IDEAS home Printed from https://ideas.repec.org/a/eee/matcom/v185y2021icp614-628.html
   My bibliography  Save this article

Simulation of Varying Parameter Recurrent Neural Network with application to matrix inversion

Author

Listed:
  • Stanimirović, Predrag
  • Gerontitis, Dimitris
  • Tzekis, Panagiotis
  • Behera, Ratikanta
  • Sahoo, Jajati Keshari

Abstract

A class of adaptive recurrent neural networks (RNN) for computing the inverse of a time-varying matrix with accelerated convergence time is defined and considered. The proposed neural dynamic model involves an exponential gain time-varying term in the nonlinear activation of the finite-time Zhang neural network (FTZNN) dynamical equation. Individual models belonging to the proposed class are defined by means of corresponding error functions. It is shown theoretically and experimentally that usage of the exponential nonlinear activation accelerates the convergence rate of the error function compared to previous dynamical systems for solving the time-varying (TV) and time-invariant (TI) matrix inversion.

Suggested Citation

  • Stanimirović, Predrag & Gerontitis, Dimitris & Tzekis, Panagiotis & Behera, Ratikanta & Sahoo, Jajati Keshari, 2021. "Simulation of Varying Parameter Recurrent Neural Network with application to matrix inversion," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 185(C), pages 614-628.
  • Handle: RePEc:eee:matcom:v:185:y:2021:i:c:p:614-628
    DOI: 10.1016/j.matcom.2021.01.018
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378475421000355
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.matcom.2021.01.018?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. Kate A. Smith, 1999. "Neural Networks for Combinatorial Optimization: A Review of More Than a Decade of Research," INFORMS Journal on Computing, INFORMS, vol. 11(1), pages 15-34, February.
    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. Zhu, Jingcan & Jin, Jie & Chen, Weijie & Gong, Jianqiang, 2022. "A combined power activation function based convergent factor-variable ZNN model for solving dynamic matrix inversion," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 197(C), pages 291-307.
    2. Jin, Jie & Chen, Weijie & Qiu, Lixin & Zhu, Jingcan & Liu, Haiyan, 2023. "A noise tolerant parameter-variable zeroing neural network and its applications," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 207(C), pages 482-498.

    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. Monfared, M.A.S. & Etemadi, M., 2006. "The impact of energy function structure on solving generalized assignment problem using Hopfield neural network," European Journal of Operational Research, Elsevier, vol. 168(2), pages 645-654, January.
    2. Eric Larsen & Sébastien Lachapelle & Yoshua Bengio & Emma Frejinger & Simon Lacoste-Julien & Andrea Lodi, 2022. "Predicting Tactical Solutions to Operational Planning Problems Under Imperfect Information," INFORMS Journal on Computing, INFORMS, vol. 34(1), pages 227-242, January.
    3. Emilio Moretti & Elena Tappia & Veronique Limère & Marco Melacini, 2021. "Exploring the application of machine learning to the assembly line feeding problem," Operations Management Research, Springer, vol. 14(3), pages 403-419, December.
    4. Iakov Karandashev & Boris Kryzhanovsky, 2013. "Increasing the attraction area of the global minimum in the binary optimization problem," Journal of Global Optimization, Springer, vol. 56(3), pages 1167-1185, July.
    5. Diana Goettsch & Krystel K. Castillo-Villar & Maria Aranguren, 2020. "Machine-Learning Methods to Select Potential Depot Locations for the Supply Chain of Biomass Co-Firing," Energies, MDPI, vol. 13(24), pages 1-18, December.
    6. Safae Rbihou & Khalid Haddouch & Karim El moutaouakil, 2024. "Optimizing hyperparameters in Hopfield neural networks using evolutionary search," OPSEARCH, Springer;Operational Research Society of India, vol. 61(3), pages 1245-1273, September.
    7. Carrasco, M. P. & Pato, M. V., 2004. "A comparison of discrete and continuous neural network approaches to solve the class/teacher timetabling problem," European Journal of Operational Research, Elsevier, vol. 153(1), pages 65-79, February.
    8. Stavrou, Eleni T. & Charalambous, Christakis & Spiliotis, Stelios, 2007. "Human resource management and performance: A neural network analysis," European Journal of Operational Research, Elsevier, vol. 181(1), pages 453-467, August.
    9. Bengio, Yoshua & Lodi, Andrea & Prouvost, Antoine, 2021. "Machine learning for combinatorial optimization: A methodological tour d’horizon," European Journal of Operational Research, Elsevier, vol. 290(2), pages 405-421.
    10. Jatinder N. D. Gupta & Randall S. Sexton & Enar A. Tunc, 2000. "Selecting Scheduling Heuristics Using Neural Networks," INFORMS Journal on Computing, INFORMS, vol. 12(2), pages 150-162, May.
    11. Stavrou, Eleni & Spiliotis, Stelios & Charalambous, Chris, 2010. "Flexible working arrangements in context: An empirical investigation through self-organizing maps," European Journal of Operational Research, Elsevier, vol. 202(3), pages 893-902, May.
    12. Christoph Hertrich & Martin Skutella, 2023. "Provably Good Solutions to the Knapsack Problem via Neural Networks of Bounded Size," INFORMS Journal on Computing, INFORMS, vol. 35(5), pages 1079-1097, September.
    13. Justin J. Boutilier & Timothy C. Y. Chan, 2023. "Introducing and Integrating Machine Learning in an Operations Research Curriculum: An Application-Driven Course," INFORMS Transactions on Education, INFORMS, vol. 23(2), pages 64-83, January.
    14. Serpen, Gursel, 2004. "Managing spatio-temporal complexity in Hopfield neural network simulations for large-scale static optimization," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 64(2), pages 279-293.
    15. Dogacan Yilmaz & İ. Esra Büyüktahtakın, 2023. "Learning Optimal Solutions via an LSTM-Optimization Framework," SN Operations Research Forum, Springer, vol. 4(2), pages 1-40, June.
    16. William J. Wolfe, 1999. "A Fuzzy Hopfield-Tank Traveling Salesman Problem Model," INFORMS Journal on Computing, INFORMS, vol. 11(4), pages 329-344, November.
    17. Dimitris Fouskakis & David Draper, 2002. "Stochastic Optimization: a Review," International Statistical Review, International Statistical Institute, vol. 70(3), pages 315-349, December.
    18. Agarwal, Anurag & Colak, Selcuk & Jacob, Varghese S. & Pirkul, Hasan, 2006. "Heuristics and augmented neural networks for task scheduling with non-identical machines," European Journal of Operational Research, Elsevier, vol. 175(1), pages 296-317, November.
    19. Timothy C. Y. Chan & Daniel Letourneau & Benjamin G. Potter, 2022. "Sparse flexible design: a machine learning approach," Flexible Services and Manufacturing Journal, Springer, vol. 34(4), pages 1066-1116, December.
    20. Xiangyi Zhang & Lu Chen & Michel Gendreau & André Langevin, 2022. "Learning-Based Branch-and-Price Algorithms for the Vehicle Routing Problem with Time Windows and Two-Dimensional Loading Constraints," INFORMS Journal on Computing, INFORMS, vol. 34(3), pages 1419-1436, May.

    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:matcom:v:185:y:2021:i:c:p:614-628. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/mathematics-and-computers-in-simulation/ .

    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.