| Autor: |
Onufriienko, D., Taranenko, Yu., Oliinyk, O., Lopatin, V. |
| Předmět: |
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| Zdroj: |
Cybernetics & Systems Analysis; Sep2024, Vol. 60 Issue 5, p834-844, 11p |
| Abstrakt: |
The article considers the available methods of image recognition of continuous wavelet spectra of noisy signals with linear and nonlinear frequency modulations using convolutional neural networks. A procedure is proposed for preparing spectral images for their processing in a neural network, ensuring a sufficient probability of recognizing a given type of signal out of twenty possible ones. The methodology for solving the problem finds an image preparation algorithm that provides image augmentation by changing continuous wavelets, which ensures the identification of signals under conditions of limited resonance frequency and bandwidth. The algorithm changes the frequency of the continuous spectrum by processing the phase grating signal with different continuous wavelets after adding non-stationary noise. Signals with linear and nonlinear modulation prepared in this way, as well as signal spectra of other regular forms, are used as input data of the convolutional neural network. The procedure of dividing images of wavelet spectra into classes is performed by checking the homogeneity of the class based on the Shannon entropy value. The minimum entropy value indicates the homogeneity of the subset and the absence of "impurities" from images of other classes. The developed model of a neural network with augmentation by continuous wavelet spectra under the conditions of a limited data set has an accuracy of up to 97.95%. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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