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Neural Network Learning for Blind Source Separation With Application in Dam Safety Monitoring

Domenii publicaţii > Stiinte ingineresti + Tipuri publicaţii > Articol în volumul unei conferinţe

Autori: Th. D. Popescu

Editorial: Series Lecture Notes in Computer Science, LNCS 7666, Subseries Theoretical Computer Science and General Issues, Springer Berlin Heiderberg, Proc. of The 19-th International Conference on Neural Information Processing (ICONIP 2012) , Doha, Qatar, November 12-15, 2012, IV, p.1-8, 2012.


Usually, dam monitoring systems are based on both boundary conditions (temperature, rainfall, water level, etc.) and structural responses. Statistical analysis tools are widely used to determine eventual unwanted behaviors. The main drawback of this approach is that the structural response quantities are related to the external loads using analytical functions, whose parameters do not have physical meaning. In this paper a new approach, based on a neural network learning rule for Blind Source Separation (BSS), to find out the contributions of the dam external loads is presented and applied in a case study for a concrete dam.

Cuvinte cheie: Blind source separation, neural learning rule, large dam monitoring, case study.