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.
N1 - Publisher Copyright:
© 2015 IEEE.
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
UR - https://www.scopus.com/pages/publications/84937696196
U2 - 10.1109/ICIT.2015.7125354
DO - 10.1109/ICIT.2015.7125354
M3 - Conference contribution
AN - SCOPUS:84937696196
T3 - Proceedings of the IEEE International Conference on Industrial Technology
SP - 1772
EP - 1778
BT - 2015 IEEE International Conference on Industrial Technology, ICIT 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2015 IEEE International Conference on Industrial Technology, ICIT 2015
Y2 - 17 March 2015 through 19 March 2015
ER -
