Table 4.
FNN’s characteristics.
| Parameter | Notation | Value and Description |
|---|---|---|
| Neurons in input layer | ninput | 6 |
| Number of hidden layer | nlayer | 1 |
| Neurons in hidden layer | nneuron | 20 |
| Neurons in output layer | noutput | 1 |
| Number of weight parameters in hidden layer | ninput × nneuron = 120 | |
| Number of bias parameters in hidden layer | nneuron (20) | |
| Number of weight parameters in output layer | nneuron × noutput = 20 | |
| Number of bias parameters in output layer | noutput (1) | |
| Number of total parameters to be optimized | 161 | |
| Training algorithm | OSS | One Step Secant |
| Cost function | MSE | Mean square error |
| Activation function for hidden layer | Sigmoid | |
| Activation function for output layer | Linear |