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
𝑅^2 training	0.8716
𝑅^2 testing	0.8484