Design of supervisory control functions based on feedforward neural networks

Abstract

This paper presents the results of the research concerning possibilities of applying artificial neural networks for fault diagnosis, state estimation, and prediction in the gas pipeline transmission network. The influence of several factors on accuracy of the feedforward neural networks so applied was considered. The emphasis was put on neural network's function as state estimator. The choice of the most informative features, the amount and sampling period of training data sets, as well as different configurations of neural networks were analyzed. © 2000 Taylor and Francis, LLC.