Data-Driven Quality Prediction of Batch Processes Based on Minimal-Redundancy-Maximal-Relevance Integrated Convolutional Neural Network

<div>Part of the prediction errors under different feature preservation proportions. (a) 30.0% features reserved, (b) 35.0% features reserved, (c) 40.0% features reserved, and (d) all features reserved.</div>

Mathematical Problems in Engineering

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Figure 9

Figure 9: Data-Driven Quality Prediction of Batch Processes Based on Minimal-Redundancy-Maximal-Relevance Integrated Convolutional Neural Network 