Steepening Squared Error Function Facilitates Online Adaptation of Gaussian Scales

Masa Aki Takizawa, Masahiro Yukawa

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

Abstract

We previously proposed a joint learning scheme of Gaussian parameters (scales and centers) and coefficients for online nonlinear estimation. The instantaneous squared error cost in terms of the Gaussian scales, however, tends to have shallow slopes when the initial guess is far from optimal, causing extremely slow convergence. In this paper, we propose steepening the cost function by adding a squared distance function from the instantaneously-optimal scale. Numerical examples show that the use of the steepened cost ameliorates the convergence behaviors of the scale parameters in inappropriate initial-scale settings.

Original languageEnglish
Title of host publication2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages5450-5454
Number of pages5
ISBN (Electronic)9781509066315
DOIs
Publication statusPublished - 2020 May
Event2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Barcelona, Spain
Duration: 2020 May 42020 May 8

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2020-May
ISSN (Print)1520-6149

Conference

Conference2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020
CountrySpain
CityBarcelona
Period20/5/420/5/8

Keywords

  • automatic parameter tuning
  • Gaussian function
  • nonlinear estimation
  • online learning

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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  • Cite this

    Takizawa, M. A., & Yukawa, M. (2020). Steepening Squared Error Function Facilitates Online Adaptation of Gaussian Scales. In 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings (pp. 5450-5454). [9054092] (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2020-May). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP40776.2020.9054092