Journal
NEUROCOMPUTING
Volume 62, Issue -, Pages 427-440Publisher
ELSEVIER
DOI: 10.1016/j.neucom.2004.06.002
Keywords
acoustic diagnosis; gas leakage; modular neural networks; radial basis function networks; sound
Categories
Ask authors/readers for more resources
It is important to detect flammable or poisonous gas leaked from the cracks in pipes of petroleum refining plants or chemical plants. We applied a novel strategy of construction of neural network to the acoustic diagnosis technique for the gas leakage. An example of the modular neural network to realize the strategy is able to adapt its structure according to the dynamic environment. Experiments were performed for an artificial gas leakage device under various experimental conditions over about 18 months in a petroleum refining plant. Experimental results showed that the proposed network could adapt the structure to changes in environments and its performance was superior to that of feed-forward networks with the re-training strategy. From these results, we confirmed the effectiveness of the modular neural network for practical use. (C) 2004 Elsevier B.V. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available