
In this paper, the complete synchronization and Mittag-Leffler synchronization problems of a kind of coupled fractional-order neural networks with time-varying delays are introduced and studied. First, the sufficient conditions for a controlled system to reach complete synchronization are established by using the Kronecker product technique and Lyapunov direct method under pinning control. Here the pinning controller only needs to control part of the nodes, which can save more resources. To make the system achieve complete synchronization, only the error system is stable. Next, a new adaptive feedback controller is designed, which combines the Razumikhin-type method and Mittag-Leffler stability theory to make the controlled system realize Mittag-Leffler synchronization. The controller has time delays, and the calculation can be simplified by constructing an appropriate auxiliary function. Finally, two numerical examples are given. The simulation process shows that the conditions of the main theorems are not difficult to obtain, and the simulation results confirm the feasibility of the theorems.
Citation: Biwen Li, Xuan Cheng. Synchronization analysis of coupled fractional-order neural networks with time-varying delays[J]. Mathematical Biosciences and Engineering, 2023, 20(8): 14846-14865. doi: 10.3934/mbe.2023665
[1] | Jehad Shaikhali, Gunnar Wingsle . Redox-regulated transcription in plants: Emerging concepts. AIMS Molecular Science, 2017, 4(3): 301-338. doi: 10.3934/molsci.2017.3.301 |
[2] | Amedea B. Seabra, Halley C. Oliveira . How nitric oxide donors can protect plants in a changing environment: what we know so far and perspectives. AIMS Molecular Science, 2016, 3(4): 692-718. doi: 10.3934/molsci.2016.4.692 |
[3] | Vittorio Emanuele Bianchi, Giancarlo Falcioni . Reactive oxygen species, health and longevity. AIMS Molecular Science, 2016, 3(4): 479-504. doi: 10.3934/molsci.2016.4.479 |
[4] | Luís J. del Valle, Lourdes Franco, Ramaz Katsarava, Jordi Puiggalí . Electrospun biodegradable polymers loaded with bactericide agents. AIMS Molecular Science, 2016, 3(1): 52-87. doi: 10.3934/molsci.2016.1.52 |
[5] | Isabella Martins Lourenço, Amedea Barozzi Seabra, Marcelo Lizama Vera, Nicolás Hoffmann, Olga Rubilar Araneda, Leonardo Bardehle Parra . Synthesis and application of zinc oxide nanoparticles in Pieris brassicae larvae as a possible pesticide effect. AIMS Molecular Science, 2024, 11(4): 351-366. doi: 10.3934/molsci.2024021 |
[6] | Vahid Pouresmaeil, Marwa Mawlood Salman Al-zand, Aida Pouresmaeil, Seyedeh Samira Saghravanian, Masoud Homayouni Tabrizi . Loading diltiazem onto surface-modified nanostructured lipid carriers to evaluate its apoptotic, cytotoxic, and inflammatory effects on human breast cancer cells. AIMS Molecular Science, 2024, 11(3): 231-250. doi: 10.3934/molsci.2024014 |
[7] | Giulia Ambrosi, Pamela Milani . Endoplasmic reticulum, oxidative stress and their complex crosstalk in neurodegeneration: proteostasis, signaling pathways and molecular chaperones. AIMS Molecular Science, 2017, 4(4): 424-444. doi: 10.3934/molsci.2017.4.424 |
[8] | Davide Lovisolo, Marianna Dionisi, Federico A. Ruffinatti, Carla Distasi . Nanoparticles and potential neurotoxicity: focus on molecular mechanisms. AIMS Molecular Science, 2018, 5(1): 1-13. doi: 10.3934/molsci.2018.1.1 |
[9] | Zhaoping Qin, Patrick Robichaud, Taihao Quan . Oxidative stress and CCN1 protein in human skin connective tissue aging. AIMS Molecular Science, 2016, 3(2): 269-279. doi: 10.3934/molsci.2016.2.269 |
[10] | Morgan Robinson, Brenda Yasie Lee, Zoya Leonenko . Drugs and drug delivery systems targeting amyloid-β in Alzheimer's disease. AIMS Molecular Science, 2015, 2(3): 332-358. doi: 10.3934/molsci.2015.3.332 |
In this paper, the complete synchronization and Mittag-Leffler synchronization problems of a kind of coupled fractional-order neural networks with time-varying delays are introduced and studied. First, the sufficient conditions for a controlled system to reach complete synchronization are established by using the Kronecker product technique and Lyapunov direct method under pinning control. Here the pinning controller only needs to control part of the nodes, which can save more resources. To make the system achieve complete synchronization, only the error system is stable. Next, a new adaptive feedback controller is designed, which combines the Razumikhin-type method and Mittag-Leffler stability theory to make the controlled system realize Mittag-Leffler synchronization. The controller has time delays, and the calculation can be simplified by constructing an appropriate auxiliary function. Finally, two numerical examples are given. The simulation process shows that the conditions of the main theorems are not difficult to obtain, and the simulation results confirm the feasibility of the theorems.
Nonlinear partial differential equation is a very important branch of the nonlinear science, which has been called the foreword and hot topic of current scientific development. In theoretical science and practical application, nonlinear partial differential is used to describe the problems in the fields of optics, mechanics, communication, control science and biology [1,2,3,4,5,6,7,8,9]. At present, the main problems in the study of nonlinear partial differential equations are the existence of solutions, the stability of solutions, numerical solutions and exact solutions. With the development of research, especially the study of exact solutions of nonlinear partial differential equations has important theoretical value and application value. In the last half century, many important methods for constructing exact solutions of nonlinear partial differential equations have been proposed, such as the planar dynamic system method [10], the Jacobi elliptic function method [11], the bilinear transformation method [12], the complete discriminant system method for polynomials [13], the unified Riccati equation method [14], the generalized Kudryashov method [15], and so on [16,17,18,19,20,21,22,23,24].
There is no unified method to obtain the exact solution of nonlinear partial differential equations. Although predecessors have obtained some analytical solutions with different methods, no scholar has studied the system with complete discrimination system for polynomial method.
The Fokas system is a very important class of nonlinear partial differential equations. In this article, we focus on the Fokas system, which is given as follows [25,26,27,28,29,30,31,32,33,34,35,36,37]
{ipt+r1pxx+r2pq=0,r3qy−r4(|p|2)x=0, | (1.1) |
where p=p(x,y,t) and q=q(x,y,t) are the complex functions which stand for the nonlinear pulse propagation in monomode optical fibers. The parameters r1,r2,r3 and r4 are arbitrary non-zero constants, which are coefficients of nonlinear terms in Eq (1.1) and reflect different states of optical solitons.
This paper is arranged as follows. In Section 2, we describe the method of the complete discrimination system for polynomial method. In Section 3, we substitute traveling wave transformation into nonlinear ordinary differential equations and obtain the different new single traveling wave solutions for the Fokas system by complete discrimination system for polynomial method. At the same time, we draw some images of solutions. In Section 4, the main results are summarized.
First, we consider the following partial differential equations:
{F(u,v,ux,ut,vx,vt,uxx,uxt,utt,⋯)=0, G(u,v,ux,ut,vx,vt,uxx,uxt,utt,⋯)=0, | (2.1) |
where F and G is polynomial function which is about the partial derivatives of each order of u(x,t) and v(x,t) with respect to x and t.
Step 1: Taking the traveling wave transformation u(x,t)=u(ξ),v(x,t)=v(ξ),ξ=kx+ct into Eq (2.1), then the partial differential equation is converted to an ordinary differential equation
{F(u,v,u′,v′,u″,v″,⋯)=0,G(u,v,u′,v′,u″,v″,⋯)=0. | (2.2) |
Step 2: The above nonlinear ordinary differential equations (2.2) are reduced to the following ordinary differential form after a series of transformations:
(u′)2=u3+d2u2+d1u+d0. | (2.3) |
The Eq (2.3) can also be written in integral form as:
±(ξ−ξ0)=∫du√u3+d2u2+d1u+d0. | (2.4) |
Step 3: Let ϕ(u)=u3+d2u2+d1u+d0. According to the complete discriminant system method of third-order polynomial
{Δ=−27(2d3227+d0−d1d23)2−4(d1−d223)3,D1=d1−d223, | (2.5) |
the classification of the solution of the equation can be obtained, and the classification of traveling wave solution of the Fokas system will be given in the following section.
In the current part, we obtain all exact solutions to Eq (1.1) by complete discrimination system for polynomial method. According to the wave transformation
p(x,y,t)=φ(η)ei(λ1x+λ2y+λ3t+λ4),q(x,y,t)=ϕ(η),η=x+y−vt, | (3.1) |
where λ1,λ2,λ3,λ4 and v are real parameters, and v represents the wave frame speed.
Substituting the above transformation Eq (3.1) into Eq (1.1), we get
{(−v+2r1λ1)iφ′−λ3φ+r1φ″−r1λ21φ+r2φϕ=0,r3ϕ′−2r4φφ′=0. | (3.2) |
Integrating the second equation in (3.2) and ignoring the integral constant, we get
ϕ(η)=r4φ2(η)r3. | (3.3) |
Substituting Eq (3.3) into the first equation in (3.2) and setting v=2r1λ1, we get the following:
r1φ″−(λ3+r1λ21)φ+r2r4φ3r3=0. | (3.4) |
Multiplying φ′ both sides of the Eq (3.4), then integrating once to get
(φ′)2=a4φ4+a2φ2+a0, | (3.5) |
where a4=−r2r42r1r3,a2=λ3+r1λ21r1, a0 is the arbitrary constant.
Let φ=±√(4a4)−13ω, b1=4a2(4a4)−23,b0=4a0(4a4)−13,η1=(4a4)13η. | (3.6) |
Equation (3.5) can be expressed as the following:
(ω′η1)2=ω3+b1ω2+b0ω. | (3.7) |
Then we can get the integral expression of Eq (3.7)
±(η1−η0)=∫dω√ω(ω2+b1ω+b0), | (3.8) |
where η0 is the constant of integration.
Here, we get the F(ω)=ω2+b1ω+b0 and Δ=b21−4b0. In order to solve Eq (3.7), we discuss the third order polynomial discrimination system in four cases.
Case 1:Δ=0 and ω>0.
When b1<0, the solution of Eq (3.7) is
ω1=−b12tanh2(12√−b12(η1−η0)). | (3.9) |
ω2=−b12coth2(12√−b12(η1−η0)). | (3.10) |
Thus, the classification of all solutions of Eq (3.7) is obtained by the third order polynomial discrimination system. The exact traveling wave solutions of the Eq (1.1) are obtained by combining the above solutions and the conditions (3.6) with Eq (3.1), can be expressed as below:
p1(x,y,t)=±√r3(λ3+r1λ21)r2r4tanh(12√−2(λ3+r1λ21)r1⋅(2r2r4r1r3)−23⋅((2r2r4r1r3)13η+η0))⋅ei(λ1x+λ2y+λ3t+λ4). | (3.11) |
In Eq (3.11), p1(x,y,t) is a dark soliton solution, it expresses the energy depression on a certain intensity background. Figure 1 depict two-dimensional graph, three-dimensional graph, contour plot and density plot of the solution.
q1(x,y,t)=λ3+r1λ21r2tanh2(12√−2(λ3+r1λ21)r1⋅(2r2r4r1r3)−23⋅((2r2r4r1r3)13η+η0)) | (3.12) |
p2(x,y,t)=±√r3(λ3+r1λ21)r2r4coth(12√−2(λ3+r1λ21)r1⋅(2r2r4r1r3)−23⋅((2r2r4r1r3)13η+η0))⋅ei(λ1x+λ2y+λ3t+λ4), | (3.13) |
where p1(x,y,t),q1(x,y,t),p2(x,y,t),q2(x,y,t) are hyperbolic function solutions. Specially, p2(x,y,t) is a bright soliton solution.
q2(x,y,t)=λ3+r1λ21r2coth2(12√−2(λ3+r1λ21)r1⋅(2r2r4r1r3)−23⋅((2r2r4r1r3)13η+η0)). | (3.14) |
When b1>0, the solution of Eq (3.7) is
ω3=b12tan2(12√b12(η1−η0)). | (3.15) |
The exact traveling wave solutions of the Eq (1.1) can be expressed as below:
p3(x,y,t)=±√−r3(λ3+r1λ21)r2r4tan(12√2(λ3+r1λ21)r1⋅(2r2r4r1r3)−23⋅((2r2r4r1r3)13η+η0))⋅ei(λ1x+λ2y+λ3t+λ4). | (3.16) |
q3(x,y,t)=−λ3+r1λ21r2tan2(12√2(λ3+r1λ21)r1⋅(2r2r4r1r3)−23⋅((2r2r4r1r3)13η+η0)). | (3.17) |
In Eq (3.16) and Eq (3.17), p3(x,y,t) and q3(x,y,t) are trigonometric function solutions. q3(x,y,t) is a periodic wave solution, and it Shows the periodicity of q3(x,y,t) in Figure 2(a), (b).
When b1=0, the solution of Eq (3.7) is
ω4=4(η1−η0)2. | (3.18) |
The exact traveling wave solutions of the Eq (1.1) can be expressed as below:
p4(x,y,t)=±√−(2r2r4r1r3)−132(2r2r4r1r3)13η+η0ei(λ1x+λ2y+λ3t+λ4), | (3.19) |
q4(x,y,t)=−r4r3(2r2r4r1r3)−134((2r2r4r1r3)13η+η0)2, | (3.20) |
where p4(x,y,t) is exponential function solution, and q4(x,y,t) is rational function solution.
Case 2: Δ=0 and b0=0.
When ω>−b1 and b1<0, the solution of Eq (3.7) is
ω5=b12tanh2(12√b12(η1−η0))−b1. | (3.21) |
ω6=b12coth2(12√b12(η1−η0))−b1. | (3.22) |
The exact traveling wave solutions of the Eq (1.1) can be expressed as below:
p5(x,y,t)=±√−r3(λ3+r1λ21)r2r4(tanh2(12√2(λ3+r1λ21)r1⋅(2r2r4r1r3)−23⋅((2r2r4r1r3)13η+η0))−2)⋅ei(λ1x+λ2y+λ3t+λ4), | (3.23) |
q5(x,y,t)=−λ3+r1λ21r2tanh2(12√2(λ3+r1λ21)r1⋅(2r2r4r1r3)−23⋅((2r2r4r1r3)13η+η0))+2(λ3+r1λ21)r2, | (3.24) |
p6(x,y,t)=±√−r3(λ3+r1λ21)r2r4(coth2(12√2(λ3+r1λ21)r1⋅(2r2r4r1r3)−23⋅((2r2r4r1r3)13η+η0))−2)⋅ei(λ1x+λ2y+λ3t+λ4), | (3.25) |
q6(x,y,t)=−λ3+r1λ21r2coth2(12√2(λ3+r1λ21)r1⋅(2r2r4r1r3)−23⋅((2r2r4r1r3)13η+η0))+2(λ3+r1λ21)r2, | (3.26) |
where p5(x,y,t),q5(x,y,t),p6(x,y,t) and q6(x,y,t) are hyperbolic function solutions.
When ω>−b1 and b1>0, the solution of Eq (3.7) is
ω7=−b12tan2(12√−b12(η1−η0))−b1. | (3.27) |
The exact traveling wave solutions of the Eq (1.1) can be expressed as below:
p7(x,y,t)=±√r3(λ3+r1λ21)r2r4(tan2(12√−2(λ3+r1λ21)r1⋅(2r2r4r1r3)−23⋅((2r2r4r1r3)13η+η0))+2)⋅ei(λ1x+λ2y+λ3t+λ4), | (3.28) |
q7(x,y,t)=λ3+r1λ21r2tan2(12√−2(λ3+r1λ21)r1⋅(2r2r4r1r3)−23⋅((2r2r4r1r3)13η+η0))+2(λ3+r1λ21)r2, | (3.29) |
where p7(x,y,t) and q7(x,y,t) are trigonometric function solutions.
Case 3: Δ>0 and b0≠0. Let u<v<s, there u,v and s are constants satisfying one of them is zero and two others are the root of F(ω)=0.
When u<ω<v, we can get the solution of Eq (3.7) is
ω8=u+(v−u)sn2(√s−u2(η1−η0),c), | (3.30) |
where c2=v−us−u.
The exact traveling wave solutions of the Eq (1.1) can be expressed as below:
p8(x,y,t)=±√−(2r2r4r1r3)−13[u+(v−u)⋅sn2(√s−u2((2r2r4r1r3)13η+η0),c)]⋅ei(λ1x+λ2y+λ3t+λ4). | (3.31) |
q8(x,y,t)=−r4r3(2r2r4r1r3)−13[u+(v−u)⋅sn2(√s−u2((2r2r4r1r3)13η+η0),c)]. | (3.32) |
When ω>s, the solution of Eq (3.7) is
ω9=−v⋅sn2(√s−u(η1−η0)/2,c)+scn2(√s−u(η1−η0)/2,c). | (3.33) |
The exact traveling wave solutions of the Eq (1.1) can be expressed as below:
p9(x,y,t)=±√−(2r2r4r1r3)−13−v⋅sn2(√s−u2((2r2r4r1r3)13η+η0),c)]+scn2(√s−u2((2r2r4r1r3)13η+η0),c)ei(λ1x+λ2y+λ3t+λ4). | (3.34) |
q9(x,y,t)=−r4r3(2r2r4r1r3)−13−v⋅sn2(√s−u2((2r2r4r1r3)13η+η0),c)]+scn2(√s−u2((2r2r4r1r3)13η+η0),c). | (3.35) |
Case 4: Δ<0.
When ω>0, similarly we get
ω10=2√b01+cn(b140(η1−η0),c)−√b0, | (3.36) |
where c2=(1−b1√b02)/2.
The exact traveling wave solutions of the Eq (1.1) can be expressed as below:
p10(x,y,t)=±√2√a0(2r2r4r1r3)−12[−21+cn((4a0(−2r2r4r1r3)−13)14((2r2r4r1r3)13η+η0),c)+1]⋅ei(λ1x+λ2y+λ3t+λ4), | (3.37) |
q10(x,y,t)=−r4r32√a0(2r2r4r1r3)−12[−21+cn((4a0(−2r2r4r1r3)−13)14((2r2r4r1r3)13η+η0),c)+1], | (3.38) |
where p8(x,y,t),q8(x,y,t),p9(x,y,t),q9(x,y,t),p10(x,y,t) and q10(x,y,t) are Jacobian elliptic function solutions.
In this paper, the complete discrimination system of polynomial method has been applied to construct the single traveling wave solutions of the Fokas system. The Jacobian elliptic function solutions, the trigonometric function solutions, the hyperbolic function solutions and the rational function solutions are obtained. The obtained solutions are very rich, which can help physicists understand the propagation of traveling wave in monomode optical fibers. Furthermore, we have also depicted two-dimensional graphs, three-dimensional graphs, contour plots and density plots of the solutions of Fokas system, which explains the state of solitons from different angles.
This work was supported by Scientific Research Funds of Chengdu University (Grant No.2081920034).
The authors declare no conflict of interest.
[1] |
X. F. Li, D. J. Bi, X. Xie, Y. L. Xie, Multi-Synchronization of stochastic coupled multi-stable neural networks with time-varying delay by impulsive control, IEEE Access, 7 (2019), 15641–15653. https://doi.org/10.1109/ACCESS.2019.2893641 doi: 10.1109/ACCESS.2019.2893641
![]() |
[2] |
X. M. Zhang, Q. L. Han, X. Ge, D. Ding, An overview of recent developments in Lyapunov-Krasovskii functionals and stability criteria for recurrent neural networks with time-varying delays, Neurocomputing, 313 (2018), 392–401. https://doi.org/10.1016/j.neucom.2018.06.038 doi: 10.1016/j.neucom.2018.06.038
![]() |
[3] |
H. Lu, W. L. He, Q. L. Han, C. Peng, Fixed-time pinning-controlled synchronization for coupled delayed neural networks with discontinuous activations, Neural Networks, 116 (2019), 139–149. https://doi.org/10.1016/j.neunet.2019.04.010 doi: 10.1016/j.neunet.2019.04.010
![]() |
[4] |
W. H. Chen, S. Luo, X. Lu, Multistability in a class of stochastic delayed Hopfield neural networks, Neural Networks, 68 (2015), 52–61. https://doi.org/10.1016/j.neunet.2015.04.010 doi: 10.1016/j.neunet.2015.04.010
![]() |
[5] |
Y. Xu, J. J. Liu, W. X. Li, Quasi-synchronization of fractional-order multi-layer networks with mismatched parameters via delay-dependent impulsive feedback control, Neural Networks, 150 (2022), 43–57. https://doi.org/10.1016/j.neunet.2022.02.023 doi: 10.1016/j.neunet.2022.02.023
![]() |
[6] |
S. Y. Yin, Y. Huang, T. Y. Chang, S. F. Chang, V. S. Tseng, Continual learning with attentive recurrent neural networks for temporal data classification, Neural Networks, 158 (2023), 171–187. https://doi.org/10.1016/j.neunet.2022.10.031 doi: 10.1016/j.neunet.2022.10.031
![]() |
[7] |
S. T. Wang, F. L. C. Korris, D. Fu, Applying the improved fuzzy cellular neural network IFCNN to white blood cell detection, Neurocomputing, 70 (2007), 1348–1359. https://doi.org/10.1016/j.neucom.2006.07.012 doi: 10.1016/j.neucom.2006.07.012
![]() |
[8] |
A. D. Liu, H. Zhao, Q. J. Wang, S. J. Niu, X. Z. Gao, C. Chen, et al., A new predefined-time stability theorem and its application in the synchronization of memristive complex-valued BAM neural networks, Neural Networks, 153 (2022), 152–163. https://doi.org/10.1016/j.neunet.2022.05.031 doi: 10.1016/j.neunet.2022.05.031
![]() |
[9] |
L. M. Wang, H. B. He, Z. G. Zeng, Global synchronization of fuzzy memristive neural networks with discrete and distributed delays, IEEE Trans. Fuzzy Syst., 28 (2020), 2022–2034. https://doi.org/10.1109/TFUZZ.2019.2930032 doi: 10.1109/TFUZZ.2019.2930032
![]() |
[10] |
C. D. Huang, J. Wang, X. P. Chen, J. D. Cao, Bifurcations in a fractional-order BAM neural network with four different delays, Neural Networks, 141 (2021), 344–354. https://doi.org/10.1016/j.neunet.2021.04.005 doi: 10.1016/j.neunet.2021.04.005
![]() |
[11] |
H. S. Hou, H. Zhang, Stability and hopf bifurcation of fractional complex-valued BAM neural networks with multiple time delays, Appl. Math. Comput., 450 (2023), 127986. https://doi.org/10.1016/j.amc.2023.127986 doi: 10.1016/j.amc.2023.127986
![]() |
[12] |
J. C. Fu, C. C. Chen, J. W. Chai, S. T. C. Wong, I. C. Li, Image segmentation by EM-based adaptive pulse coupled neural networks in brain magnetic resonance imaging, Comput. Med. Imag. Grap., 34 (2010), 308–320. https://doi.org/10.1016/j.compmedimag.2009.12.002 doi: 10.1016/j.compmedimag.2009.12.002
![]() |
[13] |
W. Zhang, J. Cao, D. Chen, A. Alsaedi, Bifurcations in a fractional-order BAM neural network with four different delays, Complexity, 2019 (2019), 1–7. https://doi.org/10.1155/2019/5612150 doi: 10.1155/2019/5612150
![]() |
[14] |
J. Zhou, Solutions of mixed-type functional differential equations with state-dependence, J. Differ. Equations, 312 (2022), 148–175. https://doi.org/10.1016/j.jde.2021.12.017 doi: 10.1016/j.jde.2021.12.017
![]() |
[15] |
Y. H. Lan, H. B. Gu, C. X. Chen, Y. Zhou, Y. P. Luo, An indirect Lyapunov approach to the observer-based robust control for fractional-order complex dynamic networks, Neurocomputing, 136 (2014), 235–242. https://doi.org/10.1016/j.neucom.2014.01.009 doi: 10.1016/j.neucom.2014.01.009
![]() |
[16] |
J. Jia, X. Huang, Y. X. Li, J. D. Cao, A. Alsaedi, Global stabilization of fractional-order memristor-based neural networks with time delay, IEEE Trans. Neural Networks Learn. Syst., 31 (2020), 997–1009. https://doi.org/10.1109/TNNLS.2019.2915353 doi: 10.1109/TNNLS.2019.2915353
![]() |
[17] |
J. J. Chen, B. S. Chen, Z. G. Zeng, O(t−α) -synchronization and Mittag-Leffler synchronization for the fractional-order memristive neural networks with delays and discontinuous neuron activations, Neural Networks, 100 (2018), 10–24. https://doi.org/10.1016/j.neunet.2018.01.004 doi: 10.1016/j.neunet.2018.01.004
![]() |
[18] |
J. X. Ci, Z. Y. Guo, H. Long, S. P. Wen, T. W. Huang, Multiple asymptotical ω-periodicity of fractional-order delayed neural networks under state-dependent switching, Neural Networks, 157 (2023), 11–25. https://doi.org/10.1016/j.neunet.2022.09.034 doi: 10.1016/j.neunet.2022.09.034
![]() |
[19] |
B. S. Chen, J. J. Chen, Razumikhin-type stability theorems for functional fractional-order differential systems and applications, Appl. Math. Comput., 254 (2015), 63–69. https://doi.org/10.1016/j.amc.2014.12.010 doi: 10.1016/j.amc.2014.12.010
![]() |
[20] |
C. G. Liu, J. L. Wang, Passivity of fractional-order coupled neural networks with multiple state/derivative couplings, Neurocomputing, 455 (2021), 379–389. https://doi.org/10.1016/j.neucom.2021.05.050 doi: 10.1016/j.neucom.2021.05.050
![]() |
[21] |
L. M. Wang, C. K. Zhang, Exponential synchronization of memristor-based competitive neural networks with reaction-diffusions and infinite distributed delays, IEEE Trans. Neural Networks Learn. Syst., 99 (2022), 1–14. https://doi.org/10.1109/TNNLS.2022.3176887 doi: 10.1109/TNNLS.2022.3176887
![]() |
[22] |
C. J. Xu, D. Mu, Z. X. Liu, Y. C. Pang, M. X. Liao, C. K. Aouiti, New insight into bifurcation of fractional-order 4D neural networks incorporating two different time delays, Commun. Nonlinear Sci. Numer. Simul., 113 (2023), 107043. https://doi.org/10.1016/j.cnsns.2022.107043 doi: 10.1016/j.cnsns.2022.107043
![]() |
[23] |
S. Yang, H. J. Jiang, C. Hu, J. Yu, Exponential synchronization of fractional-order reaction-diffusion coupled neural networks with hybrid delay-dependent impulses, J. Franklin Inst., 358 (2021), 3167–3192. https://doi.org/10.1016/j.jfranklin.2021.02.003 doi: 10.1016/j.jfranklin.2021.02.003
![]() |
[24] |
X. L. Ruan, A. L. Wu, Multi-quasi-synchronization of coupled fractional-order neural networks with delays via pinning impulsive control, Adv. Differ. Equations, 2017 (2017), 359–377. https://doi.org/10.1186/s13662-017-1417-6 doi: 10.1186/s13662-017-1417-6
![]() |
[25] |
H. B. Bao, J. H. Park, J. D. Cao, Adaptive synchronization of fractional-order output-coupling neural networks via quantized output control, IEEE Trans. Neural Networks Learn. Syst., 32 (2021), 3230–3239. https://doi.org/10.1109/TNNLS.2020.3013619 doi: 10.1109/TNNLS.2020.3013619
![]() |
[26] |
P. Liu, M. X. Kong, M. L. Xu, J. W. Sun, N. Liu, Pinning synchronization of coupled fractional-order time-varying delayed neural networks with arbitrary fixed topology, Neurocomputing, 400 (2020), 46–52. https://doi.org/10.1016/j.neucom.2020.03.029 doi: 10.1016/j.neucom.2020.03.029
![]() |
[27] |
W. J. Mo, H. B. Bao, Finite-time synchronization for fractional-order quaternion-valued coupled neural networks with saturated impulse, Chaos Solitons Fractals, 164 (2022), 112714–112726. https://doi.org/10.1016/j.chaos.2022.112714 doi: 10.1016/j.chaos.2022.112714
![]() |
[28] |
H. B. Bao, J. H. Park, J. D. Cao, Synchronization of fractional-order complex-valued neural networks with time delay, Neural Networks, 81 (2016), 16–28. https://doi.org/10.1016/j.neunet.2016.05.003 doi: 10.1016/j.neunet.2016.05.003
![]() |
[29] |
A. Pratap, R. Raja, C. Sowmiya, O. Bagdasar, J. D. Cao, G. Rajchakit, Robust generalized Mittag-Leffler synchronization of fractional order neural networks with discontinuous activation and impulses, Neural Networks, 103 (2018), 128–141. https://doi.org/10.1016/j.neunet.2018.03.012 doi: 10.1016/j.neunet.2018.03.012
![]() |
[30] |
X. Zhang, C. Li, Z. He, Cluster synchronization of delayed coupled neural networks: Delay-dependent distributed impulsive control, Neural Networks, 142 (2021), 34–43. https://doi.org/10.1016/j.neunet.2021.04.026 doi: 10.1016/j.neunet.2021.04.026
![]() |
[31] |
X. F. Hu, L. M. Wang, C. K. Zhang, X. B. Wan, Y. He, Fixed-time stabilization of discontinuous spatiotemporal neural networks with time-varying coefficients via aperiodically switching control, Sci. China Inf. Sci., 66 (2023), 152204–152218. https://doi.org/10.1007/s11432-022-3633-9 doi: 10.1007/s11432-022-3633-9
![]() |
[32] |
Z. L. Yan, X. Huang, Y. J. Fan, J. W. Xia, H. Shen, Threshold-function-dependent quasi-synchronization of delayed memristive neural networks via Hybrid event-triggered control, IEEE Trans. Syst. Man Cybern. Syst., 51 (2021), 6712–6722. https://doi.org/10.1109/TSMC.2020.2964605 doi: 10.1109/TSMC.2020.2964605
![]() |
[33] |
L. Wang, J. L. Wang, Analysis and pinning control for passivity and synchronization of multiple derivative coupled reaction diffusion neural networks, J. Franklin Inst., 357 (2020), 1221–1252. https://doi.org/10.1016/j.jfranklin.2019.12.003 doi: 10.1016/j.jfranklin.2019.12.003
![]() |
[34] |
X. Wu, S. T. Liu, H. Y. Wang, Y. Wang, Stability and pinning synchronization of delayed memristive neural networks with fractional-order and reaction-diffusion terms, ISA Trans., 136 (2023), 114–125. https://doi.org/10.1016/j.isatra.2022.10.046 doi: 10.1016/j.isatra.2022.10.046
![]() |
[35] |
J. L. Wang, H. N. Wu, Synchronization and adaptive control of an array of linearly coupled reaction-diffusion neural networks with hybrid coupling, IEEE Trans. Cybern., 44 (2014), 1350–1361. https://doi.org/10.1109/TCYB.2013.2283308 doi: 10.1109/TCYB.2013.2283308
![]() |
[36] |
X. Wu, S. Liu, R. Yang, Y. J. Zhang, X. Y. Li, Global synchronization of fractional complex networks with non-delayed and delayed couplings, Neurocomputing, 290 (2018), 43–49. https://doi.org/10.1016/j.neucom.2018.02.026 doi: 10.1016/j.neucom.2018.02.026
![]() |
[37] |
J. E. Zhang, Centralized data-sampling approach for global O(t−α) synchronization of fractional-order neural networks with time delays, Discrete Dyn. Nat. Soc., 2017 (2017), 1–10. https://doi.org/10.1155/2017/6157292 doi: 10.1155/2017/6157292
![]() |
[38] |
B. B. Zheng, Z. S. Wang, Mittag-Leffler synchronization of fractional-order coupled neural networks with mixed delays, Appl. Math. Comput., 430 (2022), 127303–127315. https://doi.org/10.1016/j.amc.2022.127303 doi: 10.1016/j.amc.2022.127303
![]() |
[39] |
L. Li, X. G. Liu, M. L. Tang, S. L. Zhang, X. M. Zhang, Asymptotical synchronization analysis of fractional-order complex neural networks with non-delayed and delayed couplings, Neurocomputing, 445 (2021), 180–193. https://doi.org/10.1016/j.neucom.2021.03.001 doi: 10.1016/j.neucom.2021.03.001
![]() |
[40] |
D. Mukherjee, Stability Analysis of a Stochastic Model for Prey-Predator System with Disease in the Prey, Nonlinear Anal. Modell. Control, 8 (2003), 83–92. https://doi.org/10.15388/NA.2003.8.2.15186 doi: 10.15388/NA.2003.8.2.15186
![]() |
[41] |
D. W. Ding, J. Yan, N. Wang, D. Liang, Pinning synchronization of fractional order complex-variable dynamical networks with time-varying coupling, Chaos Solitons Fractals, 104 (2017), 41–50. https://doi.org/10.1016/j.chaos.2017.07.028 doi: 10.1016/j.chaos.2017.07.028
![]() |
[42] |
W. Fei, Y. Yang, Quasi-synchronization for fractional-order delayed dynamical networks with heterogeneous nodes, Appl. Math. Comput., 339 (2018), 1–14. https://doi.org/10.1016/j.amc.2018.07.041 doi: 10.1016/j.amc.2018.07.041
![]() |
[43] |
L. Peng, X. Li, D. Bi, X. Xie, Y. Xie, Pinning multisynchronization of delayed fractional-order memristor-based neural networks with nonlinear coupling and almost-periodic perturbations, Neural Networks, 144 (2021), 372–383. https://doi.org/10.1016/j.neunet.2021.08.029 doi: 10.1016/j.neunet.2021.08.029
![]() |
1. | Andrew Geoly, Ernest Greene, Masking the Integration of Complementary Shape Cues, 2019, 13, 1662-453X, 10.3389/fnins.2019.00178 | |
2. | Ernest Greene, Comparing methods for scaling shape similarity, 2019, 6, 2373-7972, 54, 10.3934/Neuroscience.2019.2.54 | |
3. | Hannah Nordberg, Michael J Hautus, Ernest Greene, Visual encoding of partial unknown shape boundaries, 2018, 5, 2373-7972, 132, 10.3934/Neuroscience.2018.2.132 | |
4. | Ernest Greene, Hautus Michael J, Evaluating persistence of shape information using a matching protocol, 2018, 5, 2373-7972, 81, 10.3934/Neuroscience.2018.1.81 | |
5. | Ernest Greene, Jack Morrison, Computational Scaling of Shape Similarity That has Potential for Neuromorphic Implementation, 2018, 6, 2169-3536, 38294, 10.1109/ACCESS.2018.2853656 | |
6. | Ernest Greene, New encoding concepts for shape recognition are needed, 2018, 5, 2373-7972, 162, 10.3934/Neuroscience.2018.3.162 | |
7. | Cheng Chen, Kang Jiao, Letao Ling, Zhenhua Wang, Yuan Liu, Jie Zheng, 2023, Chapter 47, 978-981-19-3631-9, 382, 10.1007/978-981-19-3632-6_47 | |
8. | Bridget A. Kelly, Charles Kemp, Daniel R. Little, Duane Hamacher, Simon J. Cropper, Visual Perception Principles in Constellation Creation, 2024, 1756-8757, 10.1111/tops.12720 |