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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
R. C. Jeffrey, The Logic of Decision, Gordon and Breach, New York, 1965.
J. Lawry, A Framework for Linguistic Modelling, Artificial Intelligence, Vol. 155, pp 1–39, 2004.
J. Lawry, Modelling and Reasoning with Vague Concepts, Springer, 2006.
D.D. Lewis, Naïve (Bayes) at Forty: The Independence Assumption in Information Retrieval, Machine Learning ECML-98, LNAI 1398, 1998, pp 4–15.
N.J. Randon, J. Lawry, Linguistic Modelling using a Semi-naïve Bayes Framework, In Proceedings of IPMU 2002, pp 1243–1250, 2002.
N.J. Randon, J. Lawry, D. Han, I.D. Cluckie, River Flow Modelling based on Fuzzy Labels, In Proceedings of IPMU 2004, 2004.
R.J. Romanowicz, P. C. Young, K. J. Beven, Data Assimilation in the Identification of Flood Inundation Models: Derivation of On-line Multi-step ahead predictions of Flows, In Hydrology: Science and Practice for the 21st Century, Vol. 1, pp 348–353, 2004.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer
About this chapter
Cite this chapter
Randon, N., Lawry, J., Cluckie, I. (2006). Online Learning for Fuzzy Bayesian Prediction. In: Lawry, J., et al. Soft Methods for Integrated Uncertainty Modelling. Advances in Soft Computing, vol 37. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-34777-1_47
Download citation
DOI: https://doi.org/10.1007/3-540-34777-1_47
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34776-7
Online ISBN: 978-3-540-34777-4
eBook Packages: EngineeringEngineering (R0)