Research article Special Issues

Synchronization of generalized fractional complex networks with partial subchannel losses

  • Received: 24 November 2023 Revised: 26 January 2024 Accepted: 30 January 2024 Published: 19 February 2024
  • MSC : 26A33, 34K24, 34K37, 65R10, 68R10

  • This article focuses on the synchronization problem for two classes of complex networks with subchannel losses and generalized fractional derivatives. Initially, a new stability theorem for generalized fractional nonlinear system is formulated using the properties of generalized fractional calculus and the generalized Laplace transform. This result is also true for classical fractional cases. Subsequently, synchronization criteria for the generalized fractional complex networks are attained by the proposed stability theorem and the state layered method. Lastly, two numerical examples with some new kernel functions are given to validate the synchronization results.

    Citation: Changping Dai, Weiyuan Ma, Ling Guo. Synchronization of generalized fractional complex networks with partial subchannel losses[J]. AIMS Mathematics, 2024, 9(3): 7063-7083. doi: 10.3934/math.2024344

    Related Papers:

    [1] Yuanfu Shao . Dynamics and optimal harvesting of a stochastic predator-prey system with regime switching, S-type distributed time delays and Lévy jumps. AIMS Mathematics, 2022, 7(3): 4068-4093. doi: 10.3934/math.2022225
    [2] Xiaodong Wang, Kai Wang, Zhidong Teng . Global dynamics and density function in a class of stochastic SVI epidemic models with Lévy jumps and nonlinear incidence. AIMS Mathematics, 2023, 8(2): 2829-2855. doi: 10.3934/math.2023148
    [3] Shuo Ma, Jiangman Li, Qiang Li, Ruonan Liu . Adaptive exponential synchronization of impulsive coupled neutral stochastic neural networks with Lévy noise and probabilistic delays under non-Lipschitz conditions. AIMS Mathematics, 2024, 9(9): 24912-24933. doi: 10.3934/math.20241214
    [4] Zhengwen Yin, Yuanshun Tan . Threshold dynamics of stochastic SIRSW infectious disease model with multiparameter perturbation. AIMS Mathematics, 2024, 9(12): 33467-33492. doi: 10.3934/math.20241597
    [5] Tian Xu, Ailong Wu . Stabilization of nonlinear hybrid stochastic time-delay neural networks with Lévy noise using discrete-time feedback control. AIMS Mathematics, 2024, 9(10): 27080-27101. doi: 10.3934/math.20241317
    [6] Yassine Sabbar, Aeshah A. Raezah . Modeling mosquito-borne disease dynamics via stochastic differential equations and generalized tempered stable distribution. AIMS Mathematics, 2024, 9(8): 22454-22485. doi: 10.3934/math.20241092
    [7] Chuanfu Chai, Yuanfu Shao, Yaping Wang . Analysis of a Holling-type IV stochastic prey-predator system with anti-predatory behavior and Lévy noise. AIMS Mathematics, 2023, 8(9): 21033-21054. doi: 10.3934/math.20231071
    [8] Yassine Sabbar, Aeshah A. Raezah, Mohammed Moumni . Enhancing epidemic modeling: exploring heavy-tailed dynamics with the generalized tempered stable distribution. AIMS Mathematics, 2024, 9(10): 29496-29528. doi: 10.3934/math.20241429
    [9] Hong Qiu, Yanzhang Huo, Tianhui Ma . Dynamical analysis of a stochastic hybrid predator-prey model with Beddington-DeAngelis functional response and Lévy jumps. AIMS Mathematics, 2022, 7(8): 14492-14512. doi: 10.3934/math.2022799
    [10] Yassine Sabbar, Aeshah A. Raezah . Threshold analysis of an algae-zooplankton model incorporating general interaction rates and nonlinear independent stochastic components. AIMS Mathematics, 2024, 9(7): 18211-18235. doi: 10.3934/math.2024889
  • This article focuses on the synchronization problem for two classes of complex networks with subchannel losses and generalized fractional derivatives. Initially, a new stability theorem for generalized fractional nonlinear system is formulated using the properties of generalized fractional calculus and the generalized Laplace transform. This result is also true for classical fractional cases. Subsequently, synchronization criteria for the generalized fractional complex networks are attained by the proposed stability theorem and the state layered method. Lastly, two numerical examples with some new kernel functions are given to validate the synchronization results.



    In the last few decades, an outstanding advancement has been witnessed in nonlinear sciences and engineering fields. Many scientists showed keen interest in finding the exact and numerical solutions for the nonlinear PDEs. Numerous techniques were devised in this regard which includes the GERFM method [1], modified variational iteration algorithm-II [3], enhanced (GG)-expansion method [4], the direct algebraic method [5], the extended trial equation method [6], the generalized fractional integral conditions method [7], the modified simple equation method [8], the Monch's theorem method [9], the extended modified mapping method [10], the reductive perturbation method [11], the new probability transformation method [12] and the differential transformation method [13]. In future work for more related extensions or generalizations of the results these references may be very helpful [2,32,33,34,35,36].

    The time fractional derivatives in the fractional reaction-diffusion model describes the process relating to the physical phenomena, physically known as the historical dependence. The space fractional derivative explains the path dependence and global correlation properties of physical processes, that is, the global dependence. The reaction diffusion equation has a dynamic role in dissipative dynamical systems as studied by various biologists [38], scientists and engineers [14]. The nonlinear form of this model has found a number of applications in numerous branches of biology, physics and chemistry [14,15]. This model has also been useful for other areas of science and effectively generalized by employing the theory of fractional calculus, for instance see [15,16]. Diffusion-wave equations involving Caputo's derivative [17,18], Riemann-Liouville derivatives [19] have been discussed by various researchers. Anomalous dispersion equations can be explained by fractional derivative [20]. An extensive variety of exact methods which have been applied for exact solutions of the fractional nonlinear reaction diffusion equation, for example see [14,15,16,17,18,19,20] and references there in.

    To interpret numerous physical phenomena in some special fields of science and engineering, nonlinear evolution equations are extensively used as models especially in solid-state physics and plasma physics. Finding the exact solutions of NLEEs is a key role in the study of these physical phenomena [39]. A lot of research work has been carried out during the past decades for evaluating the exact and numerical solutions of many nonlinear evolution equations. Among them are homotopy analysis method [21], modified exp-function method [22], (GG)-expansion method [23], exp-function method [24], homotopy perturbation method [25], Jacobi elliptic function method [26], sub equation function method [27], kudryashov method [28], and so on.We can be expressed the exact solutions of FPDE via exp (φ(η)).

    (φ(η))=exp(φ(η))+μ exp(φ(η))+λ (1.1)

    The article is arranged as follows: Section 1 represents the introduction of the article. In section 2, we have explained the Caputo's fractional derivative. In section 3, we have interpreted the exp (φ(η))-expansion method. In section 4, we use this method to explore the reaction-diffusion model. In section 5 and 6, graphical representation and physical interpretation are explained. In section 7 and 8, we have interpreted the results, discussions and conclusion.

    Property 1: [29] A function f(x,t),where x>0 is considered as Cα. Here αR, if a R and (p>α), such that

    f(x)=xpf1(x) (1.2)
    f1(x)C[0,).Where f1(x)C[0,)

    Property2: [29] A function f(x,t), where x>0 is considered to be in space Cmα. Here mN{0}, if f(m)Cα.

    Iμtf(x,t)=1Γ(μ)t0(tT)μ1f(x,T)dT, t>0. (1.3)

    Property3: [29] Suppose fCα and α1, then the Riemann Liouville integral μ, where μ>0 is given by

    Property4: [29] A fractional Caputo derivative of f with respect to t, where fCm1, mN{0},is given as

    Dμtf(x,t)=mtmf(x,t), μ=m (1.4)
    =Imμtmtmf(x,t),m1μ<m (1.5)

    Note that

    IμtDμtf(x,t)=f(x,t)m1k=0k ftk(x,0)tkk!,  m1<μm, mN (1.6)
    Iμttν=Γ(ν+1)Γ(μ+ν+1)tμ+ν. (1.7)

    Consider the fractional partial differential equation,

    φ(u,Dαtu,ux,uxx,D2αtu,Dαtux,)=0,t>0,xR,0α1, (1.8)

    where Dαtu, Dαxu, Dαxxu are derivatives, u(η)=u(x,t). For solving Eq 1.8, we follow:

    Step 1: Using a transformation, we get,

    η=x±VtαΓ(1+α),u=u(η), (1.9)

    where constant V is a nonzero. By substituting Eq 1.9 in Eq 1.8 yields ODE:

    φ(u,±Vu,ku,V2u,k2u,)=0. (1.10)

    Step 2: Assuming the traveling wave solution

    u(η)=Mn=0an(eφ(η))n, (1.11)

    where φ(η) satisfies the following equation:

    φ(η)=eφ(η)+λ+μeφ(η) (1.12)
    φ(η)=λeφ(η)e2φ(η)+μλeφ(η)+μ2e2φ(η),

    where prime indicates derivative w.r.t.η. The solutions of Eq 1.12 are written in the form of different cases.

    Class1: when μ0 and λ24μ>0, we have

    φ(η)=ln{12μ(λλtanh(λ2(c1+η)))}. (1.13)

    where λ=(4μ+λ2)

    Class 2: when, μ0 and λ24μ<0, we have

    φ(η)=ln{12μ(λ+λtan(λ2(c1+η)))}. (1.14)

    where, λ=(4μ+λ2)

    Class 3: when, λ0,μ=0 and λ24μ>0, we have

    φ(η)=ln{λ1+exp(λ(k+η)) }

    φ(η)=ln{λ1+exp(λ(k+η)) }.

    Class 4: when, λ,μ0 and λ24μ=0, we have

    φ(η)=ln{2(2+λ(k+η))(λ2(η+k))}.

    Class 5: when, λ,μ=0 and λ24μ=0, we have

    φ(η)=[ln{η+k}], (1.15)

    Step 3: Using the homogeneous balancing principal, in (10), we attain M. In view of Eq (11), Eq (10) and Eq (12), we obtain a system of equations with these parameters, a_n, λ,μ. We substitute the values in Eq (11) and Eq (12) and obtained the results of Eq (8).

    Suppose the reaction-diffusion equation is,

    D2αtu+δuxx+βu+γu3=0,0<α1, (2.1)

    where δ, β and γ are parameters without zero, setting, δ=a, β=b and γ=c and changing Eq 2.1 into an ODE.

    V2u+au+bu+cu3=0, (2.2)

    where prime represents the derivative w. r. t. η.With the help of balancing principal, u and u3, we attain, M=1.

    Rewriting the solution of Eq 2.2 we get,

    u(η)=[a0+a1(exp(φ(η)))], (2.3)

    where a0, a10 are constants, while λ,μ are some constants.

    Substituting u,u and u3 in Eq 2.2, we get the solution sets as.

    Solution set 1

    λ=0, C=C,V=122μ(2aμ+b)μ,a0=0, a1=cbμc

    Substituting in Eq 2.3, we get,

    u(η)=a1(exp(φ(η))), (2.4)

    Substituting all the five classes in Eq 2.4, we get the solutions.

    Class 1: When, λ24μ>0 and μ0,

    v1=12cbμ tanh(12(122μ(2aμ+b)tαμΓ(α+1)+x)4μ)4μcμ

    Class 2: When, λ24μ<0 and μ0,

    v2=12cbμ tan(12(122μ(2aμ+b)tαμΓ(α+1)+x)4μ)4cμ (2.5)

    Solution set 2:

    {λ=0,C=C, V=122μ(2aμ+b)μ,a0=0, a1=cbμc}

    Substituting in Eq 2.3, we get,

    u(η)=a1(exp(φ(η))), (2.6)

    Substituting equations 1.13 and 1.14 in Eq 2.6, we get the solutions.

    Class 1: When, μ0 and λ24μ>0,

    v3=12cbμ tanh(12(122μ(2aμ+b)tαμΓ(α+1)+x)4μ)4μcμ (2.7)

    Class 2: When, μ0 and λ24μ<0,

    v4=12cbμ tan(12(122μ(2aμ+b)tαμΓ(α+1)+x)4μ)4cμ (2.8)

    Solution Set 3

    {λ=λ, C=C, V=(λ24μ)(aλ24aμ2b)λ24μ, a0=bλc(λ24μ)b, a1=2c(λ24μ)b μc(λ24μ)}

    Substituting in Eq 2.3, we get,

    u(η)=a1(exp(φ(η)))+a0, (2.9)

    Substituting equations 1.13 to 1.15 in Eq 2.9, we get the solutions.

    Class 1: When, μ0 and4μ+λ2>0,

    v5=bλc(λ24μ)b1c(λ24μ)(c(λ24μ)b(λtanh(12((λ24μ)(aλ24aμ2b)tα(λ24μ)Γ(α+1)+x)λ24μ)λ24μ)) (2.10)

    Class 2: When, μ0 and4μ+λ2<0,

    v6=bλc(λ24μ)b(c(λ24μ)b(λ+tan(12((λ24μ)(aλ24aμ2b)tα(λ24μ)Γ(α+1)+x)λ2+4μ)λ2+4μ)) (2.11)

    Class 3: When λ0, 4μ+λ2>0 and μ=0,

    v7=bλc(λ24μ)b2c(λ24μ)bμ(eλ((λ24μ)(aλ24aμ2b)tα(λ24μ)Γ(α+1)+x)1)c(λ24μ)λ (2.12)

    Class 4: When, λ0, μ=0 and 4μ+λ2=0,

    v8=bλc(λ24μ)b+2c(λ24μ)bμ(2λ((λ24μ)(aλ24aμ2b)tα(λ24μ)Γ(α+1)+x)+2)c(λ24μ)λ2((λ24μ)(aλ24aμ2b)tα(λ24μ)Γ(α+1)+x) (2.13)

    Case 5: When, λ=0, μ=0 and 4μ+λ2=0,

    v9=bλc(λ24μ)b2c(λ24μ)(c(λ24μ)b μ((λ24μ)(aλ24aμ2b)tα(λ24μ)Γ(α+1)+x)) (2.14)

    Physical interpretation

    With some free parameters the proposed technique provides solitary wave solutions. By setting the specific parameters we have explained the miscellaneous wave solutions. In this study, we would explain the physical interpretation of the solutions for reaction-diffusion equation taking solution v1 for μ=20λ=1b=11a=10a1=12c=11α=1, shows the solitary wave solution in Figure 1. Figure 2 shows Soliton wave solution with paremeters μ=910λ=1b=11a=10a1=12c=11α=.5. Figures 3, 4 and 7 interprets the singular kink solution of v3,v4, v7 for μ=.010λ=1b=.11a=10a1= 12 c=11α=.1,μ=.0010λ=991b=11a=10a1=102c=1α= .1, μ=20λ=1b=11a=10a1=12c=11α=.25. Finally kink wave results have been obtained from v5,v6, v8 by setting the parameters, μ=3.0λ=1b=11a=10a1= 12 c=11α=0.001,μ=2.0λ=1b=11a=10a1=12c= -11 α=0.001,μ=20λ=1b=11a=10a1=12c=11α=0.01, which is presented in Figures 5, 6 and 8. The solutions gained in this article have been checked by putting them back into the original equation and found correct. From the above obtained results we have many potential applications in fluid mechanics, quantum field theory, plasma physics and nonlinear optics.

    Figure 1.  Solitary wave solusion ν1(η).
    Figure 2.  Soliton wave solusion ν2(η).
    Figure 3.  Singular kink wave solusion ν3(η).
    Figure 4.  Singular kink wave solusion ν4(η).
    Figure 5.  kink wave solusion ν5(η).
    Figure 6.  kink wave solusion ν6(η).
    Figure 7.  Singular kink wave solusion ν7(η).
    Figure 8.  kink wave solusion ν8(η).

    When

    μ=20, λ=1, b=11, a=10,a1=12,c=11,α=1

    When

    μ=910, λ=1, b=11, a=10,a1=12,c=11,α=1

    When

    μ=.010, λ=1, b=.11, a=10,a1=12,c=11,α=.1

    When

    μ=.0010, λ=991, b=11, a=10,a1=102,c=11,α=.1

    When

    μ=3.0, λ=1, b=11, a=10,a1=12,c=11,α=.001

    When

    μ=2.0, λ=1, b=11, a=10,a1=12,c=11,α=.001

    When

    μ=20, λ=1, b=11, a=10,a1=12,c=11,α=.25

    When

    μ=20, λ=1, b=11, a=10,a1=12,c=11,α=.01

    If we set b=β,c=γ,μ=r and η=ξ in the obtaining solution v2 and v3 in this article is equal to u5 and u1 for case 5 respectively found in [31] see Table 1.

    Table 1.  Comparing the results of [31], with our results.
    Attained Results [31] results
    (i) If we set b=β,c=γ,μ=r and η=ξ then our solution v3 becomes v3=βγtanh(rξ) (i) The solution u1 is as u1=βγtanh(rξ)
    (ii) If we set b=β,c=γ,μ=r and η=ξ then our solution v2 becomes v2=βγtan(rξ) (ii) The solution u5 is as u5=βγtan(rξ)

     | Show Table
    DownLoad: CSV

    If we set b=β,c=γ and η=ξ, in the obtaining solution v2 and v4 in this article is equal to u and u for c10,c2=0,λ=0 and μ>0 in [23] see Table 2.

    Table 2.  Comparing the results of [23], with our results.
    Attained Results [23] results
    (i) If we set b=β,c=γ and η=ξ then our solution v2 becomes v2=βγtan(μξ) (i) The solution u is as u(ξ)=±βγtan(μξ)
    (ii) If we set b=β,c=γ and η=ξ then our solution v3 becomes v3=βγtan(μξ) (ii) The solution u is as u(ξ)=±βγtan(μξ)

     | Show Table
    DownLoad: CSV

    If we set b=β,c=γ in the obtaining solution v2 and v4 are equal to v9 and v9 for λ=0 and μ is positive in v9 found in [32] see Table 3.

    Table 3.  Comparing the results of [32], with our results.
    Attained Results [32] results
    (i) If we set b=β,c=γ then our solution v2 becomes v2=βγtan(μη) (i) The solution u is as u(ξ)=±βγtan(μη)
    (ii) If we set b=β,c=γ then our solution v4 becomes v3=βγtan(μη) (ii) The solution u is as u(ξ)=±βγtan(μη)

     | Show Table
    DownLoad: CSV

    If we set b=β,c=γ and η=ξ in the obtaining solution v2 and v4 in this article are equal to u2 and u2 for k>0,β>0 and our v1 and v3 are equal to u4 and u4 for k<0,β<0 respectively founded in [30] see Table 4.

    Table 4.  Comparing the results of [30], with our results.
    Attained Results [30] results
    (i) If we set b=β,c=γ,μ=k and η=ξ then our solution v2 becomes v2=βγtan(kξ) (i) The solution u2 is as u1=±βγtan(kξ)
    (ii) If we set b=β,c=γ,μ=k and η=ξ then our solution v4 becomes v2=βγtan(kξ) (ii) The solution u2 is as u2=±βγtan(kξ)
    (iii) If we set b=β,c=γ,μ=k and η=ξ then our solution v1 becomes v1=βγtanh(kξ) (iii) The solution u4 is as u4=±βγtanh(kξ)
    (iv) If we set b=β,c=γ,μ=k and η=ξ then our solution v4 becomes v4=βγtanh(kξ) (iv) The solution u4 is as u4=±βγtanh(kξ)

     | Show Table
    DownLoad: CSV

    In the current paper, we explore that the proposed method is effective and capable to find exact solutions of reaction-diffusion equation. The obtained solutions indicate that the suggested method is direct, constructive and simple. The proposed technique can be implemented to the other NLPDEs of fractional order to establish new reliable solutions. The exact solutions are different and new along with different values of parameters. The reduction in the magnitude of computational part and the consistency of the technique give a broader applicability to the technique. The reaction diffusion equation has a dynamic role in dissipative dynamical systems as studied by various biologists, scientists and engineers. This model has found a number of applications in biology, physics (neutron diffusi0n theory), ecology and chemistry. It has also been claimed that reaction-diffusion processes have crucial basis for procedures associated to morphogenesis in biology and may even be connected to skin pigmentation and animal coats. Other applications of this model contain spread of epidemics, ecological invasions, wound healing and tumors growth. Another aim for the consideration in reaction-diffusion systems is that although they are nonlinear partial differential equations, there are often visions for an analytical treatment. My main contribution is programming and comparisons.

    The authors declare that there is no conflict of interest regarding the publication of this paper.



    [1] H. Jeong, B. Tombor, R. Albert, Z. N. Oltvai, The large-scale organization of metabolic networks, Nature, 407 (2000), 651–654. https://doi.org/10.1038/35036627 doi: 10.1038/35036627
    [2] R. Albert, H. Jeong, A. L. Barabsi, Diameter of the world-wide web, Nature, 401 (1999), 130–131. https://doi.org/10.1038/43601 doi: 10.1038/43601
    [3] D. Knoke, S. Yang, Social network analysis, London: SAGE Publications, 2020. https://doi.org/10.4135/9781506389332
    [4] D. Lohr, P. Venkov, J. Zlatanova, Transcriptional regulation in the yeast GAL gene family: A complex genetic network, Faseb. J., 9 (1995), 777–787. https://doi.org/10.1096/fasebj.9.9.7601342 doi: 10.1096/fasebj.9.9.7601342
    [5] S. Strogatz, Exploring complex network, Nature, 410 (2001), 268–276. https://doi.org/10.1038/35065725
    [6] W. Y. Ma, Z. M. Li, N. R. Ma, Synchronization of discrete fractional-order complex networks with and without unknown topology, Chaos, 32 (2022), 013112. https://doi.org/10.1063/5.0072207 doi: 10.1063/5.0072207
    [7] H. Zhang, T. Ma, G. B. Huang, Z. Wang, Robust global exponential synchronization of uncertain chaotic delayed neural networks via dual-stage impulsive control, IEEE Trans. Syst. Man. Cybern. B, 40 (2009), 831–844. https://doi.org/10.1109/TSMCB.2009.2030506 doi: 10.1109/TSMCB.2009.2030506
    [8] T. Yang, Y. Niu, J. Yu, Clock synchronization in wireless sensor networks based on bayesian estimation, IEEE Access, 8 (2020), 69683–69694. https://doi.org/10.1109/ACCESS.2020.2984785 doi: 10.1109/ACCESS.2020.2984785
    [9] H. Zhang, D. Liu, Y. Luo, D. Wang, Adaptive dynamic programming for control-algorithms and stability, London: Springer, 2013. https://doi.org/10.1007/978-1-4471-4757-2
    [10] M. Wu, N. Xiong, A. V. Vasilakos, V. C. Leung, RNN-K: A reinforced newton method for consensus-based distributed optimization and control over multiagent systems, IEEE Trans. Cybern., 52 (2022), 4012–4026. https://doi.org/10.1109/TCYB.2020.3011819 doi: 10.1109/TCYB.2020.3011819
    [11] I. Podlubny, Fractional differential equations, Cambridge: Academic Press, 1999.
    [12] V. V. Uchaikin, Fractional derivatives for physicists and engineers, Berlin: Springer, 2013. https://doi.org/10.1007/978-3-642-33911-0
    [13] W. Y. Ma, N. R. Ma, C. P. Dai, Y. Q. Chen, X. Wang, Fractional modeling and optimal control strategies for mutated COVID-19 pandemic, Math. Method. Appl. Sci., 2023, 1–25. https://doi.org/10.1002/mma.9313
    [14] L. Ma, B. W. Wu, On the fractional Lyapunov exponent for Hadamard-type fractional differential system, Chaos, 33 (2023), 013117. https://doi.org/10.1063/5.0131661 doi: 10.1063/5.0131661
    [15] H. J. Li, J. D. Cao, Event-triggered group consensus for one-sided Lipschitz multi-agent systems with input saturation, Commun. Nonlinear Sci., 121 (2023), 107234. https://doi.org/10.1016/j.cnsns.2023.107234 doi: 10.1016/j.cnsns.2023.107234
    [16] C. P. Li, W. H. Deng, Remarks on fractional derivatives, Appl. Math. Comput., 187 (2007), 777–784. https://doi.org/10.1016/j.amc.2006.08.163 doi: 10.1016/j.amc.2006.08.163
    [17] N. G. N'Gbo, J. Tang, On the bounds of Lyapunov exponents for fractional differential systems with an exponential kernel, Int. J. Bifurcat. Chaos, 32 (2022), 2250188. https://doi.org/10.1142/S0218127422501887 doi: 10.1142/S0218127422501887
    [18] J. Hadamard, Essai sur l'étude des fonctions donnes par leur développement de Taylor, J. Math. Pure. Appl., 8 (1892), 101–186.
    [19] F. Jarad, D. Baleanu, A. Abdeljawad, Caputo-type modification of the Hadamard fractional derivatives, Adv. Differ. Equ., 2012 (2012), 1–8. https://doi.org/10.1186/1687-1847-2012-142 doi: 10.1186/1687-1847-2012-142
    [20] T. J. Osler, The fractional derivatives of a composite function, SIAM J. Math. Anal., 1 (1970), 288–293. https://doi.org/10.1137/0501026 doi: 10.1137/0501026
    [21] R. Almeida, A Caputo fractional derivative of a function with respect to another function, Commun. Nonlinear Sci., 44 (2017), 460–481. https://doi.org/10.1007/s11868-021-00421-y doi: 10.1007/s11868-021-00421-y
    [22] W. Ma, C. Dai, X. Li, X. Bao, On the kinetics of ψ-fractional differential equations, Fract. Calc. Appl. Anal., 2023. https://doi.org/10.1007/s13540-023-00210-y
    [23] G. Mahmoud, M. Ahmed, T. Abed-Elhameed, Active control technique of fractional-order chaotic complex systems, Eur. Phys. J. Plus, 131 (2016), 1–11. https://doi.org/10.1140/epjp/i2016-16200-x doi: 10.1140/epjp/i2016-16200-x
    [24] W. Zheng, Y. Q. Chen, X. Wang, M. Lin, A neural network-based design method of the fractional order PID controller for a class of motion control systems, Asian J. Control, 24 (2022), 3378–3393. https://doi.org/10.1002/asjc.2727 doi: 10.1002/asjc.2727
    [25] X. Yang, J. Cao, Finite-time stochastic synchronization of complex networks, Appl. Math. Model., 34 (2010), 3631–3641. https://doi.org/10.1016/j.apm.2010.03.012 doi: 10.1016/j.apm.2010.03.012
    [26] L. Duan, J. Li, Fixed-time synchronization of fuzzy neutral-type BAM memristive inertial neural networks with proportional delays, Inf. Sci., 576 (2021), 522–541. https://doi.org/10.1016/j.ins.2021.06.093 doi: 10.1016/j.ins.2021.06.093
    [27] W. Zhang, C. Li, X. He, H. Li, Finite-time synchronization of complex networks with non-identical nodes and impulsive disturbances, Mod. Phys. Lett. B, 32 (2018), 1850002. https://doi.org/10.1142/S0217984918500021 doi: 10.1142/S0217984918500021
    [28] Y. Wang, X. He, T. Li, Asymptotic and pinning synchronization of fractional-order nonidentical complex dynamical networks with uncertain parameters, Fractal Fract., 7 (2023), 571. https://doi.org/10.3390/fractalfract7080571 doi: 10.3390/fractalfract7080571
    [29] P. F. Xia, S. L. Zhou, G. B. Giannakis, Adaptive MIMO-OFDM based on partial channel state information, IEEE Trans. Signal. Process., 52 (2004), 202–213. https://doi.org/10.1109/TSP.2003.819986 doi: 10.1109/TSP.2003.819986
    [30] C. Huang, D. W. C. Ho, J. Lu, Partial-information-based distributed filtering in two-targets tracking sensor networks, IEEE Trans. Circ. Syst. I, 59 (2012), 820–832. https://doi.org/10.1109/TCSI.2011.2169912 doi: 10.1109/TCSI.2011.2169912
    [31] Q. Wu, H. Zhang, L. Xu, Q. Yan, Finite-time synchronization of general complex dynamical networks, Asian J. Control, 17 (2015), 1643–1653. https://doi.org/10.1002/asjc.985 doi: 10.1002/asjc.985
    [32] C. Zhou, L. Zemanová, G. Zamora-Lopez, Structure-function relationship in complex brain networks expressed by hierarchical synchronization, New J. Phys., 9 (2007), 178. https://doi.org/10.1088/1367-2630/9/6/178 doi: 10.1088/1367-2630/9/6/178
    [33] L. Li, X. Liu, W. Huang, Event-based bipartite multi-agent consensus with partial information transmission and communication delays under antagonistic interactions, Sci. China Inf. Sci., 63 (2020), 150204. https://doi.org/10.1007/s11432-019-2693-x doi: 10.1007/s11432-019-2693-x
    [34] Y. Li, J. Zhang, J. Lu, J. Lou, Finite-time synchronization of complex networks with partial communication channels failure, Inf. Sci., 634 (2023), 539–549. https://doi.org/10.1016/j.ins.2023.03.077 doi: 10.1016/j.ins.2023.03.077
    [35] Q. Fan, G. C. Wu, H. Fu, A note on function space and boundedness of the general fractional integral in continuous time random walk, J. Nonlinear Math. Phys., 29 (2022), 95–102. https://doi.org/10.1007/s44198-021-00021-w doi: 10.1007/s44198-021-00021-w
    [36] R. Almeida, A. B. Malinowska, T. Odzijewicz, On systems of fractional differential equations with the ψ-Caputo derivative and their applications, Math. Methods Appl. Sci., 44 (2021), 8026–8041. https://doi.org/10.1002/mma.5678 doi: 10.1002/mma.5678
    [37] F. Jarad, T. Abdeljawad, Generalized fractional derivatives and Laplace transform, Discrete Cont. Dyn. S, 13 (2020), 709–722. https://doi.org/10.3934/dcdss.2020039 doi: 10.3934/dcdss.2020039
    [38] A. Ahmadova, N. Mahmudov, Asymptotic stability analysis of Riemann-Liouville fractional stochastic neutral differential equations, Miskolc Math. Notes, 22 (2021), 503–520. https://doi.org/10.18514/MMN.2021.3600 doi: 10.18514/MMN.2021.3600
    [39] B. K. Lenka, S. N. Bora, Lyapunov stability theorems for ψ-Caputo derivative systems, Fract. Calc. Appl. Anal., 26 (2023), 220–236. https://doi.org/10.1007/s13540-022-00114-3 doi: 10.1007/s13540-022-00114-3
    [40] S. Liu, W. Jiang, X. Li, X. F. Zhou, Lyapunov stability analysis of fractional nonlinear systems, Appl. Math. Lett., 51 (2016), 13–19. https://doi.org/10.1016/j.aml.2015.06.018 doi: 10.1016/j.aml.2015.06.018
    [41] W. Yu, G. Chen, J. Lü, On pinning synchronization of complex dynamical networks, Automatica, 45 (2009), 429–435. https://doi.org/10.1016/j.automatica.2008.07.016 doi: 10.1016/j.automatica.2008.07.016
  • Reader Comments
  • © 2024 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(1171) PDF downloads(50) Cited by(0)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog