ISSN:
1573-7497
Keywords:
Bayesian networks
;
machine learning
;
waste water treatment plants
Source:
Springer Online Journal Archives 1860-2000
Topics:
Computer Science
Notes:
Abstract Bayesian Networks have been proposed as an alternative to rule-based systems in domains with uncertainty. Applications in monitoring and control can benefit from this form of knowledge representation. Following the work of Chong and Walley, we explore the possibilities of Bayesian Networks in the Waste Water Treatment Plants (WWTP) monitoring and control domain. We show the advantages of modelling knowledge in such a domain by means of Bayesian networks, put forth new methods for knowledge acquisition, describe their applications to a real waste water treatment plant and comment on the results. We also show how a Bayesian Network learning environment was used in the process and which characteristics of data in the domain suggested new ways of representing knowledge in network form but with uncertainty representations formalisms other than probability. The results of applying a possibilistic extension of current learning methods are also shown and compared.
Type of Medium:
Electronic Resource
URL:
http://dx.doi.org/10.1023/A:1008375228885
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