Finite Memory Estimation-Based Recurrent Neural Network Learning Algorithm for Accurate Identification of Unknown Nonlinear Systems

Hyun Ho Kang, Sang Su Lee, Kwan Soo Kim, Choon Ki Ahn

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    2 Citations (Scopus)

    Abstract

    This paper proposes a novel finite memory estimation based learning algorithm (FME-LA) for recurrent neural networks (RNNs) to accurately identify unknown nonlinear systems. The proposed algorithm, FME-LA, is designed through finite memory estimation (FME) whose gain is obtained under the unbiased condition by minimizing the Frobenius norm. The FME is designed on the concept of the horizon to decide how many recent measurements are considered and maintain a finite memory structure of the learning algorithm. Therefore, the proposed algorithm provides accurate performance and fast convergence for the system identification of unknown nonlinear systems under rapidly or smoothly changing circumstances with inaccurate information, such as modeling uncertainties or incorrect noise statistics. We confirm fast convergence and accurate performance of the proposed algorithm through simulation and experimental results.

    Original languageEnglish
    Title of host publication2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages470-475
    Number of pages6
    ISBN (Electronic)9781728124858
    DOIs
    Publication statusPublished - 2019 Dec
    Event2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019 - Xiamen, China
    Duration: 2019 Dec 62019 Dec 9

    Publication series

    Name2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019

    Conference

    Conference2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019
    Country/TerritoryChina
    CityXiamen
    Period19/12/619/12/9

    Bibliographical note

    Funding Information:
    This work was supported in part by the National Research Foundation of Korea through the Ministry of Science, ICT, and Future Planning under Grant NRF-2017R1A1A1A05001325, and in part by Human Resources Program in Energy Technology of the Korea Institute of Energy Technology Evaluation and Planning (KETEP) granted financial resource from the Ministry of Trade, Industry & Energy, Republic of Korea (No.20174030201820).

    Publisher Copyright:
    © 2019 IEEE.

    Keywords

    • Finite memory structure
    • finite memory estimator
    • learning algorithm
    • recurrent neural network
    • unknown nonlinear system identification

    ASJC Scopus subject areas

    • Artificial Intelligence
    • Computer Science Applications
    • Modelling and Simulation

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