A novel algorithm for the modeling of complex processes

José de Jesús Rubio; Edwin Lughofer; Angelov Plamen; Juan Francisco Novoa; Jesús A. Meda-Campaña

Kybernetika (2018)

  • Volume: 54, Issue: 1, page 79-95
  • ISSN: 0023-5954

Abstract

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In this investigation, a new algorithm is developed for the updating of a neural network. It is concentrated in a fuzzy transition between the recursive least square and extended Kalman filter algorithms with the purpose to get a bounded gain such that a satisfactory modeling could be maintained. The advised algorithm has the advantage compared with the mentioned methods that it eludes the excessive increasing or decreasing of its gain. The gain of the recommended algorithm is uniformly stable and its convergence is found. The new algorithm is employed for the modeling of two synthetic examples.

How to cite

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Rubio, José de Jesús, et al. "A novel algorithm for the modeling of complex processes." Kybernetika 54.1 (2018): 79-95. <http://eudml.org/doc/294558>.

@article{Rubio2018,
abstract = {In this investigation, a new algorithm is developed for the updating of a neural network. It is concentrated in a fuzzy transition between the recursive least square and extended Kalman filter algorithms with the purpose to get a bounded gain such that a satisfactory modeling could be maintained. The advised algorithm has the advantage compared with the mentioned methods that it eludes the excessive increasing or decreasing of its gain. The gain of the recommended algorithm is uniformly stable and its convergence is found. The new algorithm is employed for the modeling of two synthetic examples.},
author = {Rubio, José de Jesús, Lughofer, Edwin, Plamen, Angelov, Novoa, Juan Francisco, Meda-Campaña, Jesús A.},
journal = {Kybernetika},
keywords = {recursive least square; Kalman filter; modeling; complex processes},
language = {eng},
number = {1},
pages = {79-95},
publisher = {Institute of Information Theory and Automation AS CR},
title = {A novel algorithm for the modeling of complex processes},
url = {http://eudml.org/doc/294558},
volume = {54},
year = {2018},
}

TY - JOUR
AU - Rubio, José de Jesús
AU - Lughofer, Edwin
AU - Plamen, Angelov
AU - Novoa, Juan Francisco
AU - Meda-Campaña, Jesús A.
TI - A novel algorithm for the modeling of complex processes
JO - Kybernetika
PY - 2018
PB - Institute of Information Theory and Automation AS CR
VL - 54
IS - 1
SP - 79
EP - 95
AB - In this investigation, a new algorithm is developed for the updating of a neural network. It is concentrated in a fuzzy transition between the recursive least square and extended Kalman filter algorithms with the purpose to get a bounded gain such that a satisfactory modeling could be maintained. The advised algorithm has the advantage compared with the mentioned methods that it eludes the excessive increasing or decreasing of its gain. The gain of the recommended algorithm is uniformly stable and its convergence is found. The new algorithm is employed for the modeling of two synthetic examples.
LA - eng
KW - recursive least square; Kalman filter; modeling; complex processes
UR - http://eudml.org/doc/294558
ER -

References

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