The Development of Neural Network Based System Identification ...

144 CHAPTER 5 NN BASED SYSTEM IDENTIFICATION: RESULTS AND DISCUSSION
5.5 MODEL PERFORMANCE COMPARISON USING OFF-LINE
TRAINING
The prediction performance among three NN architectures proposed in this work is
compared by collecting the error statistics that have been generated in the Figure 5.6,
5.11 and 5.15. The NN models performance comparison is made based on the optimal
network structures obtained from these figures.
Results point that the prediction
from NNARX architecture using the MLP network produce prediction quality with a
total RMSE percentage of 10.11%. The HMLP network offers prediction with slight
performance improvement than the MLP (9.82%) while the modified Elman network
gives the lowest prediction quality (15.78%).
Results from Table 5.3, 5.6 and 5.9 indicate that the average RMSE and confidence
limit values from the weights are able to produce prediction that adequately fit with the
test data. From the results, the prediction performance from the optimised structure
of the MLP, HMLP and modified Elman networks are shown to be robust from large
variation of weights values. Therefore, it can be conclude that NN training from off-line
training method such as LM algorithm would produce weights set that can produce quite
large latitude of value but still able to produce a satisfactory prediction performance.
5.6 ON-LINE SYSTEM IDENTIFICATION
In this section, the on-line training proposed in Section 4.3.5 is implemented to identify
the attitude dynamics of the UAS helicopter model using the MLP network. The suitable
regression vector structure and hidden neurons size for recursive NNARX model can
be determined using the k-fold cross validation technique previously discussed. Using
results from the off-line system identification, network structure with n y = 3 and n u = 1
is used as the basic model structure for comparing the generalisation performance of
the recursive Gauss-Newton (rGN) method with off-line training method.
The comparison also utilises the k-fold cross validation method to identify the
efficiency of the selected neural network training methods in estimating the attitude
dynamics of the helicopter. To compare the generalisation performance of the rGN