ABSTRACT This paper introduces a kind of diagnosis principle and learning algorithm of steam turbine fault diagnosis which based on Elman neural network. Comparing the results of the Elman neural network and the traditional BP neural network diagnosis, the results shows that Elman neural network is an effective way to improve the learning speed , effectively suppress the minimum defects that the traditional neural network easily trapped in, and shorten the autonomous learning time. All these proves that the Elman neural network is an effective way to diagnose the steam turbine.
Cite this paper
J. Gao and Y. Wang, "The Research on the Methods of Diagnosing the Steam Turbine Based on the Elman Neural Network," Journal of Software Engineering and Applications, Vol. 6 No. 3, 2013, pp. 87-90. doi: 10.4236/jsea.2013.63B019.
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