi-directional associative memories. Appl Opt. 1987;26:4947–60.CrossRef Kosko B. Adaptive bi-directional associative memories. Appl Opt. 1987;26:4947–60.CrossRef
2.
go back to reference Cao JD, Zhou DM. Stability analysis of delayed cellular neural networks. Neural Netw. 1998;11:1601–5.CrossRef Cao JD, Zhou DM. Stability analysis of delayed cellular neural networks. Neural Netw. 1998;11:1601–5.CrossRef
3.
go back to reference Cao JD, Wang L. Exponential stability and periodic oscillatory solution in BAM networks with delays. IEEE Trans Neural Netw. 2002;13(2):457–63.CrossRef Cao JD, Wang L. Exponential stability and periodic oscillatory solution in BAM networks with delays. IEEE Trans Neural Netw. 2002;13(2):457–63.CrossRef
4.
go back to reference Cao JD, Liang JL, James J. Exponential stability of high-order bidirectional associative memory neural networks with time delays. Physica D. 2004;199(3–4):425–36.MathSciNetCrossRef Cao JD, Liang JL, James J. Exponential stability of high-order bidirectional associative memory neural networks with time delays. Physica D. 2004;199(3–4):425–36.MathSciNetCrossRef
5.
go back to reference Cao JD, Song QK. Stability in Cohen-Grossberg-type bidirectional associative memory neural networks with time-varying delays. Nonlinearity. 2006;19:1601–17.MathSciNetCrossRef Cao JD, Song QK. Stability in Cohen-Grossberg-type bidirectional associative memory neural networks with time-varying delays. Nonlinearity. 2006;19:1601–17.MathSciNetCrossRef
6.
go back to reference Cao JD, Xiao M. Stability and Hopf bifurcation in a simplified BAM neural network with two time delays. IEEE Trans Neural Netw. 2007;18(2):416–30.CrossRef Cao JD, Xiao M. Stability and Hopf bifurcation in a simplified BAM neural network with two time delays. IEEE Trans Neural Netw. 2007;18(2):416–30.CrossRef
7.
go back to reference Zhou FY, Ma CR. Global exponential stability of high-order BAM neural networks with reaction-diffusion terms. Int J Bifurcat Chaos. 2010;20(10):3209–23.MathSciNetCrossRef Zhou FY, Ma CR. Global exponential stability of high-order BAM neural networks with reaction-diffusion terms. Int J Bifurcat Chaos. 2010;20(10):3209–23.MathSciNetCrossRef
8.
go back to reference Wang BX, Jian JG. Stability and Hopf bifurcation analysis on a four-neuron BAM neural network with distributed delays. Commun Nonlinear Sci Numer Simulat. 2010;15:189–204.MathSciNetCrossRef Wang BX, Jian JG. Stability and Hopf bifurcation analysis on a four-neuron BAM neural network with distributed delays. Commun Nonlinear Sci Numer Simulat. 2010;15:189–204.MathSciNetCrossRef
9.
go back to reference Wu HX, Liao XF, Feng W, Guo ST. Mean square stability of uncertain stochastic BAM neural networks with interval time-varying delays. Cogn Neurodyn. 2021;6:443–58.CrossRef Wu HX, Liao XF, Feng W, Guo ST. Mean square stability of uncertain stochastic BAM neural networks with interval time-varying delays. Cogn Neurodyn. 2021;6:443–58.CrossRef
10.
go back to reference Wang TY, Zhu QX. Stability analysis of stochastic BAM neural networks with reaction-diffusion, multi-proportional and distributed delays. Physica A. 2019;533:121935.MathSciNetCrossRef Wang TY, Zhu QX. Stability analysis of stochastic BAM neural networks with reaction-diffusion, multi-proportional and distributed delays. Physica A. 2019;533:121935.MathSciNetCrossRef
11.
go back to reference Tao BB, Xiao M, Zheng WX, Cao JD. Dynamics analysis and design for a bidirectional super-ring-shaped neural network with \(n\) neurons and multiple delays. IEEE Trans Neural Netw Learn Syst. 2021;32:2978–92.MathSciNetCrossRef Tao BB, Xiao M, Zheng WX, Cao JD. Dynamics analysis and design for a bidirectional super-ring-shaped neural network with \(n\) neurons and multiple delays. IEEE Trans Neural Netw Learn Syst. 2021;32:2978–92.MathSciNetCrossRef
12.
go back to reference Nicolis JS. Chaos and Information Processing. Singapore: World Scientific; 1991.CrossRef Nicolis JS. Chaos and Information Processing. Singapore: World Scientific; 1991.CrossRef
13.
go back to reference Shilnikov AL, Cymbalyuk GS. Transition between Tonio-spiking and bursting in a neuron model via the blue-sky catastrophe. Phys Rev Lett. 2005;94(4):048101. Shilnikov AL, Cymbalyuk GS. Transition between Tonio-spiking and bursting in a neuron model via the blue-sky catastrophe. Phys Rev Lett. 2005;94(4):048101.
14.
go back to reference Yu WW, Cao JD. Stability and Hopf bifurcation analysis on a four-neuron BAM neural network with time delays. Phys Lett A. 2006;351:64–78.CrossRef Yu WW, Cao JD. Stability and Hopf bifurcation analysis on a four-neuron BAM neural network with time delays. Phys Lett A. 2006;351:64–78.CrossRef
15.
go back to reference Xu CJ, Zhang QM, Wu YS. Bifurcation analysis for two-neuron networks with discrete and distributed delays. Cogn Comput. 2016;8(6):1103–18.CrossRef Xu CJ, Zhang QM, Wu YS. Bifurcation analysis for two-neuron networks with discrete and distributed delays. Cogn Comput. 2016;8(6):1103–18.CrossRef
16.
go back to reference Javidmanesh E, Dadi Z, Afsharnezhad Z. Existence and Stability Analysis of Bifurcating Periodic Solutions in a Delayed Five-Neuron BAM Neural Network Model. Nonlinear Dyn. 2013;72(1):149–64.MathSciNetCrossRef Javidmanesh E, Dadi Z, Afsharnezhad Z. Existence and Stability Analysis of Bifurcating Periodic Solutions in a Delayed Five-Neuron BAM Neural Network Model. Nonlinear Dyn. 2013;72(1):149–64.MathSciNetCrossRef
17.
go back to reference Javidmanesh E, Dadi Z, Afsharnezhad Z, Effati S. Global stability analysis and existence of periodic solutions in an eight-neuron BAM neural network model with delays. J Intell Fuzzy Syst. 2014;27:391–406.MathSciNetCrossRef Javidmanesh E, Dadi Z, Afsharnezhad Z, Effati S. Global stability analysis and existence of periodic solutions in an eight-neuron BAM neural network model with delays. J Intell Fuzzy Syst. 2014;27:391–406.MathSciNetCrossRef
18.
go back to reference Liu YW, Li SS, Liu ZR, Wang RQ. High codimensional bifurcation analysis to a six-neuron BAM neural network. Cogn Neurodyn. 2016;10:149–64.CrossRef Liu YW, Li SS, Liu ZR, Wang RQ. High codimensional bifurcation analysis to a six-neuron BAM neural network. Cogn Neurodyn. 2016;10:149–64.CrossRef
19.
go back to reference Hilfer R. Applications of fractional calculus in physics. Singapore: World Scientific York; 2000. Hilfer R. Applications of fractional calculus in physics. Singapore: World Scientific York; 2000.
20.
go back to reference Magin R. Fractional calculus in bioengineering. Crit Rev Biomed Eng. 2004;32(1):1–104.CrossRef Magin R. Fractional calculus in bioengineering. Crit Rev Biomed Eng. 2004;32(1):1–104.CrossRef
21.
go back to reference Kibas AA, Srivastava HM, Trujillo JJ. Theory and application of fractional differential equations. New York: Elsevier; 2006. Kibas AA, Srivastava HM, Trujillo JJ. Theory and application of fractional differential equations. New York: Elsevier; 2006.
22.
go back to reference Zhang JM, Wu JW, Bao HB, Cao JD. Synchronization analysis of fractional-order three-neuron BAM neural networks with multiple time delays. Appl Math Comput. 2018;339:441–50.MathSciNet Zhang JM, Wu JW, Bao HB, Cao JD. Synchronization analysis of fractional-order three-neuron BAM neural networks with multiple time delays. Appl Math Comput. 2018;339:441–50.MathSciNet
23.
go back to reference Shafiya M, Nagamani G, Dafik D. Global synchronization of uncertain fractional-order BAM neural networks with time delay via improved fractional-order integral inequality. Math Comput Simulat. 2022;191:168–86.MathSciNetCrossRef Shafiya M, Nagamani G, Dafik D. Global synchronization of uncertain fractional-order BAM neural networks with time delay via improved fractional-order integral inequality. Math Comput Simulat. 2022;191:168–86.MathSciNetCrossRef
24.
go back to reference Bao HB, Ju HP, Cao JD. Non-fragile state estimation for fractional-order delayed memristive BAM neural networks. Neural Netw. 2019;119:190–9.CrossRef Bao HB, Ju HP, Cao JD. Non-fragile state estimation for fractional-order delayed memristive BAM neural networks. Neural Netw. 2019;119:190–9.CrossRef
25.
go back to reference Nagamani G, Shafiya M, Soundararajan G, Prakash M. Robust state estimation for fractional-order delayed BAM neural networks via LMI approach. J Franklin Inst. 2020;357:4964–82.MathSciNetCrossRef Nagamani G, Shafiya M, Soundararajan G, Prakash M. Robust state estimation for fractional-order delayed BAM neural networks via LMI approach. J Franklin Inst. 2020;357:4964–82.MathSciNetCrossRef
26.
go back to reference Wu AL, Zeng ZG, Song XG. Global Mittag-Leffler stabilization of fractional-order bidirectional associative memory neural networks. Neurocomputing. 2016;177:489–96. Wu AL, Zeng ZG, Song XG. Global Mittag-Leffler stabilization of fractional-order bidirectional associative memory neural networks. Neurocomputing. 2016;177:489–96.
27.
go back to reference Xu CJ, Aouiti C, Liu ZX. A further study on bifurcation for fractional order BAM neural networks with multiple delays. Neurocomputing. 2020;417:501–15.CrossRef Xu CJ, Aouiti C, Liu ZX. A further study on bifurcation for fractional order BAM neural networks with multiple delays. Neurocomputing. 2020;417:501–15.CrossRef
28.
go back to reference Xu CJ, Liao MX, Li PL, Guo Y, Liu ZX. Bifurcation Properties for fractional order delayed BAM neural networks. Cogn Comput. 2021;13:322–56.CrossRef Xu CJ, Liao MX, Li PL, Guo Y, Liu ZX. Bifurcation Properties for fractional order delayed BAM neural networks. Cogn Comput. 2021;13:322–56.CrossRef
29.
go back to reference Huang CD, Meng YJ, Cao JD, Alsaedi A, Alsaadi FE. New bifurcation results for fractional BAM neural network with leakage delay. Chaos, Solitons Fractals. 2017;100:31–44.MathSciNetCrossRef Huang CD, Meng YJ, Cao JD, Alsaedi A, Alsaadi FE. New bifurcation results for fractional BAM neural network with leakage delay. Chaos, Solitons Fractals. 2017;100:31–44.MathSciNetCrossRef
30.
go back to reference Huang CD, Liu H, Chen YF, Chen XP, Song F. Dynamics of a fractional-order BAM neural network with leakage delay and communication delay. Fractals. 2021;29(3):2150073.CrossRef Huang CD, Liu H, Chen YF, Chen XP, Song F. Dynamics of a fractional-order BAM neural network with leakage delay and communication delay. Fractals. 2021;29(3):2150073.CrossRef
32.
33.
go back to reference Podlubny I. Fractional differential equations. New York: Academic Press; 1999. Podlubny I. Fractional differential equations. New York: Academic Press; 1999.
34.
go back to reference Bandyopadhyay B, Kamal S. Stabilization and control of fractional order systems: a sliding mode approach. Lecture Notes Electr Eng. 2015;317:115–27.MathSciNetCrossRef Bandyopadhyay B, Kamal S. Stabilization and control of fractional order systems: a sliding mode approach. Lecture Notes Electr Eng. 2015;317:115–27.MathSciNetCrossRef
35.
go back to reference Matignon D. Stability results for fractional differential equations with applications to control processing. Computat Eng Syst Appl. 1996;2:963–8. Matignon D. Stability results for fractional differential equations with applications to control processing. Computat Eng Syst Appl. 1996;2:963–8.
36.
go back to reference Fan DJ, Wei JJ. Hopf bifurcation analysis in a tri-neuron network with time delay. Nonlinear Anal RWA. 2008;9:9–25.MathSciNetCrossRef Fan DJ, Wei JJ. Hopf bifurcation analysis in a tri-neuron network with time delay. Nonlinear Anal RWA. 2008;9:9–25.MathSciNetCrossRef
Metadata
Title
Effect of Leakage Delays on Bifurcation in Fractional-Order Bidirectional Associative Memory Neural Networks with Five Neurons and Discrete Delays
Authors
Yangling Wang
Jinde Cao
Chengdai Huang
Publication date
05-06-2024
Publisher
Springer US
Published in
Cognitive Computation / Issue 5/2024
Print ISSN: 1866-9956
Electronic ISSN: 1866-9964
DOI
https://doi.org/10.1007/s12559-024-10305-0

Premium Partner