Name
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. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/ICIT.2015.7125354 | Industrial Technology |
Keywords | DocType | Citations |
artificial intelligence,condition monitoring,fuzzy neural networks,machine learning,predictive models,prognosis,time series analysis,forecasting,fuzzy logic,manufacturing,data models | Conference | 0 |
PageRank | References | Authors |
0.34 | 8 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| daniel zurita | 1 | 0 | 0.34 |
| jesus a carino | 2 | 0 | 0.34 |
| Sala, E. | 3 | 1 | 1.02 |
| miguel delgadoprieto | 4 | 0 | 0.68 |
| Juan Antonio Ortega Redondo | 5 | 208 | 29.56 |