According to the principle of structure reliability and the random character of load-strength and load-strength interference theory, the risk forecasting model based on the PNN(probabilistic neural network) is built for overhead lines load risk forecasting problem, using the meteorological typical characteristic value of extreme weather, wind speed, ice thickness, rain fall and temperature as input, using overhead lines load risk on the time scale of lines failures probability.
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Based on the principle of structure reliability and the random character and the interference theory of load-strength, the risk forecast model of the overhead lines load is built up using the probabilistic neural network (PNN). In this model, the values including wind speed, ice thickness, rain fall and temperature at the extreme weather are employed to forecast the overhead lines load risks on the time scale which are divided by the lines failures probability method ,
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According to the principle of structure reliability and the random character of load-strength and load-strength interference theory, the risk forecasting model based on the PNN(probabilistic neural network) is built for overhead lines load risk forecasting problem, using the meteorological typical characteristic value of extreme weather, wind speed, ice thickness, rain fall and temperature as input, using overhead lines load risk on the time scale of lines failures probability.
望您指正,谢谢!!
本人非英语及你的研究方向的专业人士,仅理顺了一下你的内容,仅供参考,若偏差,请谅解。
Based on the principle of structure reliability and the random character and the interference theory of load-strength, the risk forecast model of the overhead lines load is built up using the probabilistic neural network (PNN). In this model, the values including wind speed, ice thickness, rain fall and temperature at the extreme weather are employed to forecast the overhead lines load risks on the time scale which are divided by the lines failures probability method
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