کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
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410532 | 679149 | 2009 | 6 صفحه PDF | دانلود رایگان |
This paper applies the minimum gradient method (MGM) to denoise signals in engineering problems. The MGM is a novel technique based on the complexity control, which defines the learning as a bi-objective problem in such a way to find the best trade-off between the empirical risk and the machine complexity. A neural network trained with this method can be used to pre-process data aiming at increasing the signal-to-noise ratio (SNR). After training, the neural network behaves as an adaptive filter which minimizes the cross-validation error. By applying the general singular value decomposition (GSVD), we show the relation between the proposed approach and the Wiener filter. Some results are presented, including a toy example and two complex engineering problems, which prove the effectiveness of the proposed approach.
Journal: Neurocomputing - Volume 72, Issues 10–12, June 2009, Pages 2270–2275