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    Early stopping criteria for adaptive training of dynamic nonlinear behavioural models


    Loughman, Meabh and Farrell, Ronan and Dooley, John (2019) Early stopping criteria for adaptive training of dynamic nonlinear behavioural models. In: 2019 30th Irish Signals and Systems Conference (ISSC). IEEE, pp. 1-5. ISBN 9781728128009

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    Abstract

    As the physical makeup of cellular basestations evolve into systems with multiple parallel transmission paths the effort involved in modelling these complex systems increases considerably. One task in particular which contributes to signal distortion on each signal path, is the power amplifier. In power amplifier (PA) modelling, Recursive Least Squares (RLS) has been used in the past to train Volterra models with memory terms. The Volterra model is widely used for modelling of PAs. In this paper we present a comparison of the stability performance for a PA model during training for various model memory lengths, model orders of non linearity and signal sample rates. This examination provides a technique to avoid instability occurring during the adaptive training of dynamic nonlinear behavioural models.

    Item Type: Book Section
    Additional Information: Funding: This publication has emanated from research conducted with the financial support of Science Foundation Ireland (SFI) and is co-funded under the European Regional Development Fund under Grant Number 13/RC/2077. Cite as: M. Loughman, R. Farrell and J. Dooley, "Early stopping criteria for adaptive training of dynamic nonlinear behavioural models," 2019 30th Irish Signals and Systems Conference (ISSC), Maynooth, Ireland, 2019, pp. 1-5, doi: 10.1109/ISSC.2019.8904923.
    Keywords: early stopping; criteria; adaptive training; dynamic; nonlinear; models;
    Academic Unit: Faculty of Science and Engineering > Electronic Engineering
    Faculty of Science and Engineering > Research Institutes > Hamilton Institute
    Item ID: 14205
    Identification Number: https://doi.org/10.1109/ISSC.2019.8904923
    Depositing User: Ronan Farrell
    Date Deposited: 18 Mar 2021 16:19
    Publisher: IEEE
    Refereed: Yes
    Funders: Science Foundation Ireland (SFI), European Regional Development Fund
    URI:

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