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- W2393366430 abstract "Artificial Neural Network (ANN) method was used to improve the solution of power electronic devices during application design, and to analyze the nonlinear relationship between the periphery circuit parameters and the switching characteristics of large capacity GTOs (Gate Turn off thyristors), including power losses, turn on and turn off times, and the rise rate of device voltages and currents. ANN training error was ±5% approximately. An equivalent heat transfer model for GTOs, named the Foster network is fit to the results using steepest descent theory with a model error of less than ±3% compared with experimentally measured thermal response curves. All the algorithms are combined into an integrated analysis software for GTOs switching characteristics, which provides a convenient analysis platform for both GTOs dynamic switching behavior investigations and practical application system design." @default.
- W2393366430 created "2016-06-24" @default.
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- W2393366430 date "2000-01-01" @default.
- W2393366430 modified "2023-09-25" @default.
- W2393366430 title "Integrated analysis system for switching characteristics of large capacity GTOs" @default.
- W2393366430 hasPublicationYear "2000" @default.
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