Previous studies have shown that quadratic networks excel in cyclostationary feature extraction compared to conventional networks. Validation through empirical testing on datasets highlights this performance.
The experimental results reveal that despite high noise levels, the quadratic convolutional network maintains superior feature extraction capabilities, recovering cyclic frequencies which are critical for accurate signal analysis.
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