Piecewise-Linear Radial Basis Functions in Signal Processing

Carlos J. PANTALEÓN-PRIETO; Aníbal R. FIGUEIRAS-VIDAL

Piecewise-Linear Radial Basis Functions in Signal Processing

Carlos J. PANTALEÓN-PRIETO, Aníbal R. FIGUEIRAS-VIDAL

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In this paper we introduce the Piecewise Linear Radial Basis Function Model (PWL-RBFM), a new nonlinear model that uses the well known RBF framework to build a PWL functional approximation by combining an l₁ norm with a linear RBF function. A smooth generalization of the PWL-RBF is proposed: it is obtained by substituting the modulus function with the logistic function. These models are applied to several time series prediction tasks.

Publication: IEICE TRANSACTIONS on Fundamentals Vol.E77-A No.9 pp.1493-1496

Publication Date: 1994/09/25

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Type of Manuscript: Special Section LETTER (Special Section of Papers Selected from the 8th Digital Signal Processing Symposium)

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Carlos J. PANTALEÓN-PRIETO, Aníbal R. FIGUEIRAS-VIDAL, "Piecewise-Linear Radial Basis Functions in Signal Processing" in IEICE TRANSACTIONS on Fundamentals, vol. E77-A, no. 9, pp. 1493-1496, September 1994, doi: .
Abstract: In this paper we introduce the Piecewise Linear Radial Basis Function Model (PWL-RBFM), a new nonlinear model that uses the well known RBF framework to build a PWL functional approximation by combining an l₁ norm with a linear RBF function. A smooth generalization of the PWL-RBF is proposed: it is obtained by substituting the modulus function with the logistic function. These models are applied to several time series prediction tasks.
URL: https://globals.ieice.org/en_transactions/fundamentals/10.1587/e77-a_9_1493/_p

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@ARTICLE{e77-a_9_1493,
author={Carlos J. PANTALEÓN-PRIETO, Aníbal R. FIGUEIRAS-VIDAL, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Piecewise-Linear Radial Basis Functions in Signal Processing},
year={1994},
volume={E77-A},
number={9},
pages={1493-1496},
abstract={In this paper we introduce the Piecewise Linear Radial Basis Function Model (PWL-RBFM), a new nonlinear model that uses the well known RBF framework to build a PWL functional approximation by combining an l₁ norm with a linear RBF function. A smooth generalization of the PWL-RBF is proposed: it is obtained by substituting the modulus function with the logistic function. These models are applied to several time series prediction tasks.},
keywords={},
doi={},
ISSN={},
month={September},}

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TY - JOUR
TI - Piecewise-Linear Radial Basis Functions in Signal Processing
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1493
EP - 1496
AU - Carlos J. PANTALEÓN-PRIETO
AU - Aníbal R. FIGUEIRAS-VIDAL
PY - 1994
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E77-A
IS - 9
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - September 1994
AB - In this paper we introduce the Piecewise Linear Radial Basis Function Model (PWL-RBFM), a new nonlinear model that uses the well known RBF framework to build a PWL functional approximation by combining an l₁ norm with a linear RBF function. A smooth generalization of the PWL-RBF is proposed: it is obtained by substituting the modulus function with the logistic function. These models are applied to several time series prediction tasks.
ER -