Knowledge-based neurocomputing for operational decision support

Bargiela, A., Arsene, C. and Tanaka, M., 2002. Knowledge-based neurocomputing for operational decision support. In: Knowledge-Based Intelligent Information Engineering Systems and Allied Technologies, Polo di Crema, Italy, 16-18 September 2002. Amsterdam: IOS Press, pp. 427-432. ISBN 4274905357

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Abstract

We discuss a neurocomputing system for operational decision support in water distribution networks. An analog neural network is used to calculate 'loop-equations based' state estimates. This is followed by the confidence limit analysis (CLA) of the calculated estimates and the general fuzzy min-max neural pattern classification/clustering (PC). The latter emulates the process of experience-building by human operators of water systems. We refer to the resulting neurocomputing system as CLA/PC. Water distribution systems are representative of a large and important class of systems that are primarily driven by external, incompletely defined stimuli yet, the operation of which needs to be optimised according to some well defined criteria. The operational control of such systems presents considerable challenge to operators who need to develop the ability to 'distil' the overall system state from a large number of fuzzy system snapshots (measurements and estimates) in order to decide on the appropriate control acti

Item Type: Chapter in book
Creators: Bargiela, A., Arsene, C. and Tanaka, M.
Publisher: IOS Press
Place of Publication: Amsterdam
Date: 2002
Volume: 1
Divisions: Schools > School of Science and Technology
Depositing User: EPrints Services
Date Added: 09 Oct 2015 11:08
Last Modified: 19 Oct 2015 14:41
URI: http://irep.ntu.ac.uk/id/eprint/23219

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