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: