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- W4386003654 abstract "In industrial processes, the ability to predict future steps is essential as it offers long-term insights, benefiting strategic decision-making. However, traditional sequence-to-sequence models designed to predict dynamic behaviors suffer from accumulating errors during recurrent predictions which use previous outputs as inputs for the next time step. In this article, we propose a dual attention-based encoder–decoder framework, specifically designed to enhance multi-step ahead predictions in industrial processes. The dual attention model strategically minimizes the error accumulation of output sequence by leveraging a temporal attention mechanism, which focuses on relevant time-steps in the input sequence, and a supervised attention mechanism that assigns different weights to output sequence errors during training. The supervised attention method, in particular, provides a significant improvement by focusing on minimizing the error of earlier steps during backpropagation using predefined attention weights, resulting in enhanced overall multistep prediction performance. Experiments on real-world industrial datasets demonstrate that our approach outperforms baseline models, specifically simple sequence-to-sequence and single attention-based sequence-to-sequence models. In fact, our dual attention framework consistently surpasses single attention models, currently regarded as state-of-the-art, at all prediction stages. The suggested approach has potential applications in the field of process monitoring and model predictive control." @default.
- W4386003654 created "2023-08-20" @default.
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- W4386003654 date "2023-11-01" @default.
- W4386003654 modified "2023-10-17" @default.
- W4386003654 title "Dual attention-based multi-step ahead prediction enhancement for monitoring systems in industrial processes" @default.
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- W4386003654 doi "https://doi.org/10.1016/j.asoc.2023.110763" @default.
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