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Experimental design of supervisory control functions based on multilayer perceptrons

Published online by Cambridge University Press:  11 January 2002

DRAGAN D. KUKOLJ ,
MIROSLAVA T. BERKO-PUSIC  and
BRANISLAV ATLAGIC
DRAGAN D. KUKOLJ
Affiliation:
Faculty of Engineering, University of Novi Sad, Yugoslavia
MIROSLAVA T. BERKO-PUSIC
Affiliation:
Faculty of Engineering, University of Novi Sad, Yugoslavia
BRANISLAV ATLAGIC
Affiliation:
Faculty of Engineering, University of Novi Sad, Yugoslavia
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Abstract

This article presents the results of research concerning possibilities of applying multilayer perceptron type of neural network for fault diagnosis, state estimation, and prediction in the gas pipeline transmission network. The influence of several factors on accuracy of the multilayer perceptron was considered. The emphasis was put on the multilayer perceptrons' function as a state estimator. The choice of the most informative features, the amount and sampling period of training data sets, as well as different configurations of multilayer perceptrons were analyzed.

Keywords

Cross ValidationMultilayer PerceptronNeural NetworksSupervisory Control
Type
Research Article
Information
AI EDAM , Volume 15 , Issue 5 , November 2001 , pp. 425 - 431
Copyright
2001 Cambridge University Press

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