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Transformer-based sequence-to-sequence soft sensor using missing data in industrial processes
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DOI：10.1016/j.chemolab.2026.105631.png)
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Abstract
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• Introduce the self-attention mechanism to encode long-term temporal dependencies. • Use long short-term memory to model local nonlinearities and impute missing data. • Train imputation and prediction end-to-end to ensure the completed data is realistic.
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