Modelling Chaotic Systems with Neural Networks: Application to Seismic Event Predicting in Gold Mines
نویسندگان
چکیده
This thesis explores the use of neural networks for predicting difficult, real-world time series. We first establish and demonstrate methods for characterising, modelling and predicting well-known systems. The real-world system we explore is seismic event data obtained from a South African gold mine. We show that this data is chaotic. After preprocessing the raw data, we show that neural networks are able to predict seismic activity reasonably well.
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