Herein, we discuss an optimal control problem (OC-P) of a stochastic delay differential model to describe the dynamics of tumor-immune interactions under stochastic white noises and external treatments. The required criteria for the existence of an ergodic stationary distribution and possible extinction of tumors are obtained through Lyapunov functional theory. A stochastic optimality system is developed to reduce tumor cells using some control variables. The study found that combining white noises and time delays greatly affected the dynamics of the tumor-immune interaction model. Based on numerical results, it can be shown which variables are optimal for controlling tumor growth and which controls are effective for reducing tumor growth. With some conditions, white noise reduces tumor cell growth in the optimality problem. Some numerical simulations are conducted to validate the main results.
Citation: H. J. Alsakaji, F. A. Rihan, K. Udhayakumar, F. El Ktaibi. Stochastic tumor-immune interaction model with external treatments and time delays: An optimal control problem[J]. Mathematical Biosciences and Engineering, 2023, 20(11): 19270-19299. doi: 10.3934/mbe.2023852
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Herein, we discuss an optimal control problem (OC-P) of a stochastic delay differential model to describe the dynamics of tumor-immune interactions under stochastic white noises and external treatments. The required criteria for the existence of an ergodic stationary distribution and possible extinction of tumors are obtained through Lyapunov functional theory. A stochastic optimality system is developed to reduce tumor cells using some control variables. The study found that combining white noises and time delays greatly affected the dynamics of the tumor-immune interaction model. Based on numerical results, it can be shown which variables are optimal for controlling tumor growth and which controls are effective for reducing tumor growth. With some conditions, white noise reduces tumor cell growth in the optimality problem. Some numerical simulations are conducted to validate the main results.
Consider the semilinear elliptic equation
−Δu=f(u),inRn. | (1.1) |
A solution
∫Rn[|∇φ|2−f′(u)φ2]≥0,∀φ∈C∞0(Rn). | (1.2) |
The Morse index of a solution is defined to be the maximal dimension of the negative space for this quadratic form. A solution with finite Morse index is therefore not too unstable.
In 2000s, Dancer wrote a series of papers on stable and finite Morse index solutions, [13,14,15,16,17,18,19,21] (see also his survey [20] at 2010 ICM and the summary in Du [35,Section 8]). He obtained various classification results about these solutions and applied them to the study of equations with small parameters in bounded domains and global bifurcation problems. Many results on stable and finite Morse index solutions have appeared since then. We refer the reader to the monograph of Dupaigne [40] for an extensive list of results and references up to 2010.
In this paper we review some recent results about stable and finite Morse index solutions, mostly between 2010-2020. We will restrict our attention to the Liouville (-Bernstein-De Giorgi) type results on stable and finite Morse index solutions defined on the entire
In [20], Dancer proposed the following conjecture:
Assume
Then either
If
For nonegative nonlinearities
Theorem 2.1. Assume that
Hence in this case, the critical dimension is
Δu=u−p | (2.1) |
(see Esposito-Ghoussoub-Guo [42], Meadows [72], Ma-Wei [71] and Du-Guo [36]), to consider Liouville property for stable solutions, a more natural class is those solutions with a suitable polynomial growth at infinity (or even without any growth condition). In this case, it seems that the critical dimension should be
Problem 1. Assume
∫10f(u)du=−∞. | (2.2) |
Then there is no positive, stable solution on
Here the condition (2.2) implies that there does not exist one dimensional stable solution. Note that by the above remark, no assumption on the growth of solutions is added.
If
If there exists a one dimensional stable solution of (1.1), then a double well structure is associated to this equation, that is, there exist two constants
F(a±)=0,F(t)<0in(a−,a+). |
Here
Under this assumption, Dancer's conjecture is closely related to De Giorgi conjecture (De Giorgi [28]) and stable De Giorgi conjecture about Allen-Cahn equation
−Δu=u−u3. | (2.3) |
By the way, based on the result of Pacard-Wei [76], in this case, the critical dimension should be
Recall that De Giorgi conjecture states that
Suppose
While the stable De Giorgi conjecture states that
Suppose
In the Allen-Cahn equation, the standard double well potential
The De Giorgi conjecture has been solved by Ghoussoub-Gui [55] in dimension
By an observation of Dancer, it is known that the method introduced in Ghoussoub-Gui [55] and Ambrosio-Cabre [4] can be used to prove the stable De Giorgi conjecture in dimension
−div(σ2∇φ)=0, | (2.4) |
but by Gazzola [54], there is no hope to use this in dimensions
Even the following weaker version of the stable De Giorgi conjecture is still open.
Problem 2. Suppose
∫BR(0)[12|∇u|2+14(1−u2)2]≤CRn−1,∀R>0. | (2.5) |
If
We say the energy growth condition (2.5) is natural because it is satisfied by minimizing solutions. (Whether this condition holds for stable solutions is another unknown point in the stable De Giorgi conjecture.) If
Another missing geometric estimate for semilinear elliptic equations is the one correspondent to the famous Simons inequality (Simons [82]) for minimal hypersurfaces, which is a fundamental tool in the study of stable minimal hypersurfaces (see e.g. [81], [79]). This difficulty is also encountered in the study of Bernstein property for the Alt-Caffarelli one phase free boundary problem (see Alt-Caffarelli [2]) and nonlocal minimal surfaces (see Caffarelli-Roquejoffre-Savin [8]).
Recall that the Alt-Caffarelli one phase free boundary problem is
{Δu=0in{u>0},|∇u|=1on∂{u>0}. | (2.6) |
There are some variants, such as the problem studied by Phillips [77] and Alt-Phillips [3]
Δu=u−pχ{u>0},0<p<1, | (2.7) |
as well as various approximations to these problems, e.g.
Δu=fε(u), |
where
Although most studies on these free boundary problems are focused on minimizing solutions or viscosity solutions, recently there arises some interest in higher energy critical points, see Jerison-Perera [64]. To understand these solutions, the stability condition should play an important role. Indeed, even for minimizing solutions, to prove the optimal partial regularity of free boundaries, one needs the classification of stable, homogeneous solutions just as in the Bernstein problem for minimal hypersurfaces, see Weiss [92]. For the Alt-Caffarelli one phase free boundary problem, it is conjectured that the critical dimension is
In recent years, we see also much progress on De Giorgi conjecture for fractional Allen-Cahn equation
(−Δ)su=u−u3. | (2.8) |
In particular, Figalli-Serra [52] solved the stable De Giorgi conjecture for the
For equations enjoying a scaling invariance, much progress has been obtained in the last decade. This is because in this case, usually there exists a monotonicity formula. As in the Bernstein problem for minimal hypersurfaces (see Fleming [53]), we can use the blowing down analysis and then the classification of homogeneous solutions to prove Liouville type results.
This approach was first undertaken by the author in [86] to study the partial regularity of stable solutions to the Lane-Emden equation (see also Davila-Dupaigne-Farina [25] for related results)
−Δu=|u|p−1u,p>1. | (3.1) |
The scaling invariance for this equation says, if
uλ(x):=λ2p−1u(λx) |
is also a solution of (3.1).
The optimal Liouville theorem for stable and finite Morse index solutions to this equation was established in Farina [43] by a Moser type iteration argument. The blowing down analysis can be used to give another proof. By employing Pacard's monotonicity formula ([74,75]) and Federer's dimension reduction principle ([50]), a sharp dimension estimate on the singular set of stable solutions is given in [86]. In this approach, usually we need only an estimate up to the energy level.
This approach was further developed in Davila et. al. [26] and Du-Guo-Wang [39]. In [26], a monotonicity formula is derived for the fourth order Lane-Emden equation
Δ2u=|u|p−1u. | (3.2) |
Then by the blowing down analysis and the classification of stable, homogeneous solutions, an optimal Liouville theorem for stable solutions of (3.2) is established. In [39], a similar result is obtained for the weighted equation
−div(|x|θ∇u)=|x|ℓ|u|p−1u. | (3.3) |
For this equation, an
By now this approach has been applied to many other problems, for example, fourth order weighted equations or weighted systems [60,61], polyharmonic equations [70], nonlinear elliptic system [94], Toda system [89] and various elliptic equations involving fractional Laplacians [27,48,45,46,47,49,63].
One may be tempted to believe that this approach works well once the equation enjoys a scaling invariance. However, there are several important exceptions.
Problem 3. What is the optimal dimension for the Liouville theorem for stable solutions to the equation
−Δ2u=u−p,u≥0. | (3.4) |
This equation arises from the MEMS problem, see Esposito-Ghoussoub-Guo [42]. For some
We also encounter the same difficulty with the possible lack of a monotonicity formula in some other problems, which include the equation with
−Δpu=|u|m−1u | (3.5) |
and its fourth order version
Δ(|Δu|m−1Δu)=|u|p−1u. | (3.6) |
When both
{−Δu=vq,−Δv=up, | (3.7) |
see Mtiri-Ye [73]. For these problems, a mysterious problem is the role of homogeneous (or radial) solutions in the classification of stable solutions. In particular, is the radial, homogenous solution mostly unstable (in a suitable sense) among all solutions?
Finally, if the blowing down analysis approach works, usually we could obtain a radial symmetry result about stable solutions when the space dimension is critical. By the moving plane method, this claim can be reduced to the classification of stable, homogeneous solutions in the critical dimension. This is similar to the classification of stable minimal hypercones in
Problem 4. Suppose
By the radial symmetry criterion of Guo [58], this is reduced to the classification of solutions
−ΔSn−1w+2p−1(n−2−2p−1)w=|w|p−1w, | (3.8) |
satisfying the stability condition
∫Sn−1[|∇φ|2+(n−2)24φ2]≥p∫Sn−1|w|p−1φ2,∀φ∈C1(Sn−1). | (3.9) |
If
In [86], this is wrongly claimed to be true. But the proof therein works only for a small range
In Dancer-Guo-Wei [24], infinitely many solutions to (3.8) are constructed. However, it seems difficult to verify (3.9) because it involves a spectral bound condition. To the best knowledge of the author, there is still no known smooth stable (in the sense of (3.9)) solutions of (3.8) other than the constant solutions.
Problem 5. Take
Concerning finite Morse index solutions on
Next, for those scaling invariant equations discussed in Section 3, the blowing down analysis still works. So if the Liouville theorem holds for stable solutions, then it also holds for finite Morse index solutions, except in an exceptional dimension. (This is the "Sobolev" critical dimension. For example, for (3.1), it is well known that when
In the rest of this section we review some recent results on finite Morse index solutions of Allen-Cahn equation (2.3). The author in [88], and later jointly with Wei in [90], studied the structure of finite Morse index solutions in
Theorem 4.1. A finite Morse index solutions of the Allen-Cahn equation (2.3) in
Here an end is a connected component of the nodal set
By applying a reverse version of the infinite dimensional Lyapnunov-Schimdt reduction method (see Del Pino et. al. [30], [31], [32], [33], [34]), when the interfaces in the Allen-Cahn equation are clustering, we were able to reduce the stability condition in the Allen-Cahn equation to a corresponding one for Toda system
Δfk=e−√2(fk−fk−1)−e−√2(fk+1−fk). | (4.1) |
With such a connection between Allen-Cahn equation and Toda system, various results about stable solutions of Toda system can be transferred to the Allen-Cahn equation. For example, in Wang-Wei [90,91], Farina's integral estimate in [44] and the
−Δu=eu, | (4.2) |
were used to establish a curvature estimate for level sets of solutions to singularly perturbed Allen-Cahn equation. (More precisely, we need the corresponding results for Toda system (4.1), but which are direct generalizations of the results about Liouville equation, see [89].) In Gui-Wang-Wei [57], the Liouville theorem in Dancer-Farina [23] about finite Morse index solutions to (4.2) was used to establish the following result.
Theorem 4.2. Suppose
Here we do not state the
The "finite Morse index implies finite ends" property is expected to be true in any dimension, but by now only this axially symmetric case has been proven. Note that, although Theorem 4.2 is a result in high dimensions, the axial symmetry makes the problem essentially two dimensional. This allows us to prove the quadratic decay for curvatures of
In Cao-Shen-Zhu [11], it was shown that stable minimal hypersurfaces in
Problem 6. Can we prove a topological finiteness result for ends of finite Morse index solutions to Allen-Cahn equation (2.3)?
Since we only want a topological finiteness, this should hold for any dimension
In general, our understanding of finite Morse index solutions in higher dimensions is very lacking. Anyway, we do not know too much about stable solutions in dimensions
Problem 7. Assuming the stable De Giorgi conjecture in dimensions
The three dimensional case can be proved as in [90] and [57], but this approach does not work in dimensions
The author's interest in stable and finite Morse index solutions was largely intrigued by N. Dancer and Y. Du about ten years ago. They taught me a lot about stable and finite Morse index solutions when I was a postdoc at Sydney University. Several problems collected in this paper were communicated to the author during various occasions from Juan Davila, Louis Dupaigne, Zongming Guo, Xia Huang, Yong Liu, Yoshihiro Tonegawa, Juncheng Wei and Dong Ye over a long period. I am grateful to them for sharing their insights on these problems. My research has been supported by the National Natural Science Foundation of China (No. 11871381).
[1] | World Health Organization, Cancer, 2022. Available from: https://www.who.int/news-room/fact-sheets/detail/cancer |
[2] |
A. Desai, T. Mohammed, N. Duma, M. Garassino, L. Hicks, N. Kuderer, et al., COVID-19 and cancer: A review of the registry-based pandemic response, JAMA Oncol., 7 (2021), 1882–1890. https://doi.org/10.1001/jamaoncol.2021.4083 doi: 10.1001/jamaoncol.2021.4083
![]() |
[3] |
K. Dehingia, H. Sarmah, Y. Alharbi, K. Hosseini, Mathematical analysis of a cancer model with time-delay in tumor-immune interaction and stimulation processes, Adv. Differ. Equation, 2021 (2021), 1–27. https://doi.org/10.1186/s13662-021-03621-4 doi: 10.1186/s13662-021-03621-4
![]() |
[4] |
F. A. Rihan, K. Udhayakumar, Fractional order delay differential model of a tumor-immune system with vaccine efficacy: Stability, bifurcation and control, Chaos Solitons Fractals, 173 (2023) 113670. https://doi.org/10.1016/j.chaos.2023.113670 doi: 10.1016/j.chaos.2023.113670
![]() |
[5] |
F. A. Rihan, G. Velmurugan, Dynamics of fractional-order delay differential model for tumor-immune system, Chaos Solitons Fractals, 132 (2020), 109592. https://doi.org/10.1016/j.chaos.2019.109592 doi: 10.1016/j.chaos.2019.109592
![]() |
[6] |
V. Bitsouni, V. Tsilidis, Mathematical modeling of tumor-immune system interactions: The effect of rituximab on breast cancer immune response, J. Theor. Biol., 539 (2022), 111001. https://doi.org/10.1016/j.jtbi.2021.111001 doi: 10.1016/j.jtbi.2021.111001
![]() |
[7] |
M. Itik, S. Banks, Chaos in a three-dimensional cancer model, Int. J. Bifurcat. Chaos, 20 (2010), 71–79. https://doi.org/10.1142/S0218127410025417 doi: 10.1142/S0218127410025417
![]() |
[8] |
R. Yafia, A study of differential equation modeling malignant tumor cells in competition with immune system, Int. J. Biomath., 4 (2011), 185–206. https://doi.org/10.1142/S1793524511001404 doi: 10.1142/S1793524511001404
![]() |
[9] |
Y. Radouane, Hopf bifurcation in a delayed model for tumor-immune system competition with negative immune response, Discrete Dyn. Nat. Soc., 2006 (2006), 095296. https://doi.org/10.1155/DDNS/2006/95296 doi: 10.1155/DDNS/2006/95296
![]() |
[10] |
F. Najm, R. Yafia, M. A. Aziz-Alaoui, Hopf bifurcation in oncolytic therapeutic modeling: Viruses as anti-tumor means with viral lytic cycle, Int. J. Bifurcat. Chaos, 32 (2022), 2250171. https://doi.org/10.1142/S0218127422501711 doi: 10.1142/S0218127422501711
![]() |
[11] |
R. Brady, H. Enderling, Mathematical models of cancer: When to predict novel therapies, and when not to, Bull. Math. Biol., 81 (2019), 3722–3731. https://doi.org/10.1007/s11538-019-00640-x doi: 10.1007/s11538-019-00640-x
![]() |
[12] | T. Phan, S. Crook, A. Bryce, C. Maley, E. Kostelich, Y. Kuang, Mathematical modeling of prostate cancer and clinical application, Appl. Sci., 10 (2020), 2721. https://www.mdpi.com/2076-3417/10/8/2721 |
[13] |
O. Nave, Adding features from the mathematical model of breast cancer to predict the tumour size, Int. J. Comput. Math. Comput. Syst. Theory, 5 (2020), 159–174. https://doi.org/10.1080/23799927.2020.1792552 doi: 10.1080/23799927.2020.1792552
![]() |
[14] |
D. Kirschner, J. Panetta, Modeling immunotherapy of the tumor-immune interaction, J. Math. Biol., 37 (1998), 235–252. https://doi.org/10.1007/s002850050127 doi: 10.1007/s002850050127
![]() |
[15] |
V. Kuznetsov, L. Makalkin, M. Taylor, A. Perelson, Nonlinear dynamics of immunogenic tumors: Parameter estimation and global bifurcation analysis, Bull. Math. Biol., 56 (1994), 295–321. https://doi.org/10.1016/S0092-8240(05)80260-5 doi: 10.1016/S0092-8240(05)80260-5
![]() |
[16] |
A. Omame, C. Nnanna, S. Inyama, Optimal control and cost-effectiveness analysis of an HPV-chlamydia trachomatis co-infection model, Acta Biotheor., 69 (2021), 185–223. 10.1007/s10441-020-09401-z doi: 10.1007/s10441-020-09401-z
![]() |
[17] |
U. Ijeoma, S. Inyama, A. Omame, Mathematical model and optimal control of new-castle disease (ND), Appl. Math. Comput., 9 (2020), 70–84. doi: 10.11648/j.acm.20200903.14 doi: 10.11648/j.acm.20200903.14
![]() |
[18] |
F. A. Rihan, S. Lakshmanan, H. Maurer, Optimal control of tumor-immune model with time-delay and immuno-chemotherapy, Appl. Math. Comput., 353 (2019), 147–165. https://doi.org/10.1016/j.amc.2019.02.002 doi: 10.1016/j.amc.2019.02.002
![]() |
[19] |
F. A. Rihan, H. J. Alsakaji, S. Kundu, O. Mohamed, Dynamics of a time-delay differential model for tumor-immune interactions with random noise, Alex. Eng. J., 61 (2022), 11913–11923. https://doi.org/10.1016/j.aej.2022.05.027 doi: 10.1016/j.aej.2022.05.027
![]() |
[20] |
M. Yu, Y. Dong, Y. Takeuchi, Dual role of delay effects in a tumour–immune system, J. Biol. Dyn., 11 (2017), 334–347. https://doi.org/10.1080/17513758.2016.1231347 doi: 10.1080/17513758.2016.1231347
![]() |
[21] | X. Mao, Stochastic Differential Equations and Applications, Elsevier, 2007. https://doi.org/10.1016/B978-1-904275-34-3.50014-1 |
[22] |
M. Baar, L. Coquille, H. Mayer, M. Hölzel, M. Rogava, T. Tüting, et al., A stochastic model for immunotherapy of cancer, Sci. Rep., 6 (2016), 1–10. https://doi.org/10.1038/srep24169 doi: 10.1038/srep24169
![]() |
[23] |
L. Han, C. He, Y. Kuang, Dynamics of a model of tumor-immune interaction with time delay and noise, DCDS-S, 13(2020). http://dx.doi.org/10.3934/dcdss.2020140 doi: 10.3934/dcdss.2020140
![]() |
[24] |
H. J. Alsakaji, F. A. Rihan, A. Hashish, Dynamics of a stochastic epidemic model with vaccination and multiple time-delays for COVID-19 in the UAE, Complexity, 2022 (2022), 1–15. https://doi.org/10.1155/2022/4247800 doi: 10.1155/2022/4247800
![]() |
[25] |
C. Odoux, H. Fohrer, T. Hoppo, L. Guzik, D. Stolz, D. Lewis, et al., A stochastic model for cancer stem cell origin in metastatic colon cancer, Cancer Res., 68 (2008), 6932–6941. https://doi.org/10.1158/0008-5472.CAN-07-5779 doi: 10.1158/0008-5472.CAN-07-5779
![]() |
[26] |
Y. Deng, M. Liu, Analysis of a stochastic tumor-immune model with regime switching and impulsive perturbations, Appl. Math. Model., 78 (2020), 482–504. https://doi.org/10.1016/j.apm.2019.10.010 doi: 10.1016/j.apm.2019.10.010
![]() |
[27] |
A. Raza, J. Awrejcewicz, M. Rafiq, N. Ahmed, M. Mohsin, Stochastic analysis of nonlinear cancer disease model through virotherapy and computational methods, Mathematics, 10 (2022), 368. https://doi.org/10.3390/math10030368 doi: 10.3390/math10030368
![]() |
[28] |
K. Dehingia, H. Sarmah, K. Hosseini, K. Sadri, S. Salahshour, C. Park, An optimal control problem of immuno-chemotherapy in presence of gene therapy, AIMS Math., 6 (2021), 11530–11549. https://doi.org/10.3934/math.2021669 doi: 10.3934/math.2021669
![]() |
[29] | F. A. Rihan, Delay Differential Equations and Applications to Biology, Springer, 2021. https://doi.org/10.1007/978-981-16-0626-7 |
[30] |
C. Orrieri, E. Rocca, L. Scarpa, Optimal control of stochastic phase-field models related to tumor growth, ESAIM Control Optim. Calc. Var., 26 (2020), 104. https://doi.org/10.1051/cocv/2020022 doi: 10.1051/cocv/2020022
![]() |
[31] |
M. Huang, S. Liu, X. Song, X. Zou, Control strategies for a tumor-immune system with impulsive drug delivery under a random environment, Acta Math. Sci., 42 (2022), 1141–1159. https://doi.org/10.1007/s10473-022-0319-1 doi: 10.1007/s10473-022-0319-1
![]() |
[32] | L. J. Allen, An Introduction to Stochastic Processes with Applications to Biology, CRC press, 2010. https://doi.org/10.1201/b12537 |
[33] |
F. Rihan, H. Alsakaji, Persistence and extinction for stochastic delay differential model of prey predator system with hunting cooperation in predators, Adv. Differ. Equations, 2020 (2020), 1–22 https://doi.org/10.1186/s13662-020-02579-z doi: 10.1186/s13662-020-02579-z
![]() |
[34] | X. Mao, C. Yuan, Stochastic Differential Equations with Markovian Switching, World Scientific, 2006. https://doi.org/10.1142/p473 |
[35] | R. Hasminskii, Stochastic Stability of Differential Equations, Springer-Verlag Berlin Heidelberg, 2012. https://doi.org/10.1007/978-3-642-23280-0 |
[36] |
S. Rajasekar, M. Pitchaimani, Qualitative analysis of stochastically perturbed SIRS epidemic model with two viruses, Chaos Solitons Fractals, 118 (2019), 207–221. https://doi.org/10.1016/j.chaos.2018.11.023 doi: 10.1016/j.chaos.2018.11.023
![]() |
[37] |
E. Beretta, V. Kolmanovskii, L. Shaikhet, Stability of epidemic model with time delays influenced by stochastic perturbations, Math. Comput. Simul., 45 (1998), 269–277. https://doi.org/10.1016/S0378-4754(97)00106-7 doi: 10.1016/S0378-4754(97)00106-7
![]() |
[38] | M. Kinnally, Stationary Distributions for Stochastic Delay Differential Equations with Non-negativity Constraints, University of California, San Diego, 2009. |
[39] | G. Milstein, Numerical Integration of Stochastic Differential Equations, Springer Science & Business Media, 1994. https://doi.org/10.1007/978-94-015-8455-5 |
[40] |
Q. Luo, X. Mao, Stochastic population dynamics under regime switching, J. Math. Anal. Appl., 334 (2007), 69–84. https://doi.org/10.1016/j.jmaa.2006.12.032 doi: 10.1016/j.jmaa.2006.12.032
![]() |
[41] |
Q. An, E. Beretta, Y. Kuang, C. Wang, H. Wang, Geometric stability switch criteria in delay differential equations with two delays and delay dependent parameters, J. Differ. Equation, 266 (2019), 7073–7100. https://doi.org/10.1016/j.jde.2018.11.025 doi: 10.1016/j.jde.2018.11.025
![]() |
[42] |
Q. Sun, M. Xiao, M. B. Tao, Local bifurcation analysis of a fractional-order dynamic model of genetic regulatory networks with delays, Neural Process. Lett., 47 (2018), 1285–1296. https://doi.org/10.1007/s11063-017-9690-7 doi: 10.1007/s11063-017-9690-7
![]() |
[43] |
L. Li, Z. Wang, Y. Li, H. Shen, J. Lu, Hopf bifurcation analysis of a complex-valued neural network model with discrete and distributed delays, Appl. Math. Comput., 330 (2018), 152–169. https://doi.org/10.1016/j.amc.2018.02.029 doi: 10.1016/j.amc.2018.02.029
![]() |
[44] |
C. Xu, M. Liao, P. Li, Y. Guo, Q. Xiao, S. Yuan, Influence of multiple time delays on bifurcation of fractional-order neural networks, Appl. Math. Comput., 361 (2019), 565–582. https://doi.org/10.1016/j.amc.2019.05.057 doi: 10.1016/j.amc.2019.05.057
![]() |
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