IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i10p2328-d1148509.html
   My bibliography  Save this article

Synchronization Analysis of Linearly Coupled Systems with Signal-Dependent Noises

Author

Listed:
  • Yanhao Ren

    (School of Mathematical Sciences, Fudan University, Shanghai 200433, China
    Shanghai Center for Mathematical Sciences, Fudan University, Shanghai 200433, China)

  • Qiang Luo

    (Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China)

  • Wenlian Lu

    (School of Mathematical Sciences, Fudan University, Shanghai 200433, China
    Shanghai Center for Mathematical Sciences, Fudan University, Shanghai 200433, China
    Shanghai Key Laboratory for Contemporary Applied Mathematics, Fudan University, Shanghai 200433, China
    Key Laboratory of Mathematics for Nonlinear Science (Ministry of Education), Fudan University, Shanghai 200433, China)

Abstract

In this paper, we propose methods for analyzing the synchronization stability of stochastic linearly coupled differential equation systems, with signal-dependent noise perturbation. We consider signal-dependent noise, which is common in many fields, to discuss the stability of the synchronization manifold of multiagent systems and linearly coupled nonlinear dynamical systems under sufficient conditions. Numerical simulations are performed in the paper, and the results show the effectiveness of our theorems.

Suggested Citation

  • Yanhao Ren & Qiang Luo & Wenlian Lu, 2023. "Synchronization Analysis of Linearly Coupled Systems with Signal-Dependent Noises," Mathematics, MDPI, vol. 11(10), pages 1-15, May.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:10:p:2328-:d:1148509
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/10/2328/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/11/10/2328/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Xinlei Yi & Wenlian Lu & Tianping Chen, 2013. "Achieving Synchronization in Arrays of Coupled Differential Systems with Time-Varying Couplings," Abstract and Applied Analysis, Hindawi, vol. 2013, pages 1-15, July.
    2. Qiang Luo & Tian Ge & Fabian Grabenhorst & Jianfeng Feng & Edmund T Rolls, 2013. "Attention-Dependent Modulation of Cortical Taste Circuits Revealed by Granger Causality with Signal-Dependent Noise," PLOS Computational Biology, Public Library of Science, vol. 9(10), pages 1-15, October.
    3. Zhang, Qing & Chen, Shihua & Hu, Yuanming & Wang, Changping, 2006. "Synchronizing the noise-perturbed unified chaotic system by sliding mode control," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 371(2), pages 317-324.
    4. Akgül, Akif & Rajagopal, Karthikeyan & Durdu, Ali & Pala, Muhammed Ali & Boyraz, Ömer Faruk & Yildiz, Mustafa Zahid, 2021. "A simple fractional-order chaotic system based on memristor and memcapacitor and its synchronization application," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).
    5. Sun, Yonghui & Cao, Jinde, 2007. "Adaptive synchronization between two different noise-perturbed chaotic systems with fully unknown parameters," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 376(C), pages 253-265.
    6. Christopher M. Harris & Daniel M. Wolpert, 1998. "Signal-dependent noise determines motor planning," Nature, Nature, vol. 394(6695), pages 780-784, August.
    7. Yi Zhao & Jianwen Feng & Jingyi Wang, 2012. "Cluster Synchronization for Linearly Coupled Complex Networks with Identical and Nonidentical Nodes," Mathematical Problems in Engineering, Hindawi, vol. 2012, pages 1-17, August.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Shogo Yonekura & Yasuo Kuniyoshi, 2017. "Bodily motion fluctuation improves reaching success rate in a neurophysical agent via geometric-stochastic resonance," PLOS ONE, Public Library of Science, vol. 12(12), pages 1-16, December.
    2. Shih-Wei Wu & Maria F Dal Martello & Laurence T Maloney, 2009. "Sub-Optimal Allocation of Time in Sequential Movements," PLOS ONE, Public Library of Science, vol. 4(12), pages 1-13, December.
    3. Max Berniker & Megan K O’Brien & Konrad P Kording & Alaa A Ahmed, 2013. "An Examination of the Generalizability of Motor Costs," PLOS ONE, Public Library of Science, vol. 8(1), pages 1-11, January.
    4. Lionel Rigoux & Emmanuel Guigon, 2012. "A Model of Reward- and Effort-Based Optimal Decision Making and Motor Control," PLOS Computational Biology, Public Library of Science, vol. 8(10), pages 1-13, October.
    5. Seth W. Egger & Stephen G. Lisberger, 2022. "Neural structure of a sensory decoder for motor control," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    6. Ashesh Vasalya & Gowrishankar Ganesh & Abderrahmane Kheddar, 2018. "More than just co-workers: Presence of humanoid robot co-worker influences human performance," PLOS ONE, Public Library of Science, vol. 13(11), pages 1-19, November.
    7. Cornelis van de Kamp & Peter J Gawthrop & Henrik Gollee & Ian D Loram, 2013. "Refractoriness in Sustained Visuo-Manual Control: Is the Refractory Duration Intrinsic or Does It Depend on External System Properties?," PLOS Computational Biology, Public Library of Science, vol. 9(1), pages 1-15, January.
    8. Josh Merel & Donald M Pianto & John P Cunningham & Liam Paninski, 2015. "Encoder-Decoder Optimization for Brain-Computer Interfaces," PLOS Computational Biology, Public Library of Science, vol. 11(6), pages 1-25, June.
    9. Wei Zhang & Sasha Reschechtko & Barry Hahn & Cynthia Benson & Elias Youssef, 2019. "Force-stabilizing synergies can be retained by coordinating sensory-blocked and sensory-intact digits," PLOS ONE, Public Library of Science, vol. 14(12), pages 1-17, December.
    10. Julian J Tramper & Bart van den Broek & Wim Wiegerinck & Hilbert J Kappen & Stan Gielen, 2012. "Time-Integrated Position Error Accounts for Sensorimotor Behavior in Time-Constrained Tasks," PLOS ONE, Public Library of Science, vol. 7(3), pages 1-10, March.
    11. Konrad P Körding & Izumi Fukunaga & Ian S Howard & James N Ingram & Daniel M Wolpert, 2004. "A Neuroeconomics Approach to Inferring Utility Functions in Sensorimotor Control," PLOS Biology, Public Library of Science, vol. 2(10), pages 1-1, September.
    12. Vassilios N Christopoulos & Paul R Schrater, 2009. "Grasping Objects with Environmentally Induced Position Uncertainty," PLOS Computational Biology, Public Library of Science, vol. 5(10), pages 1-11, October.
    13. Bastien Berret & Frédéric Jean, 2020. "Stochastic optimal open-loop control as a theory of force and impedance planning via muscle co-contraction," PLOS Computational Biology, Public Library of Science, vol. 16(2), pages 1-28, February.
    14. H H L M Goossens & A J van Opstal, 2012. "Optimal Control of Saccades by Spatial-Temporal Activity Patterns in the Monkey Superior Colliculus," PLOS Computational Biology, Public Library of Science, vol. 8(5), pages 1-18, May.
    15. Michael Sherback & Francisco J Valero-Cuevas & Raffaello D'Andrea, 2010. "Slower Visuomotor Corrections with Unchanged Latency are Consistent with Optimal Adaptation to Increased Endogenous Noise in the Elderly," PLOS Computational Biology, Public Library of Science, vol. 6(3), pages 1-13, March.
    16. Bastien Berret & Adrien Conessa & Nicolas Schweighofer & Etienne Burdet, 2021. "Stochastic optimal feedforward-feedback control determines timing and variability of arm movements with or without vision," PLOS Computational Biology, Public Library of Science, vol. 17(6), pages 1-24, June.
    17. Nadav S Bar & Sigurd Skogestad & Jose M Marçal & Nachum Ulanovsky & Yossi Yovel, 2015. "A Sensory-Motor Control Model of Animal Flight Explains Why Bats Fly Differently in Light Versus Dark," PLOS Biology, Public Library of Science, vol. 13(1), pages 1-18, January.
    18. Yildirim, Melih, 2022. "Optical color image encryption scheme with a novel DNA encoding algorithm based on a chaotic circuit," Chaos, Solitons & Fractals, Elsevier, vol. 155(C).
    19. Huang, Pengfei & Chai, Yi & Chen, Xiaolong, 2022. "Multiple dynamics analysis of Lorenz-family systems and the application in signal detection," Chaos, Solitons & Fractals, Elsevier, vol. 156(C).
    20. Paolo Tommasino & Antonella Maselli & Domenico Campolo & Francesco Lacquaniti & Andrea d’Avella, 2021. "A Hessian-based decomposition characterizes how performance in complex motor skills depends on individual strategy and variability," PLOS ONE, Public Library of Science, vol. 16(6), pages 1-32, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:11:y:2023:i:10:p:2328-:d:1148509. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.