IDEAS home Printed from https://ideas.repec.org/a/eee/chsofr/v119y2019icp126-134.html
   My bibliography  Save this article

Saddle-node bifurcation parameter detection strategy with nested-layer particle swarm optimization

Author

Listed:
  • Matsushita, H.
  • Kurokawa, H.
  • Kousaka, T.

Abstract

Nested-layer particle swarm optimization (NLPSO) detects bifurcation parameters in discrete-time dynamical systems. Previous studies have proven the effectiveness of NLPSO for period-doubling bifurcations, but not for other bifurcation phenomena. This paper demonstrates that NLPSO can effectively detect saddle-node bifurcations. Problems in detecting saddle-node bifurcation parameters by conventional NLPSO are clarified, and are solved by imposing a simple condition on the NLPSO objective function. Under this conditional objective function, the NLPSO accurately detected both saddle-node and period-doubling bifurcation parameters regardless of their stability, without requiring careful initialization, exact calculations or Lyapunov exponents.

Suggested Citation

  • Matsushita, H. & Kurokawa, H. & Kousaka, T., 2019. "Saddle-node bifurcation parameter detection strategy with nested-layer particle swarm optimization," Chaos, Solitons & Fractals, Elsevier, vol. 119(C), pages 126-134.
    • Handle: RePEc:eee:chsofr:v:119:y:2019:i:c:p:126-134
    DOI: 10.1016/j.chaos.2018.12.016
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960077918308105
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.chaos.2018.12.016?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Liu, Bo & Wang, Ling & Jin, Yi-Hui & Tang, Fang & Huang, De-Xian, 2006. "Directing orbits of chaotic systems by particle swarm optimization," Chaos, Solitons & Fractals, Elsevier, vol. 29(2), pages 454-461.
    2. Gao, Fei & Gao, Hongrui & Li, Zhuoqiu & Tong, Hengqing & Lee, Ju-Jang, 2009. "Detecting unstable periodic orbits of nonlinear mappings by a novel quantum-behaved particle swarm optimization non-Lyapunov way," Chaos, Solitons & Fractals, Elsevier, vol. 42(4), pages 2450-2463.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Matsushita, Haruna & Kurokawa, Hiroaki & Kousaka, Takuji, 2023. "Non-gradient-based simultaneous strategy for bifurcation parameter detection," Chaos, Solitons & Fractals, Elsevier, vol. 176(C).
    2. Liu, Lianggui & Zhang, Rui & Chen, Qiuxia, 2022. "High-performance global peak tracking technique for PV arrays subject to rapidly changing PSC," Chaos, Solitons & Fractals, Elsevier, vol. 160(C).

    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. He, Qie & Wang, Ling & Liu, Bo, 2007. "Parameter estimation for chaotic systems by particle swarm optimization," Chaos, Solitons & Fractals, Elsevier, vol. 34(2), pages 654-661.
    2. Peng, Bo & Liu, Bo & Zhang, Fu-Yi & Wang, Ling, 2009. "Differential evolution algorithm-based parameter estimation for chaotic systems," Chaos, Solitons & Fractals, Elsevier, vol. 39(5), pages 2110-2118.
    3. Coelho, Leandro dos Santos, 2008. "A quantum particle swarm optimizer with chaotic mutation operator," Chaos, Solitons & Fractals, Elsevier, vol. 37(5), pages 1409-1418.
    4. Wang, Yang & Pan, Binfeng & Zheng, Yue & Lu, Xiang, 2017. "ETLBO based optimal targeting to the moon in the PCR3BP chaotic system," Chaos, Solitons & Fractals, Elsevier, vol. 105(C), pages 21-28.
    5. He, Yao-Yao & Zhou, Jian-Zhong & Xiang, Xiu-Qiao & Chen, Heng & Qin, Hui, 2009. "Comparison of different chaotic maps in particle swarm optimization algorithm for long-term cascaded hydroelectric system scheduling," Chaos, Solitons & Fractals, Elsevier, vol. 42(5), pages 3169-3176.
    6. Coelho, Leandro dos Santos & Mariani, Viviana Cocco, 2009. "A novel chaotic particle swarm optimization approach using Hénon map and implicit filtering local search for economic load dispatch," Chaos, Solitons & Fractals, Elsevier, vol. 39(2), pages 510-518.
    7. Alatas, Bilal & Akin, Erhan, 2009. "Chaotically encoded particle swarm optimization algorithm and its applications," Chaos, Solitons & Fractals, Elsevier, vol. 41(2), pages 939-950.

    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:eee:chsofr:v:119:y:2019:i:c:p:126-134. 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: Thayer, Thomas R. (email available below). General contact details of provider: https://www.journals.elsevier.com/chaos-solitons-and-fractals .

    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.