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Boundary Value Problems

, 2018:111 | Cite as

Dynamical analysis of a competition and cooperation system with multiple delays

  • Xin Zhang
  • Zizhen Zhang
  • Matthew J. Wade
Open Access
Research
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Abstract

This paper is concerned with a competition and cooperation system with multiple constant delays relating to economic enterprise. The stability of the unique positive equilibrium is investigated and the existence of Hopf bifurcations is demonstrated by analysing the associated characteristic equation. Furthermore, the explicit formulae determining the stability and the direction of periodic solutions bifurcating from Hopf bifurcations are obtained by applying centre manifold theory and the normal form method. Finally, special attention is paid to some numerical simulations in order to support the theoretical predictions.

Keywords

Competition and cooperation system Time delays Stability Local Hopf bifurcation 

MSC

34K10 34K20 37L10 

1 Introduction

Delay differential equations (DDEs) which involve delays and change in time [1, 2] exhibit considerably more complex dynamical behaviour than ordinary differential equations (ODEs), since the delays could cause a stable equilibrium to become unstable and fluctuate. Many complicated and large-scale systems in nature and society can be modelled as DDEs due to their flexibility and generality for representing virtually any natural and man-made structure. Research of the dynamical behaviour of DDEs has received much attention in interdisciplinary subjects, including natural sciences [3, 4, 5, 6], engineering [7, 8], life sciences [9] and others [10, 11, 12, 13, 14, 15, 16]. In recent decades, scientists have focused on the stability and bifurcation phenomena of the continuous-time autonomous predator–prey system with multiple delays (see, for example, [17, 18, 19, 20, 21, 22]). In fact, there is a strong relationship between how species co-evolve in nature and how different enterprises co-exist in societal economics, leading to significant research into the delayed competition and cooperation model for business enterprises [23, 24, 25], which are governed by the following system of ODEs:
$$ \textstyle\begin{cases} \dot{x_{1}}(t)=r_{1}x_{1}(t)(1-\frac{{x_{1}(t)}}{K_{1}}-\frac{\alpha (x_{2}(t)-c_{2})^{2}}{K_{2}}),\\ \dot{x_{2}}(t)=r_{2}x_{2}(t)(1-\frac{{x_{2}(t)}}{K_{2}}+\frac{\beta (x_{1}(t)-c_{1})^{2}}{K_{1}}), \end{cases} $$
(1.1)
where \(x_{1}(t)\), \(x_{2}(t)\) denote the output of enterprise \(x_{1}\) and enterprise \(x_{2}\) at time t, respectively, \((x_{1}(t),x_{2}(t))\in\mathbb{R}^{1}\times\mathbb{R}^{1}\); \(r_{i}\) (\(i=1,2\)) represents the intrinsic growth rate for the output of the two enterprises; \(K_{i}\) (\(i=1,2\)) is a measure of the load capacity of the two enterprises in an unrestricted natural market; α, β are the coefficients of competition of enterprise \(x_{1}\) and \(x_{2}\), respectively; \(c_{i}\) (\(i=1,2\)) denotes the initial production of them. All the parameters assume strictly positive values.
Let \(a_{1}=\frac{r_{1}}{K_{1}}\), \(a_{2}=\frac{r_{2}}{K_{2}}\), \(b_{1}=\frac{\alpha r_{1}}{K_{2}}\), \(b_{2}=\frac{\beta r_{2}}{K_{1}}\), \(d_{i}=r_{i}-a_{i}c_{i}\), \(\forall i=1,2\), \(u(t)=x_{1}(t)-c_{1}\), \(v(t)=x_{2}(t)-c_{2}\). System (1.1) becomes
$$ \textstyle\begin{cases} \frac{{d}{u}(t)}{ {d}t}=(u(t)+c_{1})(d_{1}-a_{1}u(t)-b_{1}v^{2}(t)),\\ \frac{{d}{v}(t)}{ {d}t}=(v(t)+c_{2})(d_{2}-a_{2}v(t)+b_{2}u^{2}(t)),\\ u(0)>0,\qquad v(0)>0, \end{cases} $$
(1.2)
where \((u(t),v(t))\in\mathbb{R}^{1}\times\mathbb{R}^{1}\).
Taking into account the influence of the prior history of the enterprises, authors have introduced a time delay, τ, to the feedback in model (1.2) [26], which is a more realistic approach for understanding competition and cooperation dynamics. Delays can induce oscillations and periodic solutions through bifurcations as the delay is increased. Therefore, it is interesting to investigate the following delayed model:
$$ \textstyle\begin{cases} \dot{y_{1}}(t)=(y_{1} (t)+c_{1})(d_{1}-a_{1}y_{1}(t-\tau _{1})-b_{1}y_{2}^{2}(t-\tau_{2})),\\ \dot{y_{2}}(t)=(y_{2} (t)+c_{2})(d_{2}-a_{2}y_{2}(t-\tau _{1})+b_{2}y_{1}^{2}(t-\tau_{3})), \end{cases} $$
(1.3)
where \(y_{i}(t)\) (\(i=1,2\)) denotes the output of two enterprises at time t, \(a_{i}\), \(b_{i}\) (\(i=1,2\)) denote the intraspecific competition rate and interspecific effect rate between them, where \(a_{i}\), \(b_{i}\), \(c_{i}\), \(d_{i}\) (\(i=1,2\)) are positive constants. \(\tau_{1}\) denotes the interior delays of themselves, \(\tau_{i}\) (\(i=2,3\)) denotes the exterior delays between each other, and \(\tau_{i}\) (\(i=1,2,3\)) is non-negative constant delays.
We define \(\mathbb{R}_{+}\equiv\{x\in\mathbb{R}:x\geq0\}\), \(\operatorname{int} \mathbb{R}_{+}\equiv\{x\in\mathbb{R}:x >0\}\), \(\hat{\tau}=\max\{\tau_{1},\tau_{2},\tau_{3}\}\). Denote by \(C([-\hat{\tau},0],\mathbb{R}_{+})\) the infinite dimensional Banach space of continuous functions from the interval \([-\hat{\tau},0]\) into \(\mathbb{R}_{+}\), equipped with the uniform norm. We assume that the initial data for model (1.3) is taken from
$$ X=C\bigl([-\hat{\tau},0],\mathbb{R}_{+}\bigr)\times C \bigl([-\hat{\tau},0],\mathbb{R}_{+}\bigr). $$
(1.4)
The variables \(y_{1}(t)\) and \(y_{2}(t)\) in model (1.3) belong to X for \(t\in[-\hat{\tau},0]\).

By [1] (Theorem 2.1 and 2.3, Chap. 2, p. 41), solutions of system (1.3) with the initial value in C exist and are unique for all \(t>0\).

Liao [23] assumed \(\tau_{i}\ (i=1,2,3)=\tau\) and Li [24] considered \(\tau_{1}=0\), regarding τ and \(\tau_{2}+\tau_{3}\) as the bifurcation parameters, respectively. They investigated the existence of the unique positive equilibrium and proved that the Hopf bifurcation can occur as the bifurcation parameter crosses some critical value, and studied the direction of Hopf bifurcation and stability of the periodic solutions. In [27], Liao considered \(\tau_{2}=\tau_{3}\neq\tau_{1}\), analysed the stability of the positive equilibrium and the existence of local Hopf bifurcation and provided some numerical simulations. However, they did not give the underlying description of the bifurcated periodic solution.

In the more realistic competition and cooperation model, the interior delays exist (i.e. \(\tau_{1}\neq0\)) and the exterior delays are not necessarily equal (i.e. \(\tau_{2}\neq \tau_{3}\)). Based on these observations, we studied the system of (1.3) that could better describe the real system behaviour. Compared with the models from the literature [23, 27], the dynamical behaviour of system (1.3) is more complicated than the above models.

In this paper, we have taken the delay \(\tau_{1}:=\tau_{2}+\tau_{3}\) as the bifurcation parameter and show that when \(\tau_{1}\) passes through the critical values, the positive equilibrium loses its stability and a Hopf bifurcation occurs. Furthermore, we give details of the bifurcation values that describe the direction of the Hopf bifurcation and the stability of the bifurcated periodic solution using centre manifold theory and the normal form method introduced by Hassard et al. [28]. Finally, some numerical simulations and conclusions are given to illustrate the theoretical predictions.

2 The existence and the property of the local Hopf bifurcation

In this section, we give the following results about the existence and stability of the positive equilibrium of system (1.3).

Proposition 1

For system (1.3), assume that\(a_{2}\), \(b_{1}\), \(d_{1}\), \(d_{2}\)are positive constants such that
\((\mathrm{H}_{1})\)

\(a_{2}^{2}d_{1}>b_{1}d_{2}^{2}\)

holds, then the system has a unique positive equilibrium\(E^{\ast }=(y_{1}^{\ast},y_{2}^{\ast})\). Furthermore, when system (1.3) has no delay, i.e. \(\tau_{i}\ (i=1,2,3)=0\), then\(E^{\ast}\)is globally asymptotically stable.

Proof

For system (1.3), assumption \((\mathrm{H}_{1})\) is the parameter condition which ensures the existence of the positive equilibrium \(E^{\ast}\). The proof for the existence of \(E^{\ast}\) is similar to that in [23], we omit it here.

We now prove the global asymptotic stability. When \(\tau_{i}\ (i=1,2,3)=0\), system (1.3) is reduced to the ODE system (1.1). Defining Dulac function as \(B(x_{1},x_{2})=\frac{1}{x_{1}x_{2}}\), and
$$ \begin{aligned}D&=\frac{\partial \{\frac{1}{x_{1}x_{2}} [r_{1}x_{1}(t)(1-\frac {{x_{1}(t)}}{K_{1}}-\frac{\alpha(x_{2}(t)-c_{2})^{2}}{K_{2}}) ] \}}{\partial x_{1}}+\frac{\partial \{\frac {1}{x_{1}x_{2}} [r_{2}x_{2}(t)(1-\frac{{x_{2}(t)}}{K_{2}}+\frac{\beta (x_{1}(t)-c_{1})^{2}}{K_{1}}) ] \}}{\partial x_{2}} \\ &=-{\frac{r_{{1}}}{K_{{1}}x_{2}}}-{\frac{r_{{2}}}{x_{1}K_{{2}}}},\end{aligned} $$
we easily get \(D<0\) in the \(\operatorname{int} \mathbb{R}_{+}^{2}=\{ (x_{1},x_{2}):x_{1}>0,x_{2}>0\}\) space. By [29] (Theorem 4.1.2, Chap. 4, p. 72), it follows from Dulac’s principle that the system has no closed path curve. So \(E^{\ast}\) is globally asymptotically stable when system (1.3) has no delay and also when the non-negative delays are sufficiently small. □
If a pair of complex roots with negative real parts and non-zero imaginary parts cross the imaginary axis as τ increases, this potentially results in Hopf bifurcation and the positive equilibrium \(E^{\ast}\) loses stability. Now we discuss the existence of a local Hopf bifurcation occurring at \(E^{\ast}\). Let \(u_{1}(t)=y_{1}(t)-y_{1}^{\ast}\), \(u_{2}(t)=y_{2}(t)-y_{2}^{\ast}\), then system (1.3) becomes
$$ \textstyle\begin{cases} \dot{{u}}_{1}(t)= (u_{1} (t)+y_{1}^{\ast}+c_{1} )[-a_{1}u_{1}(t-\tau_{1}) -b_{1}{u_{2}}^{2}(t-\tau_{2})-2b_{1}y_{2}^{\ast}u_{2}(t-\tau_{2})],\\ \dot{{u}_{2}}(t)= (u_{2} (t)+y_{2}^{\ast}+c_{1} )[-a_{2}u_{2}(t-\tau_{1}) +b_{2}{u_{1}}^{2}(t-\tau_{3})+2b_{2}y_{2}^{\ast}u_{1}(t-\tau_{3})], \end{cases} $$
(2.1)
the linearization of system (2.1) at \(E^{\ast}\) is
$$ \textstyle\begin{cases} \dot{u}_{1}(t)=-a_{1}(y_{1}^{\ast}+c_{1})u_{1}(t-\tau _{1})-2b_{1}y_{2}^{\ast}(y_{1}^{\ast}+c_{1})u_{2}(t-\tau_{2}),\\ \dot{u}_{2}(t)=2b_{2}y_{2}^{\ast}(y_{2}^{\ast}+c_{1})u_{1}(t-\tau _{3})-a_{2}(y_{2}^{\ast}+c_{1})u_{2}(t-\tau_{1}), \end{cases} $$
(2.2)
and the associated characteristic equation of (2.2) is
$$ \begin{vmatrix} \lambda+a_{1} \bigl(y_{1}^{\ast}+c_{1}\bigr)e^{-\lambda\tau_{1}} &2b_{1}y_{2}^{\ast}\bigl(y_{1}^{\ast}+c_{1} \bigr)e^{-\lambda\tau_{2}} \\ -2b_{2}y_{2}^{\ast}\bigl(y_{2}^{\ast}+c_{1} \bigr)e^{-\lambda\tau_{3}} &\lambda+a_{2}\bigl(y_{2}^{\ast}+c_{1} \bigr)e^{-\lambda\tau_{1}} \end{vmatrix} =0. $$
For the above characteristic equation, it is hard to do the complete analysis for the distribution of the roots, so we assume that
\((\mathrm{H}_{2})\)

\(\tau_{2}+\tau_{3}= \tau_{1}\)

holds, and \(\tau_{1}\triangleq\tau\).
Hence, the characteristic equation is equivalent to
$$ \lambda^{2}+e^{-\lambda\tau}(p\lambda+q)+re^{-2\lambda\tau}=0, $$
(2.3)
where \(p=a_{1}(y_{1}^{\ast}+c_{1})+a_{2}(y_{2}^{\ast}+c_{1})\), \(q=4b_{1}b_{2}(y_{2}^{\ast})^{2}(y_{1}^{\ast}+c_{1})(y_{2}^{\ast}+c_{1})\), \(r=a_{1}a_{2}(y_{1}^{\ast}+c_{1})(y_{2}^{\ast}+c_{1})\).

Since the characteristic equation (2.3) has the same form as equation (2.4) in [30], so by Theorem 2.5 in [30], we can get the following result, which presents the conditions for a Hopf bifurcation to occur in system (1.3).

Proposition 2

Suppose that\((\mathrm{H}_{1})\)and\((\mathrm{H}_{2})\)hold. Then
$$ \tau^{j}_{k}=1/{\omega_{k}}\bigl[\arccos q/ \bigl({\omega^{2}_{k}-r}\bigr)+2j\pi \bigr],\quad k=1,2,3,4,j=0,1,2, \ldots $$
are Hopf bifurcation values at\(E^{\ast}\), where\(i\omega _{k}\) (\(k=1,2,3,4\)) are the roots of (2.3). And\(E^{\ast}\)is locally asymptotically stable for\(\tau\in[0,\tau _{1}^{0}]\)and unstable where\(\tau>\tau_{1}^{0}\).

Remark 1

The characteristic equation (2.3) has some pairs of purely imaginary roots denoted by \(\lambda=\pm i\omega_{k}\) with \(\tau=\tau^{j}_{k}\) under the condition of \((\mathrm{H}_{1})\), \((\mathrm{H}_{2})\). Define \(\tau^{0}=\tau^{0}_{k_{0}}=\min_{1\leq k\leq4}\{\tau^{0}_{k}\} \), \(\omega_{0}=\omega_{k_{0}}\), where \(k_{0}\in\{1,2,3,4\}\). Then \(\tau^{0}\) is the first value of τ such that (2.3) has purely imaginary roots. For convenience, we denote \(\tau^{j}_{k}\) by \(\tau^{j}\) (\(j=0,1,2,\ldots\)) for fixed \(k\in\{1,2,3,4\}\).

Remark 2

Let \(\lambda(\tau)=\alpha(\tau)\pm i\omega(\tau)\) be the roots of (2.3) near \(\tau=\tau^{j}\) satisfying \(\alpha(\tau^{j})=0\), \(\omega(\tau^{j})=\omega_{0}\) (\(j=0,1,2,\ldots\)). By the theory of DDEs, for \(\forall\tau^{j}_{k}\), \(\exists\varepsilon>0\) s.t. \(\lambda(\tau )\) in \(|\tau-\tau^{j}_{k}|<\varepsilon\) about τ is continuous and differentiable. The transversality condition \(\frac{{d}\operatorname{Re}\lambda(\tau)}{{d}\tau } |_{\tau=\tau_{j}}>0\) is satisfied (more details are provided in [30]).

In the previous part, it was shown that system (2.1) undergoes a Hopf bifurcation under certain conditions. Here we will derive explicit formulae determining the direction of the Hopf bifurcation and the stability of the periodic solutions bifurcating from \(E^{\ast}\) at \(\tau^{j}\) (\(j=0,1,2,\ldots\)), by employing centre manifold theory and the normal form method. For convenience, denote \(\tau^{j}\) by τ̃ and \(\tau =\widetilde{\tau}+\mu\), \(\mu\in\mathbb{R}\), then \(\mu=0\) is the Hopf bifurcation value for system (1.3), where \(\widetilde{\tau}=\widetilde{\tau_{2}}+\widetilde{\tau_{3}}\), \(\tau=\widetilde{\tau_{2}}+\widetilde{\tau_{3}}+\mu\). Without loss of generality, assume \(\widetilde{\tau_{2}}<\widetilde{\tau_{3}}\).

The discussion will be divided into five steps as follows.

Step 1. Transform system ( 2.1 ) into the abstract ODE.

System (2.1) can locally be represented as the following DDE in \(C=C([-\widetilde{\tau},0],R^{2})\):
$$ \dot{u}(t)=L_{\mu}(u_{t})+F( \mu,u_{t}), $$
(2.4)
where \(u(t)= (u_{1}(t),u_{2}(t) )^{T}\), \(u_{t}(\theta)=u(t+\theta )\), \(L_{\mu}:C\rightarrow R\) is a bounded linear operator and \(F:R\times C\rightarrow R\) is continuous and differentiable with
$$ \begin{aligned} L_{\mu}\phi&= (\widetilde{ \tau}+\mu) \begin{pmatrix}-a_{1}(y_{1}^{\ast}+c_{1})\phi_{1}(-\tau _{1})+2b_{1}y_{2}^{\ast}(y_{1}^{\ast}+c_{1})\phi_{2}(-\tau_{2})\\ 2b_{2}y_{2}^{\ast}(y_{2}^{\ast}+c_{1})\phi_{1}(-\tau _{3})-a_{2}(y_{2}^{\ast}+c_{1})\phi_{2}(-\tau_{1}) \end{pmatrix} , \end{aligned} $$
and
$$ \begin{aligned} F(\mu,\phi)={}&(\widetilde{\tau}+\mu)\\ &\times\begin{pmatrix}-a_{1}\phi_{1}(0)\phi_{1}(-\tau_{1})-b_{1}\phi_{1}(0)\phi ^{2}_{2}(-\tau_{2}) -2b_{1}y^{\ast}_{2}\phi_{1}(0)\phi_{2}(-\tau_{2})-b_{1}(y^{\ast }_{1}+c_{1})\phi^{2}_{2}(-\tau_{2}) \\-a_{2}\phi_{2}(0)\phi_{2}(-\tau_{1})+b_{2}\phi_{2}(0)\phi ^{1}_{2}(-\tau_{3}) +2b_{2}y^{\ast}_{2}\phi_{2}(0)\phi_{1}(-\tau_{3})+b_{2}(y^{\ast }_{2}+c_{1})\phi^{2}_{1}(-\tau_{3}) \end{pmatrix} ,\end{aligned} $$
where \(\phi=(\phi_{1}(\theta),\phi_{2}(\theta))\in C\).
By the Riesz representation theorem, there exists a \(2\times2\) matrix whose elements are a bounded variation function \(\eta(\theta,\mu)\) in \(\theta\in[-\widetilde{\tau},0]\) such that
$$ L_{\mu}\phi= \int_{-\widetilde{\tau}}^{0} d\eta(\theta,\mu)\phi(\theta), \quad\phi\in C, $$
where \(\eta(\theta,\mu)\) can be chosen as
$$\eta(\theta,\mu)= \textstyle\begin{cases} (\widetilde{\tau}+\mu)(M+N+P),&\theta\in[-\widetilde{\tau }_{2},0),\\ (\widetilde{\tau}+\mu)(M+N),&\theta\in(-\widetilde{\tau }_{3},-\widetilde{\tau}_{2}),\\ (\widetilde{\tau}+\mu)M,&\theta\in(-\widetilde{\tau},-\widetilde {\tau}_{3}],\\ 0,&\theta=-\widetilde{\tau}, \end{cases} $$
with
$$ \begin{gathered} M= \begin{pmatrix}-a_{1}(y_{1}^{\ast}+c_{1})&0\\0&-a_{2}(y_{2}^{\ast}+c_{1}) \end{pmatrix} , \qquad N= \begin{pmatrix}0&-2b_{1}y_{2}^{\ast}(y_{1}^{\ast}+c_{1})\\0&0 \end{pmatrix} , \\ P= \begin{pmatrix}0&0\\2b_{2}y_{2}^{\ast}(y_{2}^{\ast}+c_{1})&0 \end{pmatrix} . \end{gathered} $$
For \(\phi\in C\), let
$$\begin{gathered} A(\mu)\phi(\theta)= \textstyle\begin{cases} d\phi(\theta)/{d\theta},&\theta\in[-\widetilde{\tau},0),\\ \int_{-\widetilde{\tau}}^{0}d\eta(\mu,\theta)\phi(\theta),&\theta=0, \end{cases}\displaystyle \\ R(\mu)\phi(\theta)= \textstyle\begin{cases} 0,&\theta\in[-\widetilde{\tau},0),\\ F(\mu,\phi),& \theta=0, \end{cases}\displaystyle \end{gathered}$$
then system (2.4) is equivalent to the following abstract operator equation:
$$ \dot{u}(t)=A({\mu})u_{t}+R(\mu)u_{t}. $$
(2.5)

Step 2. Calculate the eigenfunctions of \(A=A(0)\) and the adjoint operator \(A^{\ast}\) corresponding to \(i\omega_{0}\widetilde{\tau}\) and \(-i\omega _{0}\widetilde{\tau}\) .

For \(\psi\in C([0,\widetilde{\tau}],\mathbb{(}{C}^{2})^{\ast})\), where \(\mathbb{(}{C}^{2})^{\ast}\) is the two-dimensional complex space of row vectors, we define the adjoint operator \(A^{\ast}\) of A
$$A^{\ast}\psi(s)= \textstyle\begin{cases} -d\psi(s)/{ds},&s\in(0,\widetilde{\tau}],\\\int_{-\widetilde {\tau}}^{0}d\eta^{T}(\mu,t)\psi(-t),& s=0, \end{cases} $$
and the bilinear form is given by
$$ \bigl\langle \psi(s),\phi(\theta)\bigr\rangle =\overline{\psi}(0)\phi(0) - \int_{-\widetilde{\tau}}^{0} \int_{\xi=0}^{\theta}\overline{\psi }^{T}(\xi- \theta)\,d\eta(\theta)\phi(\xi)\,d\xi, $$
where \(\eta(\theta)=\eta(\theta,0)\). Then \(A=A(0)\) and \(A^{\ast}(0)\) are adjoint operators.
By [27], \(\pm i\omega_{0}\widetilde{\tau}\) are eigenvalues of \(A(0)\), so they are also eigenvalues of \(A^{\ast}(0)\). Suppose that \(q(\theta)=(1,\alpha)^{T}e^{i\omega_{0}\theta}\) is the eigenfunction of \(A(0)\) corresponding to the eigenvalue \(i\omega _{0}\widetilde{\tau}\) and \(q^{\ast}(s)=G(\beta,1)e^{i\omega_{0}s}\) is the eigenfunction of \(A^{\ast}\) corresponding to the eigenvalue \(-i\omega_{0}\widetilde{\tau}\), where
$$\begin{gathered} \alpha=- \bigl[-i\omega_{0}+a_{2}\bigl(y^{\ast}_{2}+c_{1} \bigr)i\omega _{0}e^{-i\omega_{0}\widetilde{\tau}} \bigr]/{2b_{1}y^{\ast}_{2} \bigl(y^{\ast }_{1}+c_{1}\bigr)e^{-i\omega_{0}\widetilde{\tau}}}, \\ \beta=- \bigl[i\omega_{0}+a_{1}\bigl(y^{\ast}_{1}+c_{1} \bigr)i\omega_{0}e^{-i\omega _{0}\widetilde{\tau}} \bigr]/{2b_{1}y^{\ast}_{2} \bigl(y^{\ast }_{1}+c_{1}\bigr)e^{-i\omega_{0}\widetilde{\tau}}}, \\ \begin{aligned}G&= \bigl\{ \overline{\beta}+\alpha-2\alpha\overline{\beta}b_{1}y^{\ast }_{2} \bigl(y^{\ast}_{1}+c_{1}\bigr)\widetilde{ \tau}_{2}e^{i\omega_{0}\widetilde {\tau}_{2}} +2\alpha\overline{\beta}b_{2}y^{\ast}_{2} \bigl(y^{\ast}_{2}+c_{1}\bigr)\widetilde { \tau}_{3}e^{i\omega_{0}\widetilde{\tau}_{3}} \\ &\quad{}+ \bigl[-\overline{\beta}a_{1}\bigl(y^{\ast}_{1}+c_{1} \bigr)-\alpha _{2}\bigl(y^{\ast}_{2}+c_{1} \bigr) \bigr]\widetilde{\tau}e^{i\omega_{0}\widetilde {\tau}} \bigr\} ^{-1},\end{aligned}\end{gathered} $$
which assures that \(\langle{q}^{\ast}(s),q(\theta)\rangle=1\), \(\langle{q}^{\ast }(s),\overline{q}(\theta)\rangle=0\).

Step 3. Obtain the reduced system on the centre manifold.

In this part, we will use the same notations as in [28] and compute the coordinates to describe the centre manifold \(\mathbf {C}_{0}\) at \(\mu=0\) (a local centre manifold is in general not unique, and the dimension of local centre manifold is 2). Let \(u_{t}\in C\) be the solution of system (2.5) when \(\mu=0\), and define
$$ z(t)=\bigl\langle q^{\ast},u_{t}\bigr\rangle ,\qquad W(t,\theta)=u_{t}( \theta)-z(t)q(\theta)-\overline {z}(t)\overline{q}(\theta), $$
(2.6)
where z and z̅ are local coordinates for the centre manifold \(\mathbf{C}_{0}\) in the direction of \(q^{\ast}\) and \(\overline{q}^{\ast}\). On the centre manifold \(\mathbf{C}_{0}\), we have \(W(t,\theta )=W(z(t),\overline{z}(t),\theta)\), where
$$\begin{aligned} W(z,\overline{z},\theta)=W_{20}(\theta)z^{2}/{2}+W_{11}( \theta )z\overline{z}+W_{02}(\theta)\overline{z}^{2}/{2}+ \cdots. \end{aligned}$$
(2.7)
The existence of a centre manifold enables us to reduce (2.5) to an ODE on \(\mathbf{C}_{0}\). Note that W is real if \(u_{t}\) is real, we consider only real solutions. For solution \(u_{t}\in\mathbf{C}_{0}\) of system (2.5) at \(\mu=0\),
$$ \begin{aligned}[b] \dot{z}(t)&=\bigl\langle q^{\ast},\dot{u}_{t}\bigr\rangle =\bigl\langle q^{\ast},A(u_{t})+R(u_{t})\bigr\rangle =\bigl\langle A^{\ast}\bigl(q^{\ast}\bigr),u_{t}\bigr\rangle +\bigl\langle q^{\ast},R(u_{t})\bigr\rangle \\ &=i\omega_{0}\widetilde{\tau}z(t)+\overline{q}^{\ast}(0)\cdot f\bigl(0,W(z,\overline{z},\theta)+2\operatorname{Re}\bigl\{ z(t)q(\theta)\bigr\} \bigr) \\ &=i\omega_{0}\widetilde{\tau}z(t)+\overline{q}^{\ast}(0)\cdot f(0,u_{t}),\end{aligned} $$
(2.8)
with
$$ \overline{q}^{\ast}(0)\cdot f(0,u_{t})\triangleq g(z,\overline {z}). $$
Rewriting (2.8), we obtain that the reduced system on \(\mathbf {C}_{0}\) is described by
$$ \dot{z}(t)=i\omega_{0}\widetilde{\tau}z(t)+g(z, \overline{z}), $$
(2.9)
where
$$ g(z,\overline{z})=g_{20}(\theta)z^{2}/{2}+g_{11}( \theta)z\overline{z} +g_{02}(\theta)\overline{z}^{2}/{2}+g_{21}( \theta)z^{2}\overline {z}/{2}+\cdots. $$
(2.10)
We will mainly discuss equation (2.9) in the following part.

Step 4. Obtain the values of \(g_{20}\) , \(g_{11}\) , \(g_{02}\) , \(g_{21}\) in ( 2.10 ).

In this part, we calculate the coefficients \(W_{20}(\theta)\), \(W_{11}(\theta)\), \(W_{02}(\theta)\), … and substitute them in (2.8) to get the reduced system (2.9) on \(\mathbf{C}_{0}\).

It follows from (2.6) that
$$ \begin{aligned}[b] u_{t}(\theta)&=u(t+ \theta)=W(t,\theta)+2\operatorname{Re}\bigl\{ z(t),q(\theta)\bigr\} \\ &=W_{20}(\theta)z^{2}/{2}+W_{11}(\theta)z \overline{z}+W_{02}(\theta )\overline{z}^{2}/{2} +(1, \alpha)^{T}e^{i\omega_{0}\widetilde{\tau}\theta}z \\ &\quad{}+(1,\overline{\alpha})^{T}e^{-i\omega_{0}\widetilde{\tau}\theta }\overline{z}+ \cdots. \end{aligned} $$
And we have
$$ \begin{gathered} u_{1}(t)=z+ \overline{z}+W^{(1)}(t,0),\qquad u_{2}(t)=z\alpha+\overline{z} \overline{\alpha}+W^{(2)}(t,0), \\ u_{1}(t-\widetilde{\tau})=ze^{-i\omega_{0}\widetilde{\tau}}+\overline {z}e^{i\omega_{0}\widetilde{\tau}}+W^{(1)}(-\widetilde{\tau}), \\ u_{2}(t-\widetilde{\tau})=z\alpha e^{-i\omega_{0}\widetilde{\tau }}+\overline{z} \overline{\alpha}e^{i\omega_{0}\widetilde{\tau }}+W^{(2)}(-\widetilde{\tau}), \\ u_{2}(t-\widetilde{\tau}_{2})=z\alpha e^{-i\omega_{0}\widetilde{\tau}_{2}}+ \overline{z} \overline{\alpha}e^{i\omega_{0}\widetilde{\tau }_{2}}+W^{(2)}(-\widetilde{ \tau}_{2}), \\ u_{1}(t-\widetilde{\tau}_{3})=ze^{-i\omega_{0}\widetilde{\tau }_{3}}+ \overline{z}e^{i\omega_{0}\widetilde{\tau }_{3}}+W^{(1)}(-\widetilde{\tau}_{3}), \\ u_{2}(t-\widetilde{\tau}_{3})=z\alpha e^{-i\omega_{0}\widetilde{\tau}_{3}}+ \overline{z} \overline{\alpha}e^{i\omega_{0}\widetilde{\tau }_{3}}+W^{(2)}(-\widetilde{ \tau}_{3}). \end{gathered} $$
(2.11)
It follows that together with \(F(\mu,\phi)\) we get
$$ \begin{gathered} f(0,u_{t}) =\widetilde{\tau} \begin{pmatrix}-a_{1}u_{1}(t)u_{1}(t-\widetilde{\tau })-b_{1}u_{1}(t)u^{2}_{2}(t-\widetilde{\tau}_{2})-2b_{1}y^{\ast }_{2}u_{1}(t)u_{2}(t-\widetilde{\tau}_{2})-b_{1}(y^{\ast }_{1}+c_{1})u^{2}_{2}(t-\widetilde{\tau}_{2}) \\-a_{2}u_{2}(t)u_{2}(t-\widetilde{\tau })+b_{2}u_{2}(t)u^{1}_{2}(t-\widetilde{\tau}_{3})+2b_{2}y^{\ast }_{2}u_{2}(t)u_{1}(t-\widetilde{\tau}_{3})+b_{2}(y^{\ast }_{2}+c_{1})u^{2}_{1}(t-\widetilde{\tau}_{3}) \end{pmatrix} .\end{gathered} $$
(2.12)
Substituting (2.11) into (2.12), then this substitution into (2.8), and comparing the coefficients with (2.10), we obtain
$$ \begin{aligned} g_{20} &=2\widetilde{\tau} \overline{G} \bigl\{ \overline{\beta} \bigl[-a_{1}e^{-i\omega_{0}\widetilde{\tau}}-2b_{1}y^{\ast}_{2}{ \alpha }e^{-i\omega_{0}\widetilde{\tau_{2}}}-b_{1}\bigl(y^{\ast}_{1}+c_{1} \bigr){\alpha }^{2}e^{-2i\omega_{0}\widetilde{\tau_{2}}} \bigr] \\ &\quad{}-a_{2}{\alpha}^{2}e^{-i\omega_{0}\widetilde{\tau}}+2b_{2}y^{\ast }_{2}{ \alpha}e^{-i\omega_{0}\widetilde{\tau_{3}}}+b_{2}\bigl(y^{\ast }_{2}+c_{1} \bigr)e^{-2i\omega_{0}\widetilde{\tau}} \bigr\} , \\ g_{11} &=\widetilde{\tau}\overline{G} \bigl\{ \overline{\beta} \bigl[-a_{1}\bigl(e^{i\omega _{0}\widetilde{\tau}} +e^{-i\omega_{0}\widetilde{\tau}} \bigr)-2b_{1}y^{\ast}_{2}\bigl(\overline{\alpha} e^{i\omega_{0}\widetilde{\tau}}+\alpha e^{-i\omega_{0}\widetilde{\tau }}\bigr)-2b_{1} \bigl(y^{\ast}_{1}+c_{1}\bigr)\alpha\overline{\alpha} \bigr] \\ &\quad{}-a_{2}\alpha\overline{\alpha}\bigl(e^{i\omega_{0}\widetilde{\tau }}+e^{-i\omega_{0}\widetilde{\tau}} \bigr) +2b_{2}y^{\ast}_{2}\bigl(\alpha e^{i\omega_{0}\widetilde{\tau_{3}}}+\overline {\alpha} e^{-i\omega_{0}\widetilde{\tau_{3}}}\bigr)+2b_{2} \bigl(y^{\ast }_{2}+c_{1}\bigr) \bigr\} , \\ g_{02} &=2\widetilde{\tau}\overline{G} \bigl\{ \overline{\beta} \bigl[-a_{1}e^{i\omega_{0}\widetilde{\tau}}-2b_{1}y^{\ast}_{2} \overline {\alpha}e^{i\omega_{0}\widetilde{\tau}}- b_{1}\bigl(y^{\ast}_{1}+c_{1} \bigr)\overline{\alpha}^{2}e^{2i\omega_{0}\widetilde {\tau}} \bigr] -a_{2} \overline{\alpha}^{2}e^{i\omega_{0}\widetilde{\tau}} \\ &\quad{}+2b_{2}y^{\ast}_{2}\overline{ \alpha}e^{i\omega_{0}\widetilde {\tau}}+b_{2}\bigl(y^{\ast}_{2}+c_{1} \bigr) \bigr\} , \\ g_{21} &=\widetilde{\tau}\overline{G} \bigl\{ \overline{\beta} \bigl[-a_{1} \bigl(W^{(1)}_{11}(-\widetilde{ \tau})+W^{(1)}_{20}(-\widetilde{\tau})/{2} +e^{-i\omega_{0}\widetilde{\tau}}W^{(1)}_{11}(0) +e^{i\omega_{0}\widetilde{\tau}}W^{(1)}_{20}(0)/{2} \bigr) \\ &\quad{}-b_{1}\bigl(2\alpha\overline{\alpha}+\overline{ \alpha}^{2}e^{2i\omega _{0}\widetilde{\tau_{{2}}}}\bigr) -2b_{1}y^{\ast}_{2} \bigl(W^{(2)}_{11}(-\widetilde{\tau })+W^{(2)}_{20}(- \widetilde{\tau})/{2}+\alpha e^{-i\omega_{0}\widetilde {\tau}} W^{(1)}_{11}(0) \\ &\quad{}+\overline{\alpha}e^{i\omega_{0}\widetilde{\tau }}W^{(1)}_{20}(0)/{2} \bigr) -b_{1}\bigl(y^{\ast}_{1}+c_{1}\bigr) \bigl(\alpha e^{-i\omega_{0}\widetilde{\tau }}W^{(2)}_{11}(-\widetilde{\tau}) + \overline{\alpha}e^{i\omega_{0}\widetilde{\tau }}W^{(2)}_{20}(-\widetilde{ \tau})/{2} \bigr) \bigr] \\ &\quad{}-a_{2} \bigl(\alpha W^{(2)}_{11}(- \widetilde{\tau})+\overline {\alpha}W^{(2)}_{20}(-\widetilde{ \tau})/{2}+\alpha e^{-i\omega _{0}\widetilde{\tau}} W^{(2)}_{11}(0)+\overline{ \alpha}e^{i\omega _{0}\widetilde{\tau}}W^{(2)}_{20}(0)/{2} \bigr) \\ &\quad{}+b_{2}\bigl(2\alpha+e^{-2i\omega_{0}\widetilde{\tau}}\bigr)+2b_{2}y^{\ast }_{2} \bigl[\alpha W^{(1)}_{11}(-\widetilde{\tau })+W^{(1)}_{20}(-\widetilde{\tau})/2+e^{-i\omega_{0}\widetilde{\tau }}W^{(1)}_{11}(0) \\ &\quad{}+e^{i\omega_{0}\widetilde{\tau}}W^{(1)}_{20}(0) \bigr] +b_{2}\bigl(y^{\ast}_{2}+c_{1}\bigr) \bigl[2e^{-i\omega_{0}\widetilde{\tau }}W^{(1)}_{11}(-\widetilde{ \tau_{3}}) +e^{i\omega_{0}\widetilde{\tau}}W^{(1)}_{20}(- \widetilde{\tau_{3}}) \bigr] \bigr\} . \end{aligned} $$
(2.13)
Since there are \(W_{20}(\theta)\) and \(W_{11}(\theta)\) in \(g_{21}\), we still need to compute them.
From (2.5) and (2.6), we have
$$ \begin{aligned} \dot{W}=\dot{u}_{t}- \dot{z}q-\dot{\overline{z}} \overline{q}&= \textstyle\begin{cases} AW-2\operatorname{Re}\{gq(\theta)\},&\theta\in[-1,0),\\ AW-2\operatorname {Re}\{gq(0)\}+f_{0},&\theta=0, \end{cases}\displaystyle \end{aligned} $$
(2.14)
where
$$ \begin{aligned} f_{0}=f_{z^{2}}z^{2}/{2}+f_{z \overline{z}}z \overline{z}+f_{\overline {z}^{2}}\overline{z}^{2}/{2}+f_{z^{2}\overline{z}}z^{2} \overline {z}/{2}\cdots. \end{aligned} $$
On the other hand, near the origin, on the centre manifold \(\mathbf {C}_{0}\), according to (2.7), we obtain
$$ \begin{aligned}[b] \dot{W}&=W_{z}\dot{z}+W_{\overline{z}}\dot{ \overline{z}} = \bigl[W_{20}(\theta)z+W_{11}(\theta) \overline{z} \bigr]\dot{z}+ \bigl[W_{11}(\theta)z+W_{02}( \theta)\overline{z} \bigr]\dot{\overline {z}} \\ &= \bigl[W_{20}(\theta)z+W_{11}(\theta)\overline{z} \bigr] \bigl(i\omega _{0}z+g(z,\overline{z}) \bigr) \\ &\quad{}+ \bigl[W_{11}(\theta)z+W_{02}(\theta)\overline{z} \bigr] \bigl(\overline{g}(z,\overline{z})-i\omega_{0}\overline{z} \bigr)+\cdots .\end{aligned} $$
(2.15)
Substituting (2.7) into the right-hand side of (2.14), equating terms of \(\frac{z^{2}}{2}\) and zz̅ of (2.14) with (2.15), we obtain
$$\begin{aligned}& (2i\omega_{0}I-A)W_{20}(\theta)= \textstyle\begin{cases} -g_{20}q(\theta)-\overline{g}_{02}\overline{q}(\theta),&\theta\in [-\widetilde{\tau},0),\\ -g_{20}q(0)-\overline{g}_{02}\overline {q}(0)+f_{z^{2}},&\theta=0, \end{cases}\displaystyle \end{aligned}$$
(2.16)
$$\begin{aligned}& -AW_{11}(\theta)= \textstyle\begin{cases} -g_{11}q(\theta)-\overline{g}_{11}\overline{q}(\theta),&\theta\in [-\widetilde{\tau},0),\\ -g_{11}q(0)-\overline{g}_{11}\overline{q}(0)+f_{z \overline{z}},& \theta=0. \end{cases}\displaystyle \end{aligned}$$
(2.17)
According to the definition of A and from (2.16), (2.17) for \(\theta\in[-\widetilde{\tau},0)\), we get
$$\begin{gathered} \dot{W}_{20}(\theta)=2i\omega_{0} W_{20}( \theta)+g_{20}q(\theta )+\overline{g}_{02}\overline{q}( \theta), \\ \dot{W}_{11}(\theta)=g_{11}q(\theta)+\overline{g}_{11} \overline {q}(\theta).\end{gathered} $$
Solving for \({W}_{20}(\theta)\) and \({W}_{11}(\theta)\), we obtain
$$\begin{aligned}& {W}_{20}(\theta)=ig_{20}/{\omega_{0}}\cdot q(0)e^{i\omega_{0}\theta} +i\overline{g}_{02}/{3\omega_{0}}\cdot \overline{q}(0)e^{-i\omega _{0}\theta} +E_{1}e^{2i\omega_{0}\theta}, \end{aligned}$$
(2.18)
$$\begin{aligned}& {W}_{20}(\theta)=-ig_{11}/{\omega_{0}}\cdot q(0)e^{i\omega_{0}\theta} +i\overline{g}_{11}/{\omega_{0}}\cdot \overline{q}(0)e^{-i\omega _{0}\theta} +E_{2}, \end{aligned}$$
(2.19)
where \(E_{1}=(E^{(1)}_{1},E^{(2)}_{1})^{T}\in R^{2}\) and \(E_{2}=(E^{(1)}_{2},E^{(2)}_{2})^{T}\in R^{2}\) are constant vectors.
In what follows we shall seek appropriate \(E_{1}\) and \(E_{2}\) in (2.18) and (2.19), respectively. According to the definition of A and (2.16), (2.17) for \(\theta=0\), we have
$$\begin{aligned}& \int_{-\widetilde{\tau}}^{0}d\eta(\theta){W}_{20}( \theta)=2i\omega_{0} W_{20}(0)+g_{20}q(0)+ \overline{g}_{02}\overline{q}(0)-f_{z^{2}}, \end{aligned}$$
(2.20)
$$\begin{aligned}& \int_{-\widetilde{\tau}}^{0}d\eta(\theta){W}_{11}(\theta )=g_{11}q(0)+\overline{g}_{11}\overline{q}(0)-f_{z \overline{z}}, \end{aligned}$$
(2.21)
where \(\eta(\theta)=\eta(0,\theta)\) and
$$\begin{gathered} f_{z^{2}}= \begin{pmatrix}-a_{1}e^{-i\omega_{0}\widetilde{\tau}}-2b_{1}y^{\ast }_{2}{\alpha}e^{-i\omega_{0}\widetilde{\tau_{2}}}-b_{1}(y^{\ast }_{1}+c_{1}){\alpha}^{2}e^{-2i\omega_{0}\widetilde{\tau_{2}}}\\ -a_{2}{\alpha}^{2}e^{-i\omega_{0}\widetilde{\tau}}+2b_{2}y^{\ast }_{2}{\alpha}e^{-i\omega_{0}\widetilde{\tau_{3}}}+b_{2}(y^{\ast }_{2}+c_{1})e^{-2i\omega_{0}\widetilde{\tau}} \end{pmatrix} , \\ f_{z\overline{z}}= \begin{pmatrix} -a_{1}(e^{i\omega_{0}\widetilde{\tau}} +e^{-i\omega_{0}\widetilde{\tau}})-2b_{1}y^{\ast}_{2}(\overline{\alpha} e^{i\omega_{0}\widetilde{\tau}}+\alpha e^{-i\omega_{0}\widetilde{\tau }})-2b_{1}(y^{\ast}_{1}+c_{1})\alpha\overline{\alpha}\\ -a_{2}\alpha\overline{\alpha}(e^{i\omega_{0}\widetilde{\tau }}+e^{-i\omega_{0}\widetilde{\tau}}) +2b_{2}y^{\ast}_{2}(\alpha e^{i\omega_{0}\widetilde{\tau_{3}}}+\overline {\alpha} e^{-i\omega_{0}\widetilde{\tau_{3}}})+2b_{2}(y^{\ast}_{2}+c_{1}) \end{pmatrix} . \end{gathered} $$
Substituting (2.18) into (2.20), we obtain
$$ \biggl(2i\omega_{0} I- \int_{-\widetilde{\tau}}^{0}e^{2i\omega_{0}\theta}\, d\eta(\theta) \biggr) E_{1} =f_{z^{2}}, $$
that is
$$ \begin{pmatrix}2i\omega_{0}+a_{1}(y^{\ast}_{1}+c_{1})e^{-i\omega _{0}\widetilde{\tau}} &2b_{1}y^{\ast}_{2}(y^{\ast}_{1}+c_{1})e^{-i\omega_{0}\widetilde{\tau _{2}}}\\ -2b_{2}y^{\ast}_{2}(y^{\ast}_{2}+c_{1})e^{-i\omega_{0}\widetilde{\tau_{3}}} &2i\omega_{0}+a_{2}(y^{\ast}_{2}+c_{1})e^{-i\omega_{0}\widetilde{\tau}} \end{pmatrix} E_{1}=f_{z^{2}}. $$
(2.22)
Similarly, substituting (2.19) into (2.21), we get
$$ \int_{-\widetilde{\tau}}^{0}d\eta(\theta)E_{2}=f_{z\overline {z}}, $$
that is
$$ \begin{pmatrix}a_{1}(y^{\ast}_{1}+c_{1})e^{-i\omega_{0}\widetilde{\tau}} &2b_{1}y^{\ast}_{2}(y^{\ast}_{1}+c_{1})e^{-i\omega_{0}\widetilde{\tau _{2}}}\\ -2b_{2}y^{\ast}_{2}(y^{\ast}_{2}+c_{1})e^{-i\omega_{0}\widetilde{\tau_{3}}} &a_{2}(y^{\ast}_{2}+c_{1})e^{-i\omega_{0}\widetilde{\tau}} \end{pmatrix} E_{2}=f_{z\overline{z}}. $$
(2.23)
We have obtained the values of \(E_{1}\) and \(E_{2}\) as (2.22) and (2.23) and, ultimately, the reduced system (2.9).

Step 5. Obtain the key values \(\mu_{2}\) , \(\beta_{2}\) , \(T_{2}\) to determine the property of the Hopf bifurcation.

As with the calculation of the ODE Hopf bifurcation parameter and as in [28], according to the analysis above and the expressions of \(g_{20}\), \(g_{11}\), \(g_{02}\) and \(g_{21}\), we can compute the following values:
$$ \begin{gathered}c_{1}(0)=i/{2\omega_{0}\widetilde{\tau}} \bigl(g_{11}g_{20}-2 \vert g_{11} \vert ^{2}- \vert g_{02} \vert ^{2}/{3} \bigr)+g_{21}/{2}, \\ \mu_{2}=-\operatorname{Re}\bigl\{ c_{1}(0)\bigr\} /{ \alpha'(\widetilde{\tau})}, \\ \beta_{2}=2\operatorname{Re}\bigl\{ c_{1}(0)\bigr\} , \\ T_{2}=- \bigl[\operatorname{Im}\bigl\{ c_{1}(0)\bigr\} + \mu_{2}\omega'(\widetilde{\tau }) \bigr]/{ \omega_{0}},\end{gathered} $$
(2.24)
where \(\lambda(\tau)=\alpha(\tau)\pm i\omega(\tau)\) is the characteristic root of (2.3), which is a continuous differentiable family. \(\alpha'(\widetilde{\tau })\) and \(\omega'(\widetilde{\tau})\) can be obtained by taking the derivative of the two sides of (2.3) and taking values at τ̃.

These formulae give a description of the Hopf bifurcation periodic solution of system (1.3) at \(\tau=\tau^{j}\) (\(j=0,1,2,\ldots\)) on the centre manifold. Thus, we can obtain the following results according to the discussion about properties of Hopf bifurcating periodic solutions of dynamical system in [30].

Proposition 3

Assume that\((\mathrm{H}_{1})\)and\((\mathrm{H}_{2})\)hold. Then
  1. (i)

    \(\mu_{2}\)determines the direction of the Hopf bifurcation. If\(\mu_{2}>0\) (\(\mu_{2}<0\)), then the Hopf bifurcation is supercritical (subcritical);

     
  2. (ii)

    \(\beta_{2}\)determines the stability of the bifurcating periodic solutions. If\(\beta_{2}<0\) (\(\beta_{2}>0\)), then bifurcating periodic solution is stable (unstable);

     
  3. (iii)

    \(T_{2}\)determines the period of the bifurcating periodic solutions. If\(T_{2}>0\) (\(T_{2}<0\)), then periods of the periodic solutions increase (decrease).

     

3 Numerical simulations and conclusions

In this section, we shall give some numerical simulations to support the theoretical analysis discussed in the previous section. We also present our conclusions and limitations of the analysis.

Firstly, we study the following specific model:
$$ \textstyle\begin{cases} \dot{y_{1}}(t)=(y_{1} (t)+1) (0.6-0.2y_{1}(t-\tau )-0.4y_{2}^{2}(t-\tau/3) ),\\ \dot{y_{2}}(t)=(y_{2} (t)+1) (0.4-0.6y_{2}(t-\tau )+0.2y_{1}^{2}(t-2\tau/3) ), \end{cases} $$
(3.1)
with initial values \((y_{1}(t),y_{2}(t))=(0.2,0.2)\), which satisfies \((\mathrm {H}_{1})\), \((\mathrm{H}_{2})\). By computing, \(E^{\ast}=(1,1)\) and by [30], \(h(z)=z^{4}-2.56z^{3}+0.3584z^{2}-2.1627z-0.3244=0\), which has only one positive root \(z=2.7341\), get \(\omega=1.6535\), \(\tau ^{0}=0.5847\). By Proposition 2, we find that \(E^{\ast}\) is asymptotically stable when \(0\leq\tau<\tau^{0}=0.5847\), as Figs. 1(a)–(d) illustrate, and \(E^{\ast}\) is unstable when \(\tau >\tau^{0}=0.5847\), as shown in Figs. 2(a)–(d) which are generated by dde23 [31], a Matlab tool that integrates DDEs.
Open image in new windowFigure 1
Figure 1

The trajectory graph of system (3.1) with \(\tau _{1}=0.56\), \(\tau_{2}=0.56/3\), \(\tau_{3}=1.12/3\) in (a) the t–\(y_{1}\) plane and in (b) the t–\(y_{2}\) plane. The phase graph of system (3.1) with \(\tau_{1}=0.56\), \(\tau_{2}=0.56/3\), \(\tau_{3}=1.12/3\) in (c) the \(y_{1}\)–\(y_{2}\) plane and in (d) the t–\(y_{1}\)–\(y_{2}\) plane

Open image in new windowFigure 2
Figure 2

The trajectory graph of system (3.1) with \(\tau _{1}=0.6\), \(\tau_{2}=0.6/3\), \(\tau_{3}=1.2/3\) in (a) the t–\(y_{1}\) plane and in (b) the t–\(y_{2}\) plane. The phase graph of system (3.1) with \(\tau_{1}=0.6\), \(\tau_{2}=0.6/3\), \(\tau_{3}=1.2/3\) in (c) the \(y_{1}\)–\(y_{2}\) plane and in (d) the t–\(y_{1}\)–\(y_{2}\) plane

According to the above numerical simulations and from an economic viewpoint, we conclude that a critical duration time of the two enterprise outputs exists. When the duration time is less than the critical delay, the cooperation between the two enterprises is very effective; though the competition between them exists, they can coexist and have developed over a long time. Alternatively, when the competition between the two enterprises is much stronger than their effective cooperation, the result will ultimately force a merger or a closure by one of the enterprises. Therefore, entrepreneurs must have a shrewd understanding of market forces and economic laws in order to maintain a viable and successful enterprise.

However, here we have only considered the problem of a local Hopf bifurcation and do not give the conditions ensuring the existence of a global Hopf bifurcation for large values of the delay. Furthermore, we do not consider systems with a spatial variable, which is the diffusive model subject to a suitable boundary condition. We intend to make the comparison between the two models, and find what is the influence on the dynamical behaviour with different delays and diffusive terms [32, 33, 34, 35, 36, 37, 38], and then illustrate with the theoretical predictions. Lastly, our problem is only restricted to the theoretical analysis of such economical phenomena. It may be timely and necessary to make field investigations and experimental studies for real-world scenarios, and this is left for further study.

Notes

Acknowledgements

This research receives the grant from China Scholarship Council (CSC) and was supported by the Natural Science Foundation of Anhui Province (No. 1608085QF151, No. 1608085QF145). The authors would like to express their deep thanks to the referee for valuable suggestions for the revision and improvement of the manuscript.

Availability of data and materials

Not applicable.

Authors’ information

Xin Zhang’s research interests are delay differential equations, dynamical systems, bifurcation theory and mathematical biology. The research interests of Zizhen Zhang mainly are bifurcation theory and population dynamics. Matthew J. Wade’s research topics are numerical analysis and mathematical modelling.

Authors’ contributions

The main idea of this paper was proposed by XZ and she prepared the manuscript initially and performed all the steps of the proofs in this research. All authors read and approved the final manuscript.

Funding

This research is supported by the Natural Science Foundation of Anhui Province (No. 1608085QF151, No. 1608085QF145).

Ethics approval and consent to participate

Not applicable.

Competing interests

All authors have declared that no conflict of interest exists in the submission of this manuscript, and the manuscript is approved by the authors for publication. We would like to declare on behalf of the authors that the work described was original research that has not been published previously, and not under consideration for publication elsewhere, in whole or in part. The authors listed have approved the manuscript that is enclosed.

Consent for publication

Not applicable.

References

  1. 1.
    Hale, J.: Theory of Functional Differential Equations, 2nd edn. Applied Mathematical Sciences, vol. 3 Springer, Berlin (1977) CrossRefzbMATHGoogle Scholar
  2. 2.
    Kuang, Y.: Delay Differential Equations with Applications in Population Dynamics. Mathematics in Science and Engineering, vol. 191. Academic Press, Boston (1993) zbMATHGoogle Scholar
  3. 3.
    Rezounenko, A.V., Wu, J.: A non-local PDE model for population dynamics with state-selective delay: local theory and global attractors. J. Comput. Appl. Math. 190(1–2), 99–113 (2006) MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Cooke, K., Van den Driessche, P., Zou, X.: Interaction of maturation delay and nonlinear birth in population and epidemic models. J. Math. Biol. 39(4), 332–352 (1999) MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Hill, D.C., Shafer, D.S.: Asymptotics and stability of the delayed Duffing equation. J. Differ. Equ. 265(1), 33–68 (2018) MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    Liu, X., Zhang, T.: Bogdanov–Takens and triple zero bifurcations of coupled Van der Pol–Duffing oscillators with multiple delays. Int. J. Bifurc. Chaos 27(9), Article ID 1750133 (2017) MathSciNetCrossRefzbMATHGoogle Scholar
  7. 7.
    Alvarez-Vázquez, L.J., Fernández, F.J., Muñoz-Sola, R.: Analysis of a multistate control problem related to food technology. J. Differ. Equ. 245(1), 130–153 (2008) MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Antman, S., Marsden, J., Sirovich, L.: Surveys and Tutorials in the Applied Mathematical Sciences. Springer, Berlin (2007) Google Scholar
  9. 9.
    Smith, H.: An Introduction to Delay Differential Equations with Applications to the Life Sciences. Texts in Applied Mathematics, vol. 57. Springer, New York (2011) zbMATHGoogle Scholar
  10. 10.
    Zhang, W., Li, J., Chen, M.: Global exponential stability and existence of periodic solutions for delayed reaction–diffusion BAM neural networks with Dirichlet boundary conditions. Bound. Value Probl. 2013(1), Article ID 105 (2013) MathSciNetCrossRefzbMATHGoogle Scholar
  11. 11.
    Song, Y., Zhang, T., Tadé, M.O.: Stability switches, Hopf bifurcations, and spatio-temporal patterns in a delayed neural model with bidirectional coupling. J. Nonlinear Sci. 19(6), Article ID 597 (2009) MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    Song, Y., Tade, M.O., Zhang, T.: Bifurcation analysis and spatio-temporal patterns of nonlinear oscillations in a delayed neural network with unidirectional coupling. Nonlinearity 22(5), 975 (2009) MathSciNetCrossRefzbMATHGoogle Scholar
  13. 13.
    Bianca, C., Guerrini, L., Riposo, J.: A delayed mathematical model for the acute in ammatory response to infection. Appl. Math. Inf. Sci. 9(6), 2775–2782 (2015) Google Scholar
  14. 14.
    Bianca, C., Guerrini, L.: Existence of limit cycles in the Solow model with delayed-logistic population growth. Sci. World J. 2014, Article ID 207806 (2014) CrossRefGoogle Scholar
  15. 15.
    Cai, Y., Zhang, C.: Hopf–Pitchfork bifurcation of coupled Van der Pol oscillator with delay. Nonlinear Anal., Model. Control 22(5), 598–613 (2017) MathSciNetCrossRefGoogle Scholar
  16. 16.
    Ozturk, O., Akin, E.: On nonoscillatory solutions of two dimensional nonlinear delay dynamical systems. Opusc. Math. 36(5), 651–669 (2016) MathSciNetCrossRefzbMATHGoogle Scholar
  17. 17.
    Bianca, C., Pennisi, M., Motta, S., Ragusa, M.A.: Immune system network and cancer vaccine. In: Numerical Analysis and Applied Mathematics. ICNAAM 2011: International Conference on Numerical Analysis and Applied Mathematics. AIP Conference Proceedings, vol. 1389, pp. 945–948. AIP, New York (2011) Google Scholar
  18. 18.
    Bianca, C., Pappalardo, F., Pennisi, M., Ragusa, M.: Persistence analysis in a Kolmogorov-type model for cancer-immune system competition. In: 11th International Conference of Numerical Analysis and Applied Mathematics 2013: ICNAAM 2013. AIP Conference Proceedings, vol. 1558, pp. 1797–1800. AIP, New York (2013) Google Scholar
  19. 19.
    Liu, W., Jiang, Y.: Bifurcation of a delayed Gause predator–prey model with Michaelis–Menten type harvesting. J. Theor. Biol. 438, 116–132 (2018) MathSciNetCrossRefzbMATHGoogle Scholar
  20. 20.
    Huo, H.-F., Li, W.-T.: Periodic solution of a delayed predator–prey system with Michaelis–Menten type functional response. J. Comput. Appl. Math. 166(2), 453–463 (2004).  https://doi.org/10.1016/j.cam.2003.08.042 MathSciNetCrossRefzbMATHGoogle Scholar
  21. 21.
    Bairagi, N., Jana, D.: On the stability and Hopf bifurcation of a delay-induced predator–prey system with habitat complexity. Appl. Math. Model. 35(7), 3255–3267 (2011).  https://doi.org/10.1016/j.apm.2011.01.025 MathSciNetCrossRefzbMATHGoogle Scholar
  22. 22.
    Song, Y., Wei, J.: Local Hopf bifurcation and global periodic solutions in a delayed predator–prey system. J. Math. Anal. Appl. 301(1), 1–21 (2005).  https://doi.org/10.1016/j.jmaa.2004.06.056 MathSciNetCrossRefzbMATHGoogle Scholar
  23. 23.
    Liao, M., Xu, C., Tang, X.: Stability and Hopf bifurcation for a competition and cooperation model of two enterprises with delay. Commun. Nonlinear Sci. Numer. Simul. 19(10), 3845–3856 (2014).  https://doi.org/10.1016/j.cnsns.2014.02.031 MathSciNetCrossRefGoogle Scholar
  24. 24.
    Li, L., Zhang, C.-H., Yan, X.-P.: Stability and Hopf bifurcation analysis for a two-enterprise interaction model with delays. Commun. Nonlinear Sci. Numer. Simul. 30(1–3), 70–83 (2016) MathSciNetCrossRefGoogle Scholar
  25. 25.
    Du, Z., Xu, D.: Traveling wave solution for a reaction–diffusion competitive–cooperative system with delays. Bound. Value Probl. 2016(1), Article ID 46 (2016) MathSciNetCrossRefzbMATHGoogle Scholar
  26. 26.
    Xu, C.: Periodic behavior of competition and corporation dynamical model of two enterprises on time scales. J. Quant. Econ. 29(2), 1–4 (2012) MathSciNetGoogle Scholar
  27. 27.
    Liao, M., Xu, C., Tang, X.: Dynamical behaviors for a competition and cooperation model of enterprises with two delays. Nonlinear Dyn. 75(1–2), 257–266 (2014).  https://doi.org/10.1007/s11071-013-1063-9 MathSciNetCrossRefzbMATHGoogle Scholar
  28. 28.
    Hassard, B.D., Kazarinoff, N.D., Wan, Y.H.: Theory and Applications of Hopf Bifurcation. London Mathematical Society Lecture Note Series, vol. 41. Cambridge University Press, Cambridge (1981) zbMATHGoogle Scholar
  29. 29.
    Wiggins, S.: Introduction to Applied Nonlinear Dynamical Systems and Chaos, vol. 2. Springer, New York (2003) zbMATHGoogle Scholar
  30. 30.
    Hu, G.-P., Li, W.-T., Yan, X.-P.: Hopf bifurcations in a predator–prey system with multiple delays. Chaos Solitons Fractals 42(2), 1273–1285 (2009).  https://doi.org/10.1016/j.chaos.2009.03.075 MathSciNetCrossRefzbMATHGoogle Scholar
  31. 31.
    Shampine, L.F., Thompson, S., Kierzenka, J.: Solving delay differential equations with dde23 (2000). http://www.runet.edu/~thompson/webddes/tutorial.pdf
  32. 32.
    Song, Y., Zhang, T., Peng, Y.: Turing–Hopf bifurcation in the reaction–diffusion equations and its applications. Commun. Nonlinear Sci. Numer. Simul. 33, 229–258 (2016) MathSciNetCrossRefGoogle Scholar
  33. 33.
    Cao, X., Song, Y., Zhang, T.: Hopf bifurcation and delay-induced Turing instability in a diffusive lac operon model. Int. J. Bifurc. Chaos 26(10), Article ID 1650167 (2016) MathSciNetCrossRefzbMATHGoogle Scholar
  34. 34.
    Colli, P., Gilardi, G., Sprekels, J.: A boundary control problem for the pure Cahn–Hilliard equation with dynamic boundary conditions. Adv. Nonlinear Anal. 4(4), 311–325 (2015) MathSciNetzbMATHGoogle Scholar
  35. 35.
    Ghergu, M., Radulescu, V.: Singular Elliptic Problems: Bifurcation and Asymptotic Analysis. Oxford Lecture Series in Mathematics and Its Applications, vol. 37. Clarendon, Oxford (2008) zbMATHGoogle Scholar
  36. 36.
    Wang, G.-Q., Cheng, S.S.: Bifurcation in a nonlinear steady state system. Opusc. Math. 30, 349–360 (2010) MathSciNetCrossRefzbMATHGoogle Scholar
  37. 37.
    Ghergu, M., Radulescu, V.: Nonlinear PDEs: Mathematical Models in Biology, Chemistry and Population Genetics. Springer, Berlin (2011) zbMATHGoogle Scholar
  38. 38.
    Squassina, M., Watanabe, T.: Uniqueness of limit flow for a class of quasi-linear parabolic equations. Adv. Nonlinear Anal. 6(2), 243–276 (2017) MathSciNetzbMATHGoogle Scholar

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Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  1. 1.School of MathematicsJilin UniversityChangchunChina
  2. 2.School of Management Science and EngineeringAnhui University of Finance and EconomicsBengbuChina
  3. 3.School of EngineeringNewcastle UniversityNewcastle-upon-TyneUnited Kingdom
  4. 4.School of Mathematics and StatisticsMcMaster UniversityHamiltonCanada

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