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- W2137969395 abstract "The studies analyzed the idea of Levenberg-Marquardt (LM) algorithm, and also improved genetic algorithm (GA), finally developing an adaptive model of neural algorithm through the GA-LM. This model was applied to the fault diagnoses for power transformers. The results show that the adaptive GALM neural model is able to overcome its local minimum and increase the converging speed in comparison with BP. Meanwhile, the model can diagnose the faults of transformers efficiently and also increase the ratio of fault recognition greatly. The model is supposed to have a reference value in fault diagnoses for similar electrical equipment." @default.
- W2137969395 created "2016-06-24" @default.
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- W2137969395 date "2008-01-01" @default.
- W2137969395 modified "2023-09-26" @default.
- W2137969395 title "An adaptive GALM neural model and its application for fault diagnoses" @default.
- W2137969395 cites W2108995755 @default.
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- W2137969395 doi "https://doi.org/10.1109/wcica.2008.4594409" @default.
- W2137969395 hasPublicationYear "2008" @default.
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