IDEAS home Printed from https://ideas.repec.org/a/wly/complx/v2022y2022i1n9244890.html

Exhaustive Search and Power‐Based Gradient Descent Algorithms for Time‐Delayed FIR Models

Author

Listed:
  • Hua Chen
  • Yuejiang Ji

Abstract

In this study, two modified gradient descent (GD) algorithms are proposed for time‐delayed models. To estimate the parameters and time‐delay simultaneously, a redundant rule method is introduced, which turns the time‐delayed model into an augmented model. Then, two GD algorithms can be used to identify the time‐delayed model. Compared with the traditional GD algorithms, these two modified GD algorithms have the following advantages: (1) avoid a high‐order matrix eigenvalue calculation, thus, are more efficient for large‐scale systems; (2) have faster convergence rates, therefore, are more practical in engineering practices. The convergence properties and simulation examples are presented to illustrate the efficiency of the two algorithms.

Suggested Citation

  • Hua Chen & Yuejiang Ji, 2022. "Exhaustive Search and Power‐Based Gradient Descent Algorithms for Time‐Delayed FIR Models," Complexity, John Wiley & Sons, vol. 2022(1).
    • Handle: RePEc:wly:complx:v:2022:y:2022:i:1:n:9244890
    DOI: 10.1155/2022/9244890
    as

    Download full text from publisher

    File URL: https://doi.org/10.1155/2022/9244890
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2022/9244890?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
    ---><---

    References listed on IDEAS

    as
    1. Yuejiang Ji & Lixin Lv & Quanmin Zhu, 2021. "Two Identification Methods for a Nonlinear Membership Function," Complexity, Hindawi, vol. 2021, pages 1-7, May.
    2. Zeshan Aslam Khan & Naveed Ishtiaq Chaudhary & Syed Zubair, 2019. "Fractional stochastic gradient descent for recommender systems," Electronic Markets, Springer;IIM University of St. Gallen, vol. 29(2), pages 275-285, June.
    3. Junhong Li & Xiao Li, 2018. "Particle Swarm Optimization Iterative Identification Algorithm and Gradient Iterative Identification Algorithm for Wiener Systems with Colored Noise," Complexity, Hindawi, vol. 2018, pages 1-8, July.
    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. Chaudhary, Naveed Ishtiaq & Raja, Muhammad Asif Zahoor & Khan, Zeshan Aslam & Mehmood, Ammara & Shah, Syed Muslim, 2022. "Design of fractional hierarchical gradient descent algorithm for parameter estimation of nonlinear control autoregressive systems," Chaos, Solitons & Fractals, Elsevier, vol. 157(C).
    2. Khan, Zeshan Aslam & Chaudhary, Naveed Ishtiaq & Khan, Taimoor Ali & Farooq, Umair & Pinto, Carla M.A. & Raja, Muhammad Asif Zahoor, 2023. "Enhanced fractional prediction scheme for effective matrix factorization in chaotic feedback recommender systems," Chaos, Solitons & Fractals, Elsevier, vol. 176(C).
    3. Yunqi Jiang & Huaqing Zhang & Kai Zhang & Jian Wang & Shiti Cui & Jianfa Han & Liming Zhang & Jun Yao, 2022. "Reservoir Characterization and Productivity Forecast Based on Knowledge Interaction Neural Network," Mathematics, MDPI, vol. 10(9), pages 1-22, May.
    4. Edson Fernandez & Victor Huilcapi & Isabela Birs & Ricardo Cajo, 2025. "The Role of Fractional Calculus in Modern Optimization: A Survey of Algorithms, Applications, and Open Challenges," Mathematics, MDPI, vol. 13(19), pages 1-34, October.
    5. Naveed Ahmed Malik & Ching-Lung Chang & Naveed Ishtiaq Chaudhary & Muhammad Asif Zahoor Raja & Khalid Mehmood Cheema & Chi-Min Shu & Sultan S. Alshamrani, 2022. "Knacks of Fractional Order Swarming Intelligence for Parameter Estimation of Harmonics in Electrical Systems," Mathematics, MDPI, vol. 10(9), pages 1-20, May.
    6. Farah Tawfiq Abdul Hussien & Abdul Monem S. Rahma & Hala B. Abdulwahab, 2021. "An E-Commerce Recommendation System Based on Dynamic Analysis of Customer Behavior," Sustainability, MDPI, vol. 13(19), pages 1-21, September.
    7. Xiaojun Zhou & Chunna Zhao & Yaqun Huang, 2023. "A Deep Learning Optimizer Based on Grünwald–Letnikov Fractional Order Definition," Mathematics, MDPI, vol. 11(2), pages 1-15, January.
    8. Ravi S. Sharma & Aijaz A. Shaikh & Eldon Li, 2021. "Designing Recommendation or Suggestion Systems: looking to the future," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(2), pages 243-252, June.
    9. Chaudhary, Naveed Ishtiaq & Khan, Zeshan Aslam & Kiani, Adiqa Kausar & Raja, Muhammad Asif Zahoor & Chaudhary, Iqra Ishtiaq & Pinto, Carla M.A., 2022. "Design of auxiliary model based normalized fractional gradient algorithm for nonlinear output-error systems," Chaos, Solitons & Fractals, Elsevier, vol. 163(C).
    10. Elnady, Sroor M. & El-Beltagy, Mohamed & Radwan, Ahmed G. & Fouda, Mohammed E., 2025. "A comprehensive survey of fractional gradient descent methods and their convergence analysis," Chaos, Solitons & Fractals, Elsevier, vol. 194(C).
    11. Khan, Zeshan Aslam & Chaudhary, Naveed Ishtiaq & Raja, Muhammad Asif Zahoor, 2022. "Generalized fractional strategy for recommender systems with chaotic ratings behavior," Chaos, Solitons & Fractals, Elsevier, vol. 160(C).
    12. Yin Zhang & Haider Abbas & Yi Sun, 2019. "Smart e-commerce integration with recommender systems," Electronic Markets, Springer;IIM University of St. Gallen, vol. 29(2), pages 219-220, June.

    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:wly:complx:v:2022:y:2022:i:1:n:9244890. 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: Wiley Content Delivery (email available below). General contact details of provider: https://onlinelibrary.wiley.com/journal/8503 .

    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.