Abstract
Many complex systems have characteristics which vary over time. Consider for example, the problem of modelling a river as the seasons change or adjusting the setup of a machine as it ages, to enable it to stay within predefined tolerances. In such cases offline learning limits the capability of an algorithm to accurately capture a dynamic system, since it can only base predictions on events that were encountered during the learning process. Model updating is therefore required to allow the model to change over time and to adapt to previously unseen events. In the sequel we introduce an extended version of the fuzzy Bayesian prediction algorithm [6] which learns models incorporating both uncertainty and fuzziness. This extension allows an initial model to be updated as new data becomes available. The potential of this approach will be demonstrated on a real-time flood prediction problem for the River Severn in the UK.
| Translated title of the contribution | Online Learning for Fuzzy Bayesian Prediction |
|---|---|
| Original language | English |
| Title of host publication | Soft Methods for Integrated Uncertainty Modelling (Advances in Soft Computing, 37) |
| Editors | J Lawry, E Miranda, A Bugarin, S Li, MA Gil, P Grzegorzewski, O Hyrniewicz |
| Publisher | Springer |
| Pages | 405 - 412 |
| Number of pages | 8 |
| ISBN (Print) | 9783540347767 |
| Publication status | Published - 2006 |
Bibliographical note
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Cite this
Randon, NJ., Lawry, J., & Cluckie, ID. (2006). Online Learning for Fuzzy Bayesian Prediction. In J. Lawry, E. Miranda, A. Bugarin, S. Li, MA. Gil, P. Grzegorzewski, & O. Hyrniewicz (Eds.), Soft Methods for Integrated Uncertainty Modelling (Advances in Soft Computing, 37) (pp. 405 - 412). Springer. http://www.springerlink.com/content/xg2135070609j828/