Abstract
In this paper, we study Hopf-zero bifurcation in a generalized Gopalsamy neural network model. By using multiple time scales and center manifold reduction methods, we obtain the normal forms near a Hopf-zero critical point. A comparison between these two methods shows that the two normal forms are equivalent. Moreover, bifurcations are classified in two-dimensional parameter space near the critical point, and numerical simulations are presented to demonstrate the applicability of the theoretical results.
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Cochoki, A., Unbehauen, R.: Neural Networks for Optimization and Signal Processing. Wiley, New York (1993)
Ripley, B.D.: Pattern Recognition and Neural Networks. Cambridge University Press, New York (1996)
Driessche, P., Zou, X.: Global attractivity in delayed Hopfield neural network models. SIAM J. Appl. Math. 58, 1878–1890 (1998)
Hopfield, J.J.: Neurons with graded response have collective computational properties like those of two-stage neurons. Proc. Natl. Acad. Sci. USA 81, 3088–3092 (1984)
Chua, L.O., Yang, L.: Cellular neural networks: theory. IEEE Trans. Circuits Syst. I, Fundam. Theory Appl. 35, 1257–1272 (1988)
Chua, L.O., Yang, L.: Cellular neural networks: applications. IEEE Trans. Circuits Syst. I, Fundam. Theory Appl. 35, 1273–1290 (1988)
Cao, J.: Global asymptotic stability of delayed bi-directional associative memory neural networks. Appl. Math. Comput. 142, 333–339 (2003)
Kosko, B.: Bi-directional associative memories. IEEE Trans. Syst. Man Cybern. 18, 49–60 (1988)
Xu, C., Tang, X., Liao, M.: Frequency domain analysis for bifurcation in a simplified tri-neuron BAM network model with two delays. Neural Netw. 23, 872–880 (2010)
Baldi, P., Atiya, A.F.: How delays affect neural dynamics and learning. IEEE Trans. Neural Netw. 5, 612–621 (1994)
Campbell, S.A.: Stability and bifurcation of a simple neural network with multiple time delays. Fields Inst. Commun. 21, 65–79 (1999)
Liao, X., Li, C., Wong, K.: Criteria for exponential stability of Cohen–Grossberg neural networks. Neural Netw. 17, 1401–1414 (2004)
Wu, W., Cui, B., Huang, M.: Global asymptotic stability of Cohen–Grossberg neural networks with constant and variable delays. Chaos Solitons Fractals 33, 1355–1361 (2007)
Gopalsamy, K., Leung, I.K.C.: Convergence under dynamical thresholds with delays. IEEE Trans. Neural Netw. 8, 341–348 (1997)
Pakdaman, K., Malta, C.P.: A note on convergence under dynamical thresholds with delays. IEEE Trans. Neural Netw. 9, 231–233 (1998)
Guo, S., Huang, L.: Hopf bifurcating periodic orbits in a ring of neurons with delays. Physica D 183, 19–44 (2003)
Guo, S., Yuan, Y.: Delay-induced primary rhythmic behavior in a two-layer neural network. Neural Netw. 24, 65–74 (2011)
Huang, C., He, Y., Huang, L., Yuan, Z.: Hopf bifurcation analysis of two neurons with three delays. Nonlinear Anal., Real World Appl. 8, 903–921 (2007)
Song, Y., Han, M., Wei, J.: Stability and Hopf bifurcation on a simplified BAM neural network with delays. Physica D 200, 185–204 (2005)
Wang, L., Zou, X.: Hopf bifurcation in bidirectional associative memory neural networks with delays: analysis and computation. J. Comput. Appl. Math. 167, 73–90 (2004)
Cao, J., Liang, J.: Boundedness and stability for Cohen–Grossberg neural network with time-varying delays. J. Math. Anal. Appl. 296, 665–685 (2004)
Chen, T., Rong, L.: Delay-independent stability analysis of Cohen–Grossberg neural networks. Phys. Lett. A 317, 436–449 (2003)
Mohamad, S., Gopalsamy, K.: Exponential stability of continuous-time and discrete-time cellular neural networks with delays. Appl. Math. Comput. 135, 17–38 (2003)
Wang, L., Zou, X.: Exponential stability of Cohen–Grossberg neural networks. Neural Netw. 15, 415–422 (2002)
Wang, L., Zou, X.: Harmless delays in Cohen–Grossberg neural networks. Physica D 170, 162–173 (2002)
Gupta, P.D., Majee, N.C., Roy, A.B.: Stability, bifurcation and global existence of a Hopf bifurcating periodic solution for a class of three-neuron delayed network models. Nonlinear Anal. 67, 2934–2954 (2007)
Sun, C., Han, M., Pang, X.: Global Hopf bifurcation analysis on a BAM neural network with delays. Phys. Lett. A 360, 689–695 (2007)
Wei, J., Li, M.Y.: Global existence of periodic solutions in a tri-neuron network model with delays. Physica D 198, 106–119 (2004)
Wei, J., Yuan, Y.: Synchronized Hopf bifurcation analysis in a neural network model with delays. J. Math. Anal. Appl. 312, 205–229 (2005)
Xu, X.: Local and global Hopf bifurcation in a two-neuron network with multiple delays. Int. J. Bifurc. Chaos Appl. Sci. Eng. 4, 1015–1028 (2008)
Campbell, S.A., Yuan, Y., Bungay, S.D.: Equivariant Hopf bifurcation in a ring of identical cells with delayed coupling. Nonlinearity 18, 2827–2846 (2005)
Yuan, Y.: Dynamics in a delayed-neural network. Chaos Solitons Fractals 33, 443–454 (2007)
Yan, X.: Bifurcation analysis in a simplified tri-neuron BAM network model with multiple delays. Nonlinear Anal., Real World Appl. 9, 963–976 (2008)
Campbell, S.A., Yuan, Y.: Zero singularities of codimension two and three in delay differential equations. Nonlinearity 21, 2671–2691 (2008)
Guo, S., Chen, Y., Wu, J.: Two-parameter bifurcations in a network of two neurons with multiple delays. J. Differ. Equ. 244, 444–486 (2008)
Liao, X., Guo, S., Li, C.: Stability and bifurcation analysis in tri-neuron model with time delay. Nonlinear Dyn. 49, 319–345 (2007)
Liao, X., Wong, K., Wu, Z.: Bifurcation analysis in a two neuron system with distributed delays. Physica D 149, 123–141 (2001)
Nayfeh, A.H.: Perturbation Methods. Wiley-Interscience, New York (1973)
Nayfeh, A.H.: Introduction to Perturbation Techniques. Wiley-Interscience, New York (1981)
Kuznetsov, Y.A.: Elements of Applied Bifurcation Theory, 3rd edn. Springer, New York (2004)
Wiggins, S.: Introduction to Applied Nonlinear Dynamical Systems and Chaos. Springer, New York (1990)
Das, S.L., Chatterjee, A.: Multiple scales without center manifold reductions for delay differential equations near Hopf bifurcations. Nonlinear Dyn. 30, 323–335 (2002)
Nayfeh, A.H.: Order reduction of retarded nonlinear systems–the method of multiple scales versus center-manifold reduction. Nonlinear Dyn. 51, 483–500 (2008)
Yu, P.: Computation of normal forms via a perturbation technique. J. Sound Vib. 211, 19–38 (1998)
Yu, P.: Symbolic computation of normal forms for resonant double Hopf bifurcations using a perturbation technique. J. Sound Vib. 247, 615–632 (2001)
Yu, P.: Analysis on double Hopf bifurcation using computer algebra with the aid of multiple scales. Nonlinear Dyn. 27, 19–53 (2002)
Jiang, W., Yuan, Y.: Bogdanov–Takens singularity in van der Pol’s oscillator with delayed feedback. Physica D 227, 149–161 (2007)
Jiang, W., Wang, H.: Hopf-transcritical bifurcation in retarded functional differential equations. Nonlinear Anal. 73, 3626–3640 (2010)
Ma, S., Lu, Q., Feng, Z.: Double Hopf bifurcation for van der Pol–Duffing oscillator with parametric delay feedback control. J. Math. Anal. Appl. 338, 993–1007 (2008)
Ding, Y., Jiang, W., Yu, P.: Double Hopf bifurcation in delayed van der Pol–Duffing equation. Int. J. Bifurc. Chaos Appl. Sci. Eng. (accepted)
Faria, T., Magalhães, L.: Normal forms for retarded functional differential equation and applications to Bogdanov-Takens singularity. J. Differ. Equ. 122, 201–224 (1995)
Acknowledgements
The work was supported by the National Natural Science Foundation of China (NSFC) and the National Science and Engineering Research Council of Canada (NSERC), the Heilongjiang Provincial Natural Science Foundation (No. A200806), and the Program of Excellent Team in HIT. The first author also acknowledges the financial support received from the China Scholarship Council for her visiting the University of Western Ontario.
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Appendix
Appendix
In this Appendix, we compute the normal form of (22) on center manifold near the Hopf-zero bifurcation critical point (b c ,τ c ) using the center manifold reduction (CMR) method. To achieve this, first re-scale time by \(\tilde{t}\mapsto (t/\tau)\) to normalize the delay so that system (22) becomes
The trivial equilibrium of (34) is y 1=y 2=y 3=0. At the critical point (b,τ)=(b c ,τ c ), we choose
with
Then the linearized equation of (34) at the trivial equilibrium can be written as
where \(L_{0}\phi=\int_{-1}^{0}\,\mathrm{d}\eta(\theta)\phi(\theta)\), ϕ∈C=C([−1,0],R3), and the bilinear form [51] on C∗×C (∗ stands for adjoint) is
in which ϕ∈C, ψ∈C∗. Then the phase space C is decomposed by Λ={±iωτ c ,0} as C=P⊕Q, where , and the bases for P and its adjoint P ∗ are given respectively by
and
where \(h=(3+\frac{3b_{c}\tau_{c}(1+\mathrm{i}\omega)e^{-\mathrm{i}\omega \tau_{c}}}{b_{c}e^{-\mathrm{i}\omega\tau_{c}}-1})^{-1}\).
We also use the same bifurcation parameters given by b=b c +b ε and τ=τ c +τ ε in (34), where b ε and τ ε are perturbation parameters, and denote ε=(b ε ,τ ε ). Then (34) can be written as
where
and
We now consider the enlarged phase space BC of functions from [−1,0] to R3, which are continuous on [−1,0) with a possible jump discontinuity at zero. This space can be identified as C×R3. Thus, its elements can be written in the form \(\tilde{\varphi}=\varphi+X_{0}c\), where φ∈C, c∈R3 and X 0 is a 3×3 matrix-valued function, defined by X 0(θ)=0 for θ∈[−1,0) and X 0(0)=I. In the BC, (35) becomes an abstract ODE,
where u∈C, and A is defined by
and
By using the continuous projection π: BC↦P, π(ϕ+X 0 c)=Φ[(Ψ,ϕ)+Ψ(0)c], we can decompose the enlarged phase space by Λ={±iωτ c ,0} as BC=P⊕Kerπ, where Kerπ={ϕ+X 0 c: π(ϕ+X 0 c)=0}, denoting the Kernel under the projection π. Let \(\eta=(\eta_{1},\bar{\eta}_{1},\eta_{2})^{\mathrm{T}}\), \(v_{\tilde{t}}\in Q^{1} :=Q\cap\mathrm{C}^{1}\subset\mathrm{Ker}^{\pi}\), and \(A_{Q^{1}}\) the restriction of A as an operator from Q 1 to the Banach space Kerπ. Further, denote \(u_{\tilde{t}}=\varPhi \eta+v_{\tilde{t}}\). Then Eq. (36) is decomposed to the form:
where B=diag{iωτ c ,−iωτ c ,0}.
Next, let \(M_{2}^{1}\) denote the operator defined in \(V_{2}^{5}(\mathrm{C}^{3}\times \mathrm{Ker}^{\pi})\), with
where \(V_{2}^{5}(\mathrm{C}^{3})\) represents the linear space of the second-order homogeneous polynomials in five variables \((\eta_{1},\bar{\eta}_{1},\eta_{2},b_{\varepsilon},\tau_{\varepsilon})\) with coefficients in C3. Then we may choose the decomposition \(V_{2}^{5}(\mathrm{C}^{3})= \mathrm{Im}(M_{2}^{1})\oplus\mathrm{Im}(M_{2}^{1})^{c} \) with complementary space \(\mathrm{Im}(M_{2}^{1})^{c}\) spanned by the elements b ε η 1 e 1, τ ε η 1 e 1, η 1 η 2 e 1, \(b_{\varepsilon}\bar{\eta}_{1}e_{2}\), \(\tau_{\varepsilon}\bar{\eta}_{1}e_{2}\), \(\bar{\eta}_{1}\eta_{2}e_{2}\), τ ε η 2 e 3, b ε η 2 e 3, \(\eta_{2}^{2}e_{3}\), \(\eta_{1}\bar{\eta}_{1}e_{3}\), where e i (i=1,2,3) are unit vectors.
Consequently, the normal form of (35) on the center manifold near the equilibrium (0,0,0) associated with the critical point (b ε ,τ ε )=(0,0) has the form
where \(g_{2}^{1}\) is the function giving the quadratic terms in (η,ε) for \(v_{\tilde{t}}=0\), and is determined by \(g_{2}^{1}(\eta,0,\varepsilon)=\mathit{Proj}_{(\mathrm{Im}(M_{2}^{1}))^{c}}\times f_{2}^{1}(\eta,0,\varepsilon)\), where \(f_{2}^{1}(\eta,0,\varepsilon)\) is the function giving the quadratic terms in (η,ε) for \(v_{\tilde{t}}=0\) defined by the first equation of (37). Thus, the normal form, truncated at the quadratic order terms, is given by
where \(h=(3+\frac{3b_{c}\tau_{c}(1+\mathrm{i}\omega)e^{-\mathrm{i}\omega \tau_{c}}}{b_{c}e^{-\mathrm{i}\omega\tau_{c}}-1})^{-1}\).
To find the normal form up to third order, similarly, let \(M_{3}^{1}\) denote the operator defined in \(V_{3}^{3}(\mathrm{C}^{3}\times \mathrm{Ker}^{\pi})\), with
where \(V_{3}^{3}(\mathrm{C}^{3})\) denotes the linear space of the third-order homogeneous polynomials in three variables: η 1, \(\bar{\eta}_{1}\), and η 2 with coefficients in C3. Then one may choose the decomposition \(V_{3}^{3}(\mathrm{C}^{3})=\mathrm{Im}(M_{3}^{1})\oplus\mathrm{Im}(M_{3}^{1})^{c} \) with complementary space \(\mathrm{Im}(M_{3}^{1})^{c}\) spanned by the elements \(\eta_{1}\eta_{2}^{2}e_{1}\), \(\eta_{1}^{2}\bar{\eta}_{1}e_{2}\), \(\bar{\eta}_{1}\eta_{2}^{2}e_{2}\), \(\eta_{1}\bar{\eta}_{1}^{2}e_{3}\), \(\eta_{2}^{3}e_{1}\), \(\eta_{1}\bar{\eta}_{1}\eta_{2}e_{3}\), where e i (i=1,2,3) are unit vectors.
Therefore, the normal form up to third-order terms is given by
where
and \(f_{3}^{1}(\eta,0,0)\), is the function giving the cubic terms in \((\eta, \varepsilon, v_{\tilde{t}})\) for ε=0, and \(v_{\tilde{t}}=0\), is defined by the first equation of (37). Finally, the normal form on the center manifold arising from (37) becomes
where d i (i=1,2) and P i (i=1,2,3,4) are given in (31).
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Ding, Y., Jiang, W. & Yu, P. Hopf-zero bifurcation in a generalized Gopalsamy neural network model. Nonlinear Dyn 70, 1037–1050 (2012). https://doi.org/10.1007/s11071-012-0511-2
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DOI: https://doi.org/10.1007/s11071-012-0511-2