基于二元逐次逼近前馈人工神经网络的次谐波确定方法的初步研究

F. Leccese
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引用次数: 6

摘要

实现了一种前馈人工神经网络,并对其进行了训练,实现了电网中次谐波频率的个性化。所采用的采样窗固定在20ms,对应于50hz的基频周期,然而,即使其周期未完成,网络也能够识别次谐波频率。人工神经网络所实现的特殊的二值逐次逼近结构预测了同一网络的可扩展性,从而使识别具有固定分辨率的次谐波成为可能。这种网络已经在合成信号上进行了广泛的测试。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Subharmonics determination method based on binary successive approximation feed forward artificial neural network: a preliminary study
A feedforward artificial neural network has been realized and trained to individualize subharmonics frequencies in an electric network. The adopted sampling window is fixed in 20 ms correspondent to a period of the fundamental frequency of 50 Hz, nevertheless the net is able to identify the subharmonics frequencies even if their period is not completed. The particular binary successive approximation structure realized for the ANN forecasts the scalability of the same net so allowing to recognize the subharmonics with a prefixed resolution. The net has been widely tested on synthesized signals.
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