Research Article
Data-Driven Quality Prediction of Batch Processes Based on Minimal-Redundancy-Maximal-Relevance Integrated Convolutional Neural Network
Table 1
Specific structure of fixed mRMR-CNN.
| Type | Kernel size/stride | Output size | Parameters | Trainable |
| Convolution | 3 × 3/1 | 50 × 16 × 10 | 100 | True | Max pooling | 2 × 2/2 | 25 × 8 × 10 | — | — | Convolution | 3 × 3/1 | 25 × 8 × 20 | 1820 | True | Max pooling | 2 × 2/2 | 12 × 4 × 20 | — | — | Feature selection | — | 336 | 336 | False | Dropout (50%) | — | 336 | — | — | Full-connection | — | 40 | 13480 | True | Dropout (50%) | — | 40 | — | — | Output | — | 1 | 41 | True |
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