Research Article Stability and Hopf Bifurcation in a Computer Virus Model with Multistate Antivirus

Abstract and Applied Analysis Volume, Article ID 84987, 6 pages doi:.55//84987 Research Article Stability and Hopf Bifurcation in a Computer Virus Model with Multistate Antivirus Tao Dong,, Xiaofeng Liao, and Huaqing Li State Key Laboratory of Power Transmission Equipment and System Security, College of Computer Science, Chongqing University, Chongqings 444, China College of Software and Engineering, Chongqing University of Posts and Telecommunications, Chongqing 465, China Correspondence should be addressed to Tao Dong, david 3@6.com Received 9 January ; Accepted 6 February Academic Editor: Muhammad Aslam Noor Copyright q Tao Dong et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. By considering that people may immunize their computers with countermeasures in susceptible state, exposed state and using anti-virus software may take a period of time, a computer virus model with time delay based on an SEIR model is proposed. We regard time delay as bifurcating parameter to study the dynamical behaviors which include local asymptotical stability and local Hopf bifurcation. By analyzing the associated characteristic equation, Hopf bifurcation occurs when time delay passes through a sequence of critical value. The linerized model and stability of the bifurcating periodic solutions are also derived by applying the normal form theory and the center manifold theorem. Finally, an illustrative example is also given to support the theoretical results.. Introduction As globalization and development of communication networks have made computers more and more present in our daily life, the threat of computer viruses also becomes an increasingly important issue of concern. In 3, a virus, called worm king, rapidly spread and attacked the global world, which results the network of the internet to be seriously congested and server to be paralyzed. In, the report of pestilence about computer virus in China revealed that more than 9% computers in China are infected computer virus. Computer viruses are small programs developed to damage the computer systems erasing data, stealing information. Their action throughout a network can be studied by using classical epidemiological models for disease propagation 6. In 7 9, based on SIR classical epidemic model, Mark had proposed the dynamical models for the computer

Abstract and Applied Analysis virus propagation, which provided estimations for temporal evolutions of infected nodes depending on network parameters. In 3, Richard and Mark propose a modified propagation model named SEIR susceptible-exposed-infected-recover model to simulate virus propagation. In 4, on this basis of the SIR model, Yao et al. proposed a SIDQV model with time delay which add a quarantine state to clean the virus. However, both above models assume the viruses are cleaned in the infective state. In fact, in addition to clean viruses in state I, people may immunize their computers with countermeasures in state S and state E in the real world. Moreover there may be a time lag when the node uses antivirus software to clean the virus. In this paper, in order to overcome the above-mentioned limitation, we present a new computer virus model with time delay which is depending on the SEIR model 5 ; time delay can be considered the period of the node uses antivirus software to clean the virus. This model provides an opportunity for us to study the behaviors of virus propagation in the presence of antivirus countermeasures, which are very important and desirable for understanding of the virus spread patterns, as well as for management and control of the spread. The remainder of this paper is organized as follows. In Section, the stability of trivial solutions and the existence of Hopf bifurcation are discussed. In Section 3, a formula for determining the direction of Hopf bifurcation and the stability of bifurcating periodic solutions will be given by using the normal form and center manifold theorem introduced by Hassard et al. in 6. InSection 4, numerical simulations aimed at justifying the theoretical analysis will be reported.. Mathematical Model Formulation Our model is based on the traditional SEIR model 7 9, 5, 7. The SEIR model has four states: susceptible, exposed infected but not yet infectious, infectious, and recovered. Our assumptions on the dynamical model are as follows. In the real world, in addition cleaning viruses in state I, people may immunize their computers with countermeasures in state S and state E after virus being cleaned, which may result in new state transition paths in comparison with SIR model: S-R: using countermeasure of real-time immunization, E-R: using real-time immunization after virus codes cleaning. In state S, when people install the antivirus software on their computer, we assume that their computer can be immunized at a unit time. 3 In state E, since the computer is infected by the virus, the antivirus software may use a period to search the document and clean the viruses. 4 Denote the period of time of killing viruses when users find that their computers are infected by viruses. 5 While the computer is installed the antivirus software, it will not be quarantine or replacement. On the basis of the above hypotheses 5, the dynamical model

Abstract and Applied Analysis 3 can be formulated by the following equations: ds t un βi t S t ρ SR μ S t, dt de t dt dr t dt βi t S t α μ E t ρ ER E t τ, di t dt αe t γ μ I t, ρ SR S t ρ ER E t τ γi t τ μr t,. where ρ SR describes the impact of implementing real-time immunization, ρ ER describes the impact of cleaning the virus and immunizing the nodes, and μ describes the impact of quarantine or replacement. α is the transition rate from E to I, andγ is the recovery rate from I to R. τ is the time delay that the node usees antivirus software to clean the virus. β is the transition rate from S to E. 3. Local Stability of the Equilibrium and Existence of Hopf Bifurcation We may see that the first three equations in. are independent of the fourth equation, and therefore, the fourth equation can be omitted without loss of generality. Hence, model. can be rewritten as ds t un βi t S t ρ SR μ S t, dt de t dt βi t S t α μ E t ρ ER E t τ, di t dt αe t γ μ I t. 3. For the convenience of description, we define the basic reproduction number of the infection as R μnβα ρsr μ α ρ ER μ. 3. γ μ Clearly, we have the following results with respect to the stable state of system 3.. Here, the proof is omitted see 7 for the details. Theorem 3.. If R <, system 3. has only the disease-free equilibrium E μn/ ρ SR μ,, and is globally asymptotically stable. If R >, E becomes unstable and there exists a unique positive equilibrium E ve,wheree ve μn/ ρ SR μ R, μn R /R α μ ρ ER,αE / γ μ. Furthermore, for any τ>, E is asymptotically stable if R < and unstable if R >. To investigate the qualitative properties of the positive equilibrium E with τ>, it is necessary to make the following assumption: H R >.

4 Abstract and Applied Analysis Under hypothesis H, the Jacobian matrix of the system 3. about E ve is given by a a J E ve a 3 a 4 a 7 e λτ a, 3.3 a 5 a 6 where a βi ρ SR μ, a βs, a 3 βi, a 4 α μ, a 5 α, a 6 γ μ, a 7 ρ ER. We can obtain the following characteristic equation: λ 3 b λ b λ b 3 e λτ b 4 λ b 5 λ b 6, 3.4 where b a a 4 a 6, b a a 6 a 4 a a 6 a a 5, b 3 a a 4 a 6 a a a 5 a a 3 a 5, b 4 a 7, b 5 a 7 a a 6, b 6 a a 6 a 7. 3.5 If iω ω > is a root of 3.4, then iω 3 b ω b iω b 3 e iωτ ω b 4 b 5 iω b 6. 3.6 Separating the real and imaginary parts of 3.6, we have b 5 ω sin ωτ b 6 b 4 ω cos ωτ b ω b 3, b 5 ω cos ωτ b 6 b 4 ω sin ωτ ω 3 b ω. 3.7 Adding up the squares of 3.7 yields ω 6 b b b 4 ω 4 b b b 3 b 4 b 6 b 5 ω b 3 b 6. 3.8 Letting z ω,c b b b 4, c b b b 3 b 4 b 6 b 5,c 3 b 3 b 6, then 3.8 becomes z 3 c z c z c 3. 3.9 Letting z /3 c c 3c, h z z 3 c z c z c 3, then we have the following results see 8 for details about the distributions of the positive roots of 3.9. Lemma 3. see 8. i If c 3 <, then 3.9 has at least one positive root. ii If c 3 and c 3c, then 3.9 has no positive root. iii If c 3 and c 3c >,then 3.9 has positive roots if and only if z > and h z.

Abstract and Applied Analysis 5 Suppose 3.9 has positive roots; without loss of generality, we assume that it has three positive roots defined by ω k z k, k,, 3. By 3.7, we have cos ω k τ b ω k b 3 b6 b 4 ω k b5 ωk ω k b b 5 ω k b 6 b 4 ω. 3. k Thus, denoting τ j k ω k arc cos b ω k b 3 b6 b 4 ω k b5 ωk ω k b b 5 ω k b 6 b 4 ω jπ, ω k k 3. where k,, 3; j,,..., then ±iω is a pair of purely imaginary roots of 3.4 with τ j k. Define } τ τ k min {τ, ω k ω k. 3. k,,3 Note that when τ, 3.4 becomes λ 3 b b 4 λ b b 5 λ b 3 b 6. 3.3 In addition, Routh-Hurwitz criterion 3 implies that, if the following condition holds, then all roots of 3.3 have negative real parts. H b b 4 >, b b 4 b b 5 b 3 b 6 >. Till now, we can employ a result from Ruan and Wei 3 to analyze 3.4, which is, for the convenience of the reader, stated as follows. Lemma 3.3 see 3. Consider the exponential polynomial P λ, e λτ,...,e λτ m [ λ n p λn p n λ p n p λn p n λ p n [ ] ] e λτ p m λ n p m n λ p m n e λτ m, 3.4 where τ i i,,...,m and p i j j,,...,m are constants. As τ,τ,...,τ m vary, the sum of the order of the zeros of P λ, e λτ,...,e λτ m on the open right half plane can change only if a zero appears on or crosses the imaginary axis. Using Lemmas 3. and 3.3 we can easily obtain the following results on the distribution of roots of the transcendental 3.4. Lemma 3.4.. If c 3 > and c 3c, then all roots with positive real parts of 3.4 have the same sum as those of the polynomial 3.3 for all τ. 3. If either c 3 < or c 3 and c 3c >, z >, h z, then all roots with positive real parts of 3.4 have the same sum as those of the polynomial 3.3 for τ,τ.

6 Abstract and Applied Analysis Lemma 3.5. If 3w 4 k c w k c /, then the following transversality condition holds: { { dλ }} sgn Re / when τ τ. 3.5 dτ Proof. Differentiating 3.4 with respect to τ yields [ 3λ b λ b b 4 λ b 5 τ b 4 λ b 5 λ b 6 e λτ] dλ dτ λ b 4 λ b 5 λ b 6 e λτ. 3.6 For the sake of simplicity, denoting ω and τ by ω, τ respectively, then dλ 3λ b λ b dτ λ b 4 λ b 5 λ b 6 e b 4 λ b 5 λτ λ b 4 λ b 5 λ b 6 τ λ λ 3 b λ b 3 λ λ 3 b λ b λ b 3 b 4 λ b 6 λ b 4 λ b 5 λ b 6 τ λ iω 3 b ω b 3 ω b 3 b ω i ω 3 b ω b 4 ω b 6 ω b 6 b 3 ω b 4 iω τ iω. 3.7 Then we get Re { dλ dτ } ω [ b 3 3 ω6 b b ω 4 b 3 b ω ω 3 b ω b 6 b 4 ω4 b 6 b 4 ω b 5 ω ω 6 c ω 4 c 3 3ω 4 c ω c. ω b 6 b 4 ω b 5 b ω 6 b 4 ω b 5 ω ] 3.8 Then, if 3ω 4 c ω c /, we have sgn{re{ dλ/dτ }} /, we complete proof. Thus from Lemmas 3., 3.3, 3.4,and3.5, and we have the following. Theorem 3.6. Suppose that H and H hold, then the following results hold. The positive equilibrium of 3. is asymptotically stable, if c 3 > and c 3c ; if either c 3 < or c 3 and c 3c >, z >, h z, system 3. is asymptotically stable for τ,τ and system 3. undergoes a Hopf bifurcation at the origin when τ τ. 4. Direction of the Hopf Bifurcation In this section, we derive explicit formulae for computing the direction of the Hopf bifurcation and the stability of bifurcation periodic solution at critical values τ by using the normal form theory and center manifold reduction.

Abstract and Applied Analysis 7 Letting x S S,x E E,x 3 I I, x i t x i τt, τ τ μ, and dropping the bars for simplification of notation, system 3. is transformed into an FDE as ẋ t L μ x t f μ, x t, 4. with L μ ϕ τ μ [ B ϕ B ϕ ], 4. where a a B a 3 a 4 a, B a 7 e λτ, a 5 a 6 f μ, ϕ τ μ βϕ ϕ βϕ ϕ. 4.3 Using the Riesz representation theorem, there exists a function η θ, μ of bounded variation for θ,, such that L μ ϕ dη θ, μ ϕ θ ϕ C. 4.4 In fact, we can choose η θ, μ τ μ B δ θ B δ θ, 4.5 where δ θ is Dirac delta function. In the next, for ϕ,, we define dϕ A μ dθ, ϕ θ,, dη θ, μ ϕ θ, θ, R μ, θ,, ϕ f μ, ϕ, θ. 4.6 4.7

8 Abstract and Applied Analysis Then system 4. can be rewritten as ẋ t A μ x t R μ x t, 4.8 where x t θ x t θ. The adjoint operator A of A is defined by dψ s, s A μ,, dθ ψ dη T t, ψ t, s, 4.9 where η T is the transpose of the matrix η. For ϕ C, and ψ C,, we define ψ, ϕ ψ ϕ θ θ ξ ψ ξ θ dη θ ϕ ξ dξ, 4. where η θ η θ,. We know that ±iτ ω is an eigenvalue of A, so±iτ ω is also an eigenvalue of A. We can get q θ q e iτ ω θ, <θ. 4. q From the above discussion, it is easy to know that Aq iτ ω q. 4. Hence we obtain q iω q, a 5 q iω a. a 4.3 Suppose that the eigenvector q of A is q s q e iτ ω s, q 4.4 Then the following relationship is obtained: A q iτ ω q. 4.5

Abstract and Applied Analysis 9 Hence we obtain q a iω a 3, q a 4 a 7 e iω τ a 5 q. 4.6 Let q,q. 4.7 One can obtain q,q q q θ θ ξ q T ξ θ dη θ ϕ ξ dξ q q ρ q q θ τ q q a a θ ξ ρ a 3 a 4 a δ θ a 5 a 6 a 7 δ θ q e iτ ω θ dξ dθ q 4.8 ρ q q q q ρ τ e iω τ a 7 q q. Hence we obtain ρ q q q q τ e iω τ a 7 q q. 4.9 In the remainder of this section, by using the same notations as in Hassard et al. 6, we first compute the coordinates for describing the center manifold C at μ. Leting x t be the solution of 4. with μ, we define z t q,x t, W t, θ x t Re { z t q θ }. 4. On the center manifold C we have W t, θ W z, z, t, 4. where W z, z, t W θ z W θ zz W θ z. 4.

Abstract and Applied Analysis In fact, z and z are local coordinate for C in the direction of q and q.notethat,ifx t is, we will deal with real solutions only. Since μ ż t q, ẋ t q,a μ x t R μ x t q,ax t q,rx t iτ w z q f,w t, Re [ z t q ]. 4.3 Rewrite 4.3 as ż t iτ ω z g z, z, 4.4 where z z g z, z g g zz g g z z. 4.5 From 4. and 4.4, we have AW Re [ q f z, z q θ ], θ τ,, Ẇ ẋ t żq ż q AW Re [ q f z, z q θ ] f z, z, θ. 4.6 Let Ẇ AW H z, z, θ, 4.7 where H z, z, θ H θ z H θ zz H θ z. 4.8 Expanding the above series and comparing the corresponding coefficients, we obtain A iw W θ H θ, AW θ H θ, A iw W θ H θ. 4.9 Since x t x t θ W z, z, θ zq z q, we have W z, z, θ x t W z, z, θ z q eiω θ z q e iω θ. W 3 z, z, θ q q 4.3

Abstract and Applied Analysis Thus, we can obtain ϕ z z W z ϕ zq zq W z W zz W z, W zz W z. 4.3 So ϕ ϕ q z q z q q zz W W W q W z q z. 4.3 It follows from 4.4 and 4.5 that f ϕ, μ K z K zz K 3 z K 4 z z K z K zz K 3 z K 4 z z, 4.33 where K βq, K βq, K 3 β q q, K 4 β W W W q W, q K βq, K βq, K 3 β q q, K 4 β W W W q W. q 4.34 Since q /ρ, q, q T, we have K z K zz K 3 z K 4 z z g z, z, q ρ, q K z K zz K 3 z K 4 z z. 4.35 Comparing the coefficients of the above equation with those in 4.7, we have g K K q ρ, g K K q ρ, g K 3 K 3 q ρ, g K 4 K 4 q ρ. 4.36

Abstract and Applied Analysis In what follows, we focus on the computation of W θ and W θ. For the expression of g, we have H z, z, θ Re [ q f z, z q θ ] z z z z g g zz g q θ g g zz g q θ. 4.37 Comparing the coefficients of the above equation, we can obtain that H θ g q θ g q θ, θ,, 4.38 H θ g q θ g q θ, θ,. 4.39 Substituting 4.39 into 4.7 and 4.38 into 4.7, respectively, we get Ẇ θ iτ ω W θ g q θ g q θ, Ẇ θ g q θ g q θ. 4.4 So W θ ig τ ω q e iτ ω θ g 3iτ ω q e iτ ω θ E e iτ ω θ, W θ g iτ ω q e iτ ω θ g iτ ω q e iτ ω θ E. 4.4 In the sequel, we will determine E and E. Form the definition of A in 4.8, we have dη θ W θ iτ ω W H, dη θ W θ H. 4.4 4.43 From 4.6 and 4.38-4.39, we have H θ g q θ g q θ K,K, T, H θ g q θ g q θ K,K, T. 4.44 4.45

Abstract and Applied Analysis 3 Substituting 4.4 and 4.44 into 4.4 and noticing that iω I iω I e iωθ dη θ q, e iω θ dη θ q, 4.46 we can obtain iω I e iτ ω θ dη θ E K K T, 4.47 which leads to iω a a E K a 3 iω a 4 a 7 e iω τ a E K, a 5 iω a 6 E 3 a a E K a 3 a 4 a 7 a E K. a 5 a 6 E 3 4.48 It follows that K a E 3, iω a E E a 5 iω a 6 E 3, E 3 K K / iω a, a a 3 / iω a iω a 4 a 7 e iω τ iω a 6 /a 5 iω a 6 4.49 K a E 3, E a 6 E 3 a a 5 E, E 3 a a 5 K a 5 K a a 3 a 5 a 4 a 7 a a 6 a a 5 a 6. Based on the above analysis, we can see each g ij in 4.37 is determined by parameters and delays in 3.. Thus, we can compute the following quantities: μ Re C Re λ τ, T Im C μ Im λ ω, 4.5 β ReC.

4 Abstract and Applied Analysis St 4 35 3 5 5 5 4 6 8 4 6 It 8 6 4 8 6 4 a t 5 5 c t Et 8 6 4 8 6 4 5 5 It 4 3 5 Et 5 b t 5 d 5 Figure : τ 3 <τ. The positive equilibrium E of system 3. is asymptotically stable. St Theorem 4.. In 4.5, the following results hold. The sign of μ determines the directions of the Hopf bifurcation: if μ > μ < then the Hopf bifurcation is forward backward and the bifurcating periodic solutions exist for τ>τ τ<τ. The sign of β determines the stability of the bifurcating periodic solutions: the bifurcating periodic solutions are stable unstable if β < β >. 3 The sign of T determines the period of the bifurcating periodic solutions: the period increases decreases if T > T <. 5. Numerical Examples In this section, some numerical results of system 3. are presented to justify the Previous theorem above. As an example, considering the following parameters: μ., N, γ.8, α., β., ρ SR., ρ ER., then R.76, c 3 3.684e 5, and E 79, 33.6, 48.4. According to the Lemma 3., 3.9 has one positive real root ω.94. Correspondingly, by 3.3, weobtainτ 4.5. First, we choose τ 3 <τ, the corresponding wave form and phase plots are shown in Figure ; it is easy to see from Figure that system 3. is asymptotically stable. Finally, we choose τ 4.5 > τ the

Abstract and Applied Analysis 5 St It 4 35 3 5 5 5 5 5 a 8 6 4 8 6 4 5 5 c t t Et 8 6 4 8 6 4 5 5 5 Et Figure : τ 4.5 >τ. The bifurcation periodic solution for system 3. is stable. It 4 3 5 b d t 5 St 5 corresponding wave form and phase plots are shown in Figure ; it is easy to see that Figure undergoes a Hopf bifurcation. 6. Conclusions In this paper, considering that in addition to cleaning viruses in state I, people may immunize their computers with countermeasures in state S and state E, and since using antivirus software will take a period of time, we have constructed a computer virus model with time delay depending on the SEIR model. The theoretical analyses for the computer virus models are given. Furthermore, we have proved that when time cross through the critical value, the system exist a Hopf bifurcation. Finally, simulation clarifies our results. Acknowledgments This work was supported in part by the National Natural Science Foundation of China under Grant 69734 and Grant 6749, in part by the Research Fund of Preferential Development Domain for the Doctoral Program of Ministry of Education of China under Grant 935, in part by the Natural Science Foundation project of CQCSTC under Grant 9BA4, in part by Changjiang Scholars, and in part by the State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chongqing University, under Grant 7DA576.

6 Abstract and Applied Analysis References J. E. Sawyer, M. C. Kernan, D. E. Conlon, and H. Garland, Responses to the Michelangelo computer virus threat: the role of information sources and risk homeostasis theory, Journal of Applied Social Psychology, vol. 9, no., pp. 3 5, 999. B. K. Mishra and D. K. Saini, SEIRS epidemic model with delay for transmission of malicious objects in computer network, Applied Mathematics and Computation, vol. 88, no., pp. 476 48, 7. 3 B. K. Mishra and D. Saini, Mathematical models on computer viruses, Applied Mathematics and Computation, vol. 87, no., pp. 99 936, 7. 4 B. K. Mishra and N. Jha, Fixed period of temporary immunity after run of anti-malicious software on computer nodes, Applied Mathematics and Computation, vol. 9, no., pp. 7, 7. 5 E. Gelenbe, Dealing with software viruses: a biological paradigm, Information Security Technical Report, vol., no. 4, pp. 4 5, 7. 6 E. Gelenbe, Keeping viruses under control, in Proceedings of the th International Symposium Computer and Information Sciences ISCIS 5, vol. 3733 of Lecture Notes in Computer Science, Springer, 5. 7 W. O. Kermack and A. G. McKendrick, Contributions of mathematical theory to epidemics, Proceedings of the Royal Society of London Series A, vol. 5, pp. 7 7, 97. 8 W. O. Kermack and A. G. McKendrick, Contributions of mathematical theory to epidemics, Proceedings of the Royal Society of London Series A, vol. 38, pp. 55 83, 93. 9 W. O. Kermack and A. G. McKendrick, Contributions of mathematical theory to epidemics, Proceedings of the Royal Society of London Series A, vol. 4, pp. 94, 933. W. O. Kermack and A. G. McKendrick, Contributions of mathematical theory to epidemics, Proceedings of the Royal Society of London Series A, vol. 5, pp. 7 7, 97. W. O. Kermack and A. G. McKendrick, Contributions of mathematical theory to epidemics, Proceedings of the Royal Society of London Series A, vol. 38, pp. 55 83, 93. W. O. Kermack and A. G. McKendrick, Contributions of mathematical theory to epidemics, Proceedings of the Royal Society of London Series A, vol. 4, pp. 94, 933. 3 W. T. Richard and J. C. Mark, Modeling virus propagation in peer-to-peer networks, in Proceedings of the IEEE International Conference on Information, Communications and Signal Processing ICICS 5, pp. 98 985, 5. 4 Y. Yao, X. Xie, and H. Gao, Hopf bifurcation in an Internet worm propagation model with time delay in quarantine, Mathematical and Computer Modelling. In press. 5 H. Yuan and G. Chen, Network virus-epidemic model with the point-to-group information propagation, Applied Mathematics and Computation, vol. 6, no., pp. 357 367, 8. 6 B. D. Hassard, N. D. Kazarinoff, and Y. H. Wan, Theory and Applications of Hopf Bifurcation, vol. 4, Cambridge University Press, Cambridge, UK, 98. 7 M. Y. Li and J. S. Muldowney, Global stability for the SEIR model in epidemiology, Mathematical Biosciences, vol. 5, no., pp. 55 64, 995. 8 Y. Song, M. Han, and J. Wei, Stability and Hopf bifurcation analysis on a simplified BAM neural network with delays, Physica D, vol., no. 3-4, pp. 85 4, 5. 9 S. Ruan and J. Wei, On the zeros of a third degree exponential polynomial with applications to a delayed model for the control of testosterone secretion, IMA Journal of Mathemathics Applied in Medicine and Biology, vol. 8, no., pp. 4 5,. X. Li and J. Wei, On the zeros of a fourth degree exponential polynomial with applications to a neural network model with delays, Chaos, Solitons and Fractals, vol. 6, no., pp. 59 56, 5. H. Hu and L. Huang, Stability and Hopf bifurcation analysis on a ring of four neurons with delays, Applied Mathematics and Computation, vol. 3, no., pp. 587 599, 9. D. Fan, L. Hong, and J. Wei, Hopf bifurcation analysis in synaptically coupled HR neurons with two time delays, Nonlinear Dynamics, vol. 6, no. -, pp. 35 39,. 3 S. Ruan and J. Wei, On the zeros of transcendental functions with applications to stability of delay differential equations with two delays, Dynamics of Continuous, Discrete & Impulsive Systems Series A, vol., no. 6, pp. 863 874, 3.

Advances in Operations Research Advances in Decision Sciences Journal of Applied Mathematics Algebra Journal of Probability and Statistics The Scientific World Journal International Journal of Differential Equations Submit your manuscripts at International Journal of Advances in Combinatorics Mathematical Physics Journal of Complex Analysis International Journal of Mathematics and Mathematical Sciences Mathematical Problems in Engineering Journal of Mathematics Discrete Mathematics Journal of Discrete Dynamics in Nature and Society Journal of Function Spaces Abstract and Applied Analysis International Journal of Journal of Stochastic Analysis Optimization