Thesis (MSc (Computer Science))-- University of Stellenbosch, 2001. / ENGLISH ABSTRACT: 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. / AFRIKAANSE OPSOMMING: Hierdie tesis ondersoek die gebruik van neurale netwerke om komplekse, werklik bestaande tydreekse
te voorspel. Ter aanvang noem en demonstreer ons metodes vir die karakterisering, modelering
en voorspelling van bekende stelsels. Ons gaan dan voort en ondersoek seismiese gebeurlikheidsdata
afkomstig van ’n Suid-Afrikaanse goudmyn. Ons wys dat die data chaoties van aard
is. Nadat ons die rou data verwerk, wys ons dat neurale netwerke die tydreekse redelik goed kan
voorspel. / Integrated Seismic Systems International
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:sun/oai:scholar.sun.ac.za:10019.1/4580 |
| Date | 12 1900 |
| Creators | Van Zyl, Jacobus |
| Contributors | Omlin, Christian W., Van der Walt, Andries P. J., University of Stellenbosch. Faculty of Science. Dept. of Mathematical Sciences. Computer Science. |
| Publisher | Stellenbosch : University of Stellenbosch |
| Source Sets | South African National ETD Portal |
| Language | en_ZA |
| Detected Language | Unknown |
| Type | Thesis |
| Rights | University of Stellenbosch |
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