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A Precise Algorithm for Non-Integer Harmonics Analysis Based on FFT and Neural Network

机译:基于FFT和神经网络的非整数次谐波分析的精确算法。

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摘要

By means of an artificial neural network (ANN) model, higher measurement accuracy of integer harmonics can be obtained. Combining the windowed fast Fourier transform (FFT) algorithm with the improved ANN model, we present a new precise algorithm for non-integer harmonics analysis. According to the result obtained from the Hanning-windowed FFT algorithm, we choose the initial values of orders of harmonics for the neural network. Through such processing, the time of iterations is shortened and the convergence rate of neural network is raised thereby. The simulation results show that close non-integer harmonics can be separated from a signal with higher accuracy and better real-time by using the algorithm presented in the paper.
机译:借助于人工神经网络(ANN)模型,可以获得更高的整数谐波测量精度。将加窗快速傅里叶变换(FFT)算法与改进的ANN模型相结合,我们提出了一种用于非整数谐波分析的新型精确算法。根据汉宁窗式FFT算法获得的结果,我们选择神经网络谐波阶次的初始值。通过这样的处理,缩短了迭代时间,从而提高了神经网络的收敛速度。仿真结果表明,采用本文提出的算法可以将近非整数谐波从信号中分离出来,具有更高的精度和更好的实时性。

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