IDEAS home Printed from https://ideas.repec.org/a/eee/chsofr/v33y2007i4p1220-1229.html
   My bibliography  Save this article

A new algorithm for finding the shortest paths using PCNNs

Author

Listed:
  • Qu, Hong
  • Yi, Zhang

Abstract

Pulse coupled neural networks (PCNNs), based on the phenomena of synchronous pulse bursts in the animal visual cortex, are different from traditional artificial neural networks. Caulfield and Kinser have presented the idea of utilizing the autowave in PCNNs to find the solution of the maze problem. This paper which studies the performance of the autowave in PCNNs aims at applying it to optimization problems, such as the shortest path problem. A multi-output model of pulse coupled neural networks (MPCNNs) is studied. A new algorithm for finding the shortest path problem using MPCNNs is presented. Simulations are carried out to illustrate the performance of the proposed method.

Suggested Citation

  • Qu, Hong & Yi, Zhang, 2007. "A new algorithm for finding the shortest paths using PCNNs," Chaos, Solitons & Fractals, Elsevier, vol. 33(4), pages 1220-1229.
    • Handle: RePEc:eee:chsofr:v:33:y:2007:i:4:p:1220-1229
    DOI: 10.1016/j.chaos.2006.01.097
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S096007790600141X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.chaos.2006.01.097?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. Bai, Yanping & Zhang, Wendong & Jin, Zhen, 2006. "An new self-organizing maps strategy for solving the traveling salesman problem," Chaos, Solitons & Fractals, Elsevier, vol. 28(4), pages 1082-1089.
    2. Cheng, Chao-Jung & Liao, Teh-Lu & Hwang, Chi-Chuan, 2005. "Exponential synchronization of a class of chaotic neural networks," Chaos, Solitons & Fractals, Elsevier, vol. 24(1), pages 197-206.
    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. Lu, Hongtao & van Leeuwen, C., 2006. "Synchronization of chaotic neural networks via output or state coupling," Chaos, Solitons & Fractals, Elsevier, vol. 30(1), pages 166-176.
    2. Baluni, Sapna & Sehgal, Ishani & Yadav, Vijay K. & Das, Subir, 2024. "Exponential synchronization of a class of quaternion-valued neural network with time-varying delays: A Matrix Measure Approach," Chaos, Solitons & Fractals, Elsevier, vol. 182(C).
    3. Li, Tao & Fei, Shu-min & Zhang, Kan-jian, 2008. "Synchronization control of recurrent neural networks with distributed delays," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(4), pages 982-996.
    4. Gui, Zhanji & Ge, Weigao, 2007. "Periodic solutions of nonautonomous cellular neural networks with impulses," Chaos, Solitons & Fractals, Elsevier, vol. 32(5), pages 1760-1771.
    5. Wang, Weiping & Jia, Xiao & Luo, Xiong & Kurths, Jürgen & Yuan, Manman, 2019. "Fixed-time synchronization control of memristive MAM neural networks with mixed delays and application in chaotic secure communication," Chaos, Solitons & Fractals, Elsevier, vol. 126(C), pages 85-96.
    6. Lou, Xuyang & Cui, Baotong, 2007. "Synchronization of competitive neural networks with different time scales," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 380(C), pages 563-576.
    7. Park, Ju H., 2006. "On global stability criterion for neural networks with discrete and distributed delays," Chaos, Solitons & Fractals, Elsevier, vol. 30(4), pages 897-902.
    8. Lu, Jianquan & Cao, Jinde, 2007. "Synchronization-based approach for parameters identification in delayed chaotic neural networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 382(2), pages 672-682.
    9. Xiong, Wenjun & Xie, Wei & Cao, Jinde, 2006. "Adaptive exponential synchronization of delayed chaotic networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 370(2), pages 832-842.
    10. Zhu, Wei & Xu, Daoyi & Huang, Yumei, 2008. "Global impulsive exponential synchronization of time-delayed coupled chaotic systems," Chaos, Solitons & Fractals, Elsevier, vol. 35(5), pages 904-912.
    11. Park, Ju H. & Kwon, O.M., 2009. "Global stability for neural networks of neutral-type with interval time-varying delays," Chaos, Solitons & Fractals, Elsevier, vol. 41(3), pages 1174-1181.
    12. Lu, Junwei & Guo, Yiqian & Xu, Shengyuan, 2006. "Global asymptotic stability analysis for cellular neural networks with time delays," Chaos, Solitons & Fractals, Elsevier, vol. 29(2), pages 349-353.
    13. B. Mahadevan & S. Sivakumar & D. Dinesh Kumar & K. Ganeshram, 2013. "Redesigning Midday Meal Logistics for the Akshaya Patra Foundation: OR at Work in Feeding Hungry School Children," Interfaces, INFORMS, vol. 43(6), pages 530-546, December.
    14. Zhang, Hongmei & Cao, Jinde & Xiong, Lianglin, 2019. "Novel synchronization conditions for time-varying delayed Lur’e system with parametric uncertainty," Applied Mathematics and Computation, Elsevier, vol. 350(C), pages 224-236.
    15. Gu, Ya-Qin & Shao, Chun & Fu, Xin-Chu, 2006. "Complete synchronization and stability of star-shaped complex networks," Chaos, Solitons & Fractals, Elsevier, vol. 28(2), pages 480-488.
    16. Hu, Jiming, 2009. "Synchronization conditions for chaotic nonlinear continuous neural networks," Chaos, Solitons & Fractals, Elsevier, vol. 41(5), pages 2495-2501.
    17. Park, Ju H., 2008. "On global stability criterion of neural networks with continuously distributed delays," Chaos, Solitons & Fractals, Elsevier, vol. 37(2), pages 444-449.
    18. Park, Ju H., 2007. "An analysis of global robust stability of uncertain cellular neural networks with discrete and distributed delays," Chaos, Solitons & Fractals, Elsevier, vol. 32(2), pages 800-807.
    19. Jiang, Yanhong & Yang, Bin & Wang, Jincheng & Shao, Cheng, 2009. "Delay-dependent stability criterion for delayed Hopfield neural networks," Chaos, Solitons & Fractals, Elsevier, vol. 39(5), pages 2133-2137.
    20. Song, Qiankun & Cao, Jinde, 2007. "Synchronization and anti-synchronization for chaotic systems," Chaos, Solitons & Fractals, Elsevier, vol. 33(3), pages 929-939.

    More about this item

    Statistics

    Access and download statistics

    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:33:y:2007:i:4:p:1220-1229. 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.