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A novel algorithm for the modelling of complex processes

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A novel algorithm for the modelling of complex processes. / Rubio, José de Jesús; Lughofer, Edwin; Angelov, Plamen Parvanov et al.
In: Kybernetika, Vol. 54, No. 1, 01.04.2018, p. 79-95.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Rubio, JDJ, Lughofer, E, Angelov, PP, Novoa, JF & Meda-Campana, JA 2018, 'A novel algorithm for the modelling of complex processes', Kybernetika, vol. 54, no. 1, pp. 79-95. https://doi.org/10.14736/kyb-2018-1-0079

APA

Rubio, J. D. J., Lughofer, E., Angelov, P. P., Novoa, J. F., & Meda-Campana, J. A. (2018). A novel algorithm for the modelling of complex processes. Kybernetika, 54(1), 79-95. https://doi.org/10.14736/kyb-2018-1-0079

Vancouver

Rubio JDJ, Lughofer E, Angelov PP, Novoa JF, Meda-Campana JA. A novel algorithm for the modelling of complex processes. Kybernetika. 2018 Apr 1;54(1):79-95. doi: 10.14736/kyb-2018-1-0079

Author

Rubio, José de Jesús ; Lughofer, Edwin ; Angelov, Plamen Parvanov et al. / A novel algorithm for the modelling of complex processes. In: Kybernetika. 2018 ; Vol. 54, No. 1. pp. 79-95.

Bibtex

@article{88b8ba4693d24db1a1a0928f83ba568e,
title = "A novel algorithm for the modelling of complex processes",
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, {Jos{\'e} de Jes{\'u}s} and Edwin Lughofer and Angelov, {Plamen Parvanov} and Novoa, {J. F.} and Meda-Campana, {J. A.}",
year = "2018",
month = apr,
day = "1",
doi = "10.14736/kyb-2018-1-0079",
language = "English",
volume = "54",
pages = "79--95",
journal = "Kybernetika",
issn = "1805-949X",
publisher = "Czech Academy of Sciences",
number = "1",

}

RIS

TY - JOUR

T1 - A novel algorithm for the modelling of complex processes

AU - Rubio, José de Jesús

AU - Lughofer, Edwin

AU - Angelov, Plamen Parvanov

AU - Novoa, J. F.

AU - Meda-Campana, J. A.

PY - 2018/4/1

Y1 - 2018/4/1

N2 - 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.

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.

U2 - 10.14736/kyb-2018-1-0079

DO - 10.14736/kyb-2018-1-0079

M3 - Journal article

VL - 54

SP - 79

EP - 95

JO - Kybernetika

JF - Kybernetika

SN - 1805-949X

IS - 1

ER -