Predicting chaotic sequences in a blast furnace by a generalized radial basis function network

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作者：
Sumitomo Metal Industries, Ltd, Amagasaki, Japan [1 ]
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Electron Commun Jpn Part III Fundam Electron Sci | / 7卷 / 1-10期
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Blast furnaces - Dynamics - Mathematical models - Neural networks - Time series analysis;
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摘要：
Nonlinear forecasting is applied to characterize the complexities in time series of temperature fluctuations and pressure fluctuations observed in a blast furnace. A generalized radial basis function network and a Sugihara-May predictor are used as the predictive models. The temperature sequence may be diagnosed as low-dimensional chaos according to the scaling property of the prediction error as a function of the prediction-time interval. Short-term forecasts about the temperature sequence are successfully made by the network that has learned the underlying dynamics embedded in three-dimensional delayed space.
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