Por favor utiliza este link para citar o compartir este documento: http://repositoriodigital.academica.mx/jspui/handle/987654321/183797
Título: A New Trend for FET Small Signal Modeling Using Cepstral Analysis
Palabras clave: Ingeniería
MFCCs
Neural networks
DST
FET
DWT
DCT
Editorial: Instituto Politécnico Nacional
Descripción: In this paper, a new technique is proposed for field effect transistor (FET) small-signal modeling using neural networks. This technique is based on the combination of the Mel-frequency Cepstral Coefficients (MFCCs) with the different discrete transforms such as the discrete cosine transform (DCT), the discrete sine transform (DST), and the discrete wavelet transform (DWT) of the inputs to the neural networks. The input data sets to traditional neural systems for FET smallsignal modeling are the scattering parameters and the corresponding frequencies in a certain band, and the outputs are the circuit elements. In the proposed approach, these data sets are considered as forming random signals. The MFCCs of the random signals are used to generate a small number of features characterizing the signals. In addition, other vectors are calculated from the DCT, the DST, or the DWT of the random signals and appended to the MFCCs vectors calculated from the signals. The new feature vectors are used to train the neural networks. The objective of using these new vectors is to characterize the random input sequences with much more features to be robust against measurement errors. There are two benefits for these approaches; a reduction in the number of neural networks inputs, and hence a faster convergence of the neural training algorithm and robustness against measurement errors in the testing phase. Experimental results show that the techniques based on the discrete transforms are less sensitive to measurement errors than using the traditional and MFCCs methods.
Other Identifiers: http://www.redalyc.org/articulo.oa?id=61420811005
Aparece en las Colecciones:Científica

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