Time series forecasting by means of SOM aided Fuzzy Inference Systems

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 proce...

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Bibliographic Details
Main Authors: Zurita, Daniel, Carino, Jesus A., Sala, Enric, Delgado-Prieto, Miguel, Ortega, Juan A.
Format: Conference Proceeding
Language:English
Subjects:
Artificial intelligence
Condition monitoring
Data models
Forecasting
Fuzzy logic
Fuzzy neural networks
Machine learning
Manufacturing
Neurons
Predictive models
Prognosis
Time series analysis
Training
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Summary: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.
DOI:10.1109/ICIT.2015.7125354