Piecewise approximation and neural networks

Révayová, Martina, and Török, Csaba. "Piecewise approximation and neural networks." Kybernetika 43.4 (2007): 547-559. <http://eudml.org/doc/33879>.

@article{Révayová2007,
abstract = {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.},
author = {Révayová, Martina, Török, Csaba},
journal = {Kybernetika},
keywords = {data smoothing; least squares and related methods; linear regression; approximation by polynomials; neural networks; Autotracking Piecewise Cubic Approximation; data smoothing},
language = {eng},
number = {4},
pages = {547-559},
publisher = {Institute of Information Theory and Automation AS CR},
title = {Piecewise approximation and neural networks},
url = {http://eudml.org/doc/33879},
volume = {43},
year = {2007},
}

TY - JOUR
AU - Révayová, Martina
AU - Török, Csaba
TI - Piecewise approximation and neural networks
JO - Kybernetika
PY - 2007
PB - Institute of Information Theory and Automation AS CR
VL - 43
IS - 4
SP - 547
EP - 559
AB - 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.
LA - eng
KW - data smoothing; least squares and related methods; linear regression; approximation by polynomials; neural networks; Autotracking Piecewise Cubic Approximation; data smoothing
UR - http://eudml.org/doc/33879
ER -