Research Article
Back Propagation Neural Network Model for Predicting the Performance of Immobilized Cell Biofilters Handling Gas-Phase Hydrogen Sulphide and Ammonia
Table 3
Network training parameters for choosing the best network architecture.
| Training parameters | Range of values | Best value |
| Training cycle | 1000–40000 | 40000 | Number of neurons in input layer | 2 | 2 | Number of neurons in hidden layer | 2–8 | 2 | Number of neurons in output layer | 1 | 1 | Learning rate | 0.1–0.9 | 0.9 | Momentum term | 0.1–0.9 | 0.3 |
| Fixed parameters during training | | | Error tolerance | 0.0001 | | Epoch size | 25 | | Training algorithm | Standard BEP | | Number of training data set | 102 | | Number of test data set | 32 | | training | 0.8716 | | testing | 0.8484 | |
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