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Direct Estimation of Noisy Sinusoids Using Abductive Networks
 

Summary: 1
Direct Estimation of Noisy Sinusoids
Using Abductive Networks
R. E. Abdel-Aal
Center for Applied Physical Sciences, Research Institute,
King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia
Abstract
Spectral estimation techniques have been used for many years. In many cases, their complexity
warrants investigating machine-learning alternatives where intensive computations are required
only during training, with actual estimation simplified and speeded up. This allows using simple
portable apparatus for fast and automated estimation in real time. We propose using abductive
network machine learning for estimating both the amplitude and frequency of a single sine wave
in the presence of additive Gaussian noise. Models synthesized by training on 1000
representative simulated sinusoids were evaluated on 500 new cases. With no phase variations
and a signal to noise ratio of 7 dB, average absolute percentage errors for the sinusoid amplitude
and period are 8.4% and 3.6%, respectively. Effects of the range of frequency variations and the
noise level on the complexity and accuracy of the models were investigated. Amplitude and
period estimates show signs of biased at a signal to noise ratio of 3 dB. Error variances track the
Cramer-Rao bounds at high noise levels, with no thresholding observed down to 0 dB. The
method is compared with a neural network model and with conventional DFT (discrete Fourier

  

Source: Abdel-Aal, Radwan E. - Computer Engineering Department, King Fahd University of Petroleum and Minerals

 

Collections: Computer Technologies and Information Sciences; Power Transmission, Distribution and Plants