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Title: Piecewise approximation and neural networks (English)
Author: Révayová, Martina
Author: Török, Csaba
Language: English
Journal: Kybernetika
ISSN: 0023-5954
Volume: 43
Issue: 4
Year: 2007
Pages: 547-559
Summary lang: English
Category: math
Summary: The paper deals with the recently proposed autotracking piecewise cubic approximation (APCA) based on the discrete projective transformation, and neural networks (NN). The suggested new approach facilitates the analysis of data with complex dependence and relatively small errors. We introduce a new representation of polynomials that can provide different local approximation models. We demonstrate how APCA can be applied to especially noisy data thanks to NN and local estimations. On the other hand, the new approximation method also has its impact on neural networks. We show how APCA helps to decrease the computation time of feed forward NN. (English)
Keyword: data smoothing
Keyword: least squares and related methods
Keyword: linear regression
Keyword: approximation by polynomials
Keyword: neural networks
MSC: 41A10
MSC: 62J05
MSC: 62M45
MSC: 68T05
MSC: 93E14
MSC: 93E24
idZBL: Zbl 1145.68495
idMR: MR2377932
Date available: 2009-09-24T20:26:49Z
Last updated: 2012-06-06
Stable URL:
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