@article{ef63c1972ab143b386f4c7820d720cfc,
title = "Radial-basis-function-based neural network for harmonic detection",
abstract = "The widespread application of power-electronic loads has led to increasing harmonic pollution in the supply system. In order to prevent harmonics from deteriorating the power quality, detecting harmonic components for harmonic mitigations becomes a critical issue. In this paper, an effective procedure based on the radial-basis-function neural network is proposed to detect the harmonic amplitudes of the measured signal. By comparing with several commonly used methods, it is shown that the proposed solution procedure yields more accurate results and requires less sampled data for harmonic assessment.",
keywords = "Adaptive linear combiner (ADALINE), Back-propagation neural network, Fast Fourier transform (FFT), Harmonics, Radial-basis-function neural network (RBFNN)",
author = "Chang, {Gary W.} and Chen, {Cheng I.} and Teng, {Yu Feng}",
note = "Funding Information: Manuscript received March 2, 2009; revised April 30, 2009 and August 14, 2009; accepted September 26, 2009. Date of publication October 20, 2009; date of current version May 12, 2010. This work was supported by the National Science Council of Taiwan under Grant NSC97-2221-E-194-062-MY3.",
year = "2010",
month = jun,
doi = "10.1109/TIE.2009.2034681",
language = "???core.languages.en_GB???",
volume = "57",
pages = "2171--2179",
journal = "IEEE Transactions on Industrial Electronics",
issn = "0278-0046",
number = "6",
}