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• W4295409342 endingPage "6887" @default.
• W4295409342 startingPage "6887" @default.
• W4295409342 abstract "Soft sensors based on deep learning approaches are growing in popularity due to their ability to extract high-level features from training, improving soft sensors' performance. In the training process of such a deep model, the set of hyperparameters is critical to archive generalization and reliability. However, choosing the training hyperparameters is a complex task. Usually, a random approach defines the set of hyperparameters, which may not be adequate regarding the high number of sets and the soft sensing purposes. This work proposes the RB-PSOSAE, a Representation-Based Particle Swarm Optimization with a modified evaluation function to optimize the hyperparameter set of a Stacked AutoEncoder-based soft sensor. The evaluation function considers the mean square error (MSE) of validation and the representation of the features extracted through mutual information (MI) analysis in the pre-training step. By doing this, the RB-PSOSAE computes hyperparameters capable of supporting the training process to generate models with improved generalization and relevant hidden features. As a result, the proposed method can generate more than 16.4% improvement in RMSE compared to another standard PSO-based method and, in some cases, more than 50% improvement compared to traditional methods applied to the same real-world nonlinear industrial process. Thus, the results demonstrate better prediction performance than traditional and state-of-the-art methods." @default.
• W4295409342 created "2022-09-13" @default.
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• W4295409342 date "2022-09-13" @default.
• W4295409342 modified "2023-10-14" @default.
• W4295409342 title "Industrial Soft Sensor Optimized by Improved PSO: A Deep Representation-Learning Approach" @default.
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• W4295409342 doi "https://doi.org/10.3390/s22186887" @default.
• W4295409342 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36146235" @default.
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