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Industrial time series modelling by means of the neo-fuzzy neuron

Author
Zurita, D.; Delgado Prieto, M.; Cariño , J.A.; Ortega, J.A.; Clerc, G.
Type of activity
Journal article
Journal
IEEE access
Date of publication
2016-09-20
DOI
https://doi.org/10.1109/ACCESS.2016.2611649 Open in new window
Repository
http://hdl.handle.net/2117/99534 Open in new window
URL
http://ieeexplore.ieee.org/document/7572156/ Open in new window
Abstract
Abstract—Industrial process monitoring and modelling represents a critical step in order to achieve the paradigm of Zero Defect Manufacturing. The aim of this paper is to introduce the Neo-Fuzzy Neuron method to be applied in industrial time series modelling. Its open structure and input independency provides fast learning and convergence capabilities, while assuring a proper accuracy and generalization in the modelled output. First, the auxiliary signals in the database are analyzed in order ...
Citation
Zurita, D., Delgado Prieto, M., Cariño , J.A., Ortega, J.A., Clerc, G. Industrial time series modelling by means of the neo-fuzzy neuron. "IEEE access", 20 Setembre 2016.
Keywords
Artificial intelligence, Forecasting, Fuzzy neural networks, Industrial plants, Predictive models, Time series analysis.
Group of research
MCIA - Motion Control and Industrial Applications Research Group
PERC-UPC - Power Electronics Research Centre

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