Abstract
The issues of the stability and bifurcation for a delayed BAM network involving two neurons in the I-layer and arbitrary neurons in the J-layer are concerned in the present paper. By adopting the sum of the delays as the bifurcation parameter, we discuss the distribution of the roots of the characteristic equation for high-dimension system in terms of stability switches theory and further present some sufficient conditions for the occurrence of Hopf bifurcations. Analysis reveals that Hopf bifurcation will emerge after the given system loses its stability. Moreover, we derive explicit general formulae to determine the properties of bifurcation via the normal theory and the center manifold theorem. It is demonstrated that the sum of the delays can effectively affect the dynamics of the proposed system. Finally, an illustrative example is employed to verify the validity of the theoretical results obtained.
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Liang, X.B., Wang, J.: A recurrent neural network for nonlinear optimization with a continuously differentiable objective function and bound constraints. IEEE Trans. Neural Netw. 11(6), 1251–1262 (2000)
Arik, S.: Global asymptotic stability of a larger class of neural networks with constant time delay. Phys. Lett. A 311(6), 504–511 (2003)
Zheng, B.D., Zhang, Y.Z., Zhang, C.R.: Global existence of periodic solutions on a simplified BAM neural network model with delays. Chaos Solitons Fractals 37(5), 1397–1408 (2008)
Karimi, H.R., Gao, H.J.: New delay-dependent exponential synchronization for uncertain neural networks with mixed time delays. IEEE Trans. Syst. Man Cybern. B Cybern. 40(1), 173–185 (2010)
Li, S., Yangming, M.: Nonlinearly activated neural network for solving time-varying complex sylvester equation. IEEE Trans. Cybern. 44(8), 1397–1407 (2014)
Qi, J.T., Li, C.D., Huang, T.W.: Stability of delayed memristive neural networks with time-varying impulses. Cogn. Neurodyn. 8(5), 429–436 (2014)
Hopfield, J.J.: Neurons with graded response have collective computational properties like those of two-state neurons. In: Proceedings of the National Academy of Sciences of the United States of America. vol. 81(10), pp. 3088–3092 (1984)
Mizuki, O., Tsunehiro, I., Masato, K., Akira, T., Akira, N.: Influences of nonuniformity in metal concentration in gate dielectric silicate on CMIS inverters’ propagation delay time. Solid State Electron. 48(12), 347–359 (2004)
Chis, O., Neamtu, M., Opris, D.: Deterministic and stochastic model for the role of the immune response time delay in periodic therapy of the tumors. Curr. Comput. Aided Drug Des. 7(4), 338–350 (2011)
Chang, C.Y., Liao, K.Y., Hsu, S.C., Li, J.C., Rau, J.C.: Compact test pattern selection for small delay defect. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 32(6), 971–975 (2013)
Wang, Y., Cao, J.D., Sun, G.Q., Li, J.: Effect of time delay on pattern dynamics in a spatial epidemic model. Phys. A 412, 137–148 (2014)
Kosko, B.: Adaptive bidirectional associative memories. Appl. Opt. 26(23), 4947–4960 (1987)
Kosko, B.: Bidirectional associative memories. IEEE Trans. Syst. Man Cybern. 18(1), 49–60 (1988)
Gopalsamy, K., He, X.Z.: Delay-independent stability in bidirectional associative memory networks. IEEE Trans. Neural Netw. 5(6), 998–1002 (1994)
Wei, J.J., Ruan, S.G.: Stability and bifurcation in a neural network model with two delays. Phys. D Nonlinear Phenom. 130(3), 255–272 (1999)
Wang, H., Liao, X.F., Li, C.D.: Existence and exponential stability of periodic solution of BAM neural networks with impulse and time-varying delay. Chaos Solitons Fractals 33(3), 1028–1039 (2007)
Balasubramaniam, P., Vembarasan, V.: Robust stability of uncertain fuzzy BAM neural networks of neutral-type with Markovian jumping parameters. Comput. Math. Appl. 62(4), 1838–1861 (2011)
Cao, J.D., Wan, Y.: Matrix measure strategies for stability and synchronization of inertial BAM neural network with time delays. Neural Netw. 53, 165–172 (2014)
Sathy, R., Balasubramaniam, P.: Stability analysis of fuzzy Markovian jumping Cohen–Grossberg BAM neural networks with mixed time-varying delays. Commun. Nonlinear Sci. Numer. Simul. 16(4), 2054–2064 (2011)
Li, C.D., Hu, W.F., Wu, S.C.: Stochastic stability of impulsive BAM neural networks with time delays. Comput. Math. Appl. 61(8), 2313–2316 (2011)
Zhu, Q.X., Cao, J.D.: Stability analysis of Markovian jump stochastic BAM neural networks with impulse control and mixed time delays. IEEE Trans. Neural Netw. Learn. Syst. 23(3), 467–479 (2012)
Raja, R., Karthik Raja, U., Samidurai, R., Leelamani, A.: Passivity analysis for uncertain discrete-time stochastic BAM neural networks with time-varying delays. Neural Comput. Appl. 25(3–4), 751–766 (2014)
Raja, R., Karthik Raja, U., Samidurai, R., Leelamani, A.: Dynamic analysis of discrete-time BAM neural networks with stochastic perturbations and impulses. Int. J. Mach. Learn. Cybern. 5(1), 39–50 (2014)
Shayer, L.P., Campbell, S.A.: Stability, bifurcation, and multistability in a system of two couple neurons with multiple time delays. SIAM J. Appl. Math. 61(2), 673–700 (2000)
Huang, C.D., Cao, J.D.: Hopf bifurcation in an \(n\)-dimensional Goodwin model via multiple delays feedback. Nonlinear Dyn. 79(4), 2541–2552 (2015)
Gupta, P.D., Majee, N.C., Roy, A.B.: Stability and Hopf-bifurcation analysis of delayed BAM neural network under dynamic thresholds. Nonlinear Anal. Model. Control 14(4), 435–461 (2009)
Xiao, M., Jiang, G., Zhao, L.D.: State feedback control at Hopf bifurcation in an exponential RED algorithm model. Nonlinear Dyn. 76(2), 1469–1484 (2014)
Xu, W.Y., Cao, J.D., Xiao, M.: Bifurcation analysis and control in exponential RED algorithm. Neurocomputing 129, 232–245 (2014)
Liu, M., Xu, X.F., Zhang, C.R.: Stability and global Hopf bifurcation for neutral BAM neural network. Neurocomputing 145, 122–130 (2014)
Song, Y.L., Han, M.A., Wei, J.J.: Stability and Hopf bifurcation on a simplified BAM network model with delays. Phys. D Nonlinear Phenom. 200(3–4), 185–204 (2005)
Cao, J.D., Xiao, M.: Stability and Hopf bifurcation in a simplified BAM nural network with two time delays. IEEE Trans. Neural Netw. 18(2), 416–430 (2007)
Yang, Y., Ye, J.: Stability and bifurcation in a simplified five-neuron BAM neural network with delays. Chaos Solitons Fractals 42(4), 2357–2363 (2009)
Xu, C.J., Li, P.L.: Bifurcation analysis in a simplified six-neuron BAM neural network with two delays. J. Inf. Comput. Sci. 49(13), 3849–3858 (2012)
Xiao, M., Zheng, W.X., Cao, J.D.: Hopf bifurcation of an (\(n + 1\))-neuron bidirectional associative memory neural network model with delays. IEEE Trans. Neural Netw. Learn. Syst. 24(1), 118–132 (2013)
Yu, W.W., Cao, J.D.: Stability and bifurcation on a four-neuron BAM neural network with delays. Phys. Lett. A 351(1), 64–78 (2006)
Javidmanesh, E., Afsharnezhad, Z.: Hopf bifurcation analysis of a delayed five-neuron BAM neural network with two neurons in the \(X\)-layer. Iran. J. Numer. Anal. Optim. 3(2), 1–12 (2013)
Javidmanesh, E., Afsharnezhad, Z.: Existence and stability analysis of bifurcating periodic solutions in a delayed five-neuron BAM neural network model. Nonlinear Dyn. 72(1), 149–164 (2013)
Wang, Z.H., Hu, H.Y.: Stability switches of time-delayed dynamic systems with unknown parameters. J. Sound Vib. 233(2), 215–233 (2000)
Li, J.Q., Ma, Z.E.: Stability switches in a class of characteristic equations with delay-dependent parameters. Nonlinear Anal. Real World Appl. 5(3), 389–408 (2004)
Ruan, S.G., Wei, J.J.: On the zeros of transcendental functions with applications to stability of delay differential equations with two delays. Dyn. Contin. Discrete Impuls. Syst. Ser. A Math. Anal. 10(6), 863–874 (2003)
Hale, J.: Theory of Functional Differential Equations. Springer, New York (1977)
Hassard, B.D., Kazarinoff, N.D., Wan, Y.H.: Theory and Applications of Hopf Bifurcation. Cambridge University Press, Cambridge (1981)
Acknowledgments
The authors would like to thank the anonymous reviewers and the handling editor for their helpful comments and constructive suggestions, which have been very useful in improving the quality of the manuscript. This project was funded by the Deanship of Scientific Research (DSR), King Abdulaziz University, under Grant No. (17-130-36-HiCi). The authors, therefore, acknowledge with thanks DSR technical and financial support.
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Appendices
Appendix 1
Derivation of the expressions of \(\varTheta _{ij}\), \(\varPsi _{ij}\) \((i=1,2;j=1,2,3)\) in Eqs. (10) and (11)
Appendix 2
Derivation of the expressions of \(M_{ij}(i=1,2;j=1,2)\) in Eq. (17)
Appendix 3
Derivation of the expressions of \(N_{ij}(i=1,2;j=1,2)\) in Eq. (17)
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Huang, C., Cao, J., Alofi, A. et al. Dynamics and control in an \(({\varvec{n}}+{\varvec{2}})\)-neuron BAM network with multiple delays. Nonlinear Dyn 87, 313–336 (2017). https://doi.org/10.1007/s11071-016-3045-1
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DOI: https://doi.org/10.1007/s11071-016-3045-1