A. Creating the Network
Network Architecture:
| Number of Inputs | 4 | |
| Number of Members | 3 | (3 Memberhip Functions per input, eg High, Medium, Low) |
| Number of Rules | 5 | |
| Number of Outputs | 3 | |
| Number of Actions | 2 | (2 Memberhip Functions per input, eg High, Low) |
B. Training the network
In neural net applications the weights are determined so that they minimise the Root Mean Squared errors (RMSE).
| Learning Rate | 0.3 |
| Momentum | 0.6 |
| Epochs | 100 then 500 (increase until the terminating error is reached) |
| Terminating Error | 0.01 |
| Training Mode | Batch |
C. GA Training
| Population Size | 50 |
| Minimum Weight | -20 |
| Maximum Weight | 20 |
| Mutation Rate | 0.01 |
| Generations | 100 |
| Terminating Error | 0.01 |
| Fitness Normalisation | Unchecked |
| Elitism | Checked |
| Crossover points | 1 |
| Selection strategy | Tournament |
The training error will result.
D. Network Evaluation
E. Extracting Rules
| Threshold In | 1.0 |
| Threshold Out | 1.0 |
F. Additional Work
Try different combinations of: