Time series forecasting by means of SOM aided Fuzzy Inference Systems

Daniel Zurita; Jesús A. Carino; Enric Sala; Miguel Delgado-Prieto; Juan A. Ortega

doi:10.1109/ICIT.2015.7125354

Time series forecasting by means of SOM aided Fuzzy Inference Systems

Daniel Zurita
, Jesús A. Carino
, Enric Sala
, Miguel Delgado-Prieto
, Juan A. Ortega

Polytechnic University of Catalonia

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

2 Citas (Scopus)

Resumen

The forecast of industrial process time series represents a critical factor in order to assure a proper operation of the whole manufacturing chain, as it allows to act against any process deviation before it affects the final manufactured product. In this paper, in order to take advantage from process relations and improve forecasting performance, a prediction method based in Adaptive Neuro Fuzzy Inference System (ANFIS) and Self-Organizing Maps is presented. The novelties of the proposed method are based on considering, as an input of an ANFIS model, the interrelations of process variables regarding the signal that wants to be forecasted, by means of topology preservation SOM. An experimental study performed with real industrial data from a cooper manufacturing plant indicated the suitability of the proposed method in time series forecasting applications.

Idioma original	Inglés
Título de la publicación alojada	2015 IEEE International Conference on Industrial Technology, ICIT 2015
Editorial	Institute of Electrical and Electronics Engineers Inc.
Páginas	1772-1778
Número de páginas	7
Edición	June
ISBN (versión digital)	9781479978007
DOI	https://doi.org/10.1109/ICIT.2015.7125354
Estado	Publicada - 16 jun 2015
Publicado de forma externa	Sí
Evento	2015 IEEE International Conference on Industrial Technology, ICIT 2015 - Seville, Espana Duración: 17 mar 2015 → 19 mar 2015

Serie de la publicación

Nombre	Proceedings of the IEEE International Conference on Industrial Technology
Número	June
Volumen	2015-June

Conferencia

Conferencia	2015 IEEE International Conference on Industrial Technology, ICIT 2015
País/Territorio	Espana
Ciudad	Seville
Período	17/03/15 → 19/03/15

ODS de las Naciones Unidas

Este resultado contribuye a los siguientes Objetivos de Desarrollo Sostenible

ODS 9: Industria, innovación e infraestructura

Acceder al documento

10.1109/ICIT.2015.7125354

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Zurita, D., Carino, J. A., Sala, E., Delgado-Prieto, M., & Ortega, J. A. (2015). Time series forecasting by means of SOM aided Fuzzy Inference Systems. En 2015 IEEE International Conference on Industrial Technology, ICIT 2015 (June ed., pp. 1772-1778). Artículo 7125354 (Proceedings of the IEEE International Conference on Industrial Technology; Vol. 2015-June, N.º June). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICIT.2015.7125354

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title = "Time series forecasting by means of SOM aided Fuzzy Inference Systems",

abstract = "The forecast of industrial process time series represents a critical factor in order to assure a proper operation of the whole manufacturing chain, as it allows to act against any process deviation before it affects the final manufactured product. In this paper, in order to take advantage from process relations and improve forecasting performance, a prediction method based in Adaptive Neuro Fuzzy Inference System (ANFIS) and Self-Organizing Maps is presented. The novelties of the proposed method are based on considering, as an input of an ANFIS model, the interrelations of process variables regarding the signal that wants to be forecasted, by means of topology preservation SOM. An experimental study performed with real industrial data from a cooper manufacturing plant indicated the suitability of the proposed method in time series forecasting applications.",

keywords = "Artificial intelligence, Condition monitoring, Fuzzy neural networks, Machine learning, Predictive models, Prognosis, Time series analysis",

author = "Daniel Zurita and Carino, \{Jes{\'u}s A.\} and Enric Sala and Miguel Delgado-Prieto and Ortega, \{Juan A.\}",

note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 2015 IEEE International Conference on Industrial Technology, ICIT 2015 ; Conference date: 17-03-2015 Through 19-03-2015",

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Zurita, D, Carino, JA, Sala, E, Delgado-Prieto, M & Ortega, JA 2015, Time series forecasting by means of SOM aided Fuzzy Inference Systems. En 2015 IEEE International Conference on Industrial Technology, ICIT 2015. June ed., 7125354, Proceedings of the IEEE International Conference on Industrial Technology, n.º June, vol. 2015-June, Institute of Electrical and Electronics Engineers Inc., pp. 1772-1778, 2015 IEEE International Conference on Industrial Technology, ICIT 2015, Seville, Espana, 17/03/15. https://doi.org/10.1109/ICIT.2015.7125354

Time series forecasting by means of SOM aided Fuzzy Inference Systems. / Zurita, Daniel; Carino, Jesús A.; Sala, Enric et al.
2015 IEEE International Conference on Industrial Technology, ICIT 2015. June. ed. Institute of Electrical and Electronics Engineers Inc., 2015. p. 1772-1778 7125354 (Proceedings of the IEEE International Conference on Industrial Technology; Vol. 2015-June, N.º June).

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

TY - GEN

T1 - Time series forecasting by means of SOM aided Fuzzy Inference Systems

AU - Zurita, Daniel

AU - Carino, Jesús A.

AU - Sala, Enric

AU - Delgado-Prieto, Miguel

AU - Ortega, Juan A.

PY - 2015/6/16

Y1 - 2015/6/16

N2 - The forecast of industrial process time series represents a critical factor in order to assure a proper operation of the whole manufacturing chain, as it allows to act against any process deviation before it affects the final manufactured product. In this paper, in order to take advantage from process relations and improve forecasting performance, a prediction method based in Adaptive Neuro Fuzzy Inference System (ANFIS) and Self-Organizing Maps is presented. The novelties of the proposed method are based on considering, as an input of an ANFIS model, the interrelations of process variables regarding the signal that wants to be forecasted, by means of topology preservation SOM. An experimental study performed with real industrial data from a cooper manufacturing plant indicated the suitability of the proposed method in time series forecasting applications.

AB - The forecast of industrial process time series represents a critical factor in order to assure a proper operation of the whole manufacturing chain, as it allows to act against any process deviation before it affects the final manufactured product. In this paper, in order to take advantage from process relations and improve forecasting performance, a prediction method based in Adaptive Neuro Fuzzy Inference System (ANFIS) and Self-Organizing Maps is presented. The novelties of the proposed method are based on considering, as an input of an ANFIS model, the interrelations of process variables regarding the signal that wants to be forecasted, by means of topology preservation SOM. An experimental study performed with real industrial data from a cooper manufacturing plant indicated the suitability of the proposed method in time series forecasting applications.

KW - Artificial intelligence

KW - Condition monitoring

KW - Fuzzy neural networks

KW - Machine learning

KW - Predictive models

KW - Prognosis

KW - Time series analysis

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BT - 2015 IEEE International Conference on Industrial Technology, ICIT 2015

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Zurita D, Carino JA, Sala E, Delgado-Prieto M, Ortega JA. Time series forecasting by means of SOM aided Fuzzy Inference Systems. En 2015 IEEE International Conference on Industrial Technology, ICIT 2015. June ed. Institute of Electrical and Electronics Engineers Inc. 2015. p. 1772-1778. 7125354. (Proceedings of the IEEE International Conference on Industrial Technology; June). doi: 10.1109/ICIT.2015.7125354

Time series forecasting by means of SOM aided Fuzzy Inference Systems

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