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: