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An adjusted empirical mode decomposition method, built on Student's probability density function is presented. Compared to the original EMD, the new version provides a lower number of intrinsic mode functions and is more accurate in signal modeling and prediction. Using a backpropagation neural network for learning and in-sample prediction, our experimental results on a synthetic signal, an electrocardiogram...
A technique based on an artificial neural network is presented for determining the reflection and transmission characteristics of nonuniform microstrip lines. The width of the microstrip line is expressed as a truncated Fourier series, whose coefficients are combined with the analysis frequency and input to the neural network to determine the S11 and S21 parameters. A multilayer perceptron with two...
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