Abstract
Results from studies on the application of the Adaptive Learning Network (ALN) technique for detecting sodium boiling noise are presented. The technique is tested on data from in-pile boiling experiments and from out-of-pile boiling experiments. A new approach to ALN technique where knowledge of the background noise is adequate for training the Network is suggested and tested. The new approach is suitable for developing on-line sodium boiling noise detection systems in fast reactors.
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