Deep Nonlinear Dynamic Feature Extraction for Quality Prediction Based on Spatiotemporal Neighborhood Preserving SAE
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DOI: 10.1109/tim.2021.3122187
Abstract: Complex industrial process data often exhibit nonlinear static and dynamic characteristics. Traditional deep learning methods such as stacked autoencoder (SAE) have excellent nonlinear static feature learning capabilities, but they ignore the dynamic correlation existing in… read more here.
Keywords: feature; process data; process; spatiotemporal neighborhood ... See more keywords