The Development of Neural Network Based System Identification ...

152 CHAPTER 5 NN BASED SYSTEM IDENTIFICATION: RESULTS AND DISCUSSION
networks are lower than the MLP network, the prediction performance of both NN
models are on par with the prediction quality of the MLP network. The results also
point out that the average RMSE and the confidence limit values from each of the
proposed networks are able to produce prediction that adequately fit with the test data.
The prediction from the MLP, HMLP and modified Elman networks are also shown to
be robust from large variations of weights values. Therefore, it can be concluded that
NN training from the off-line training methods such as the LM algorithm or repeated
GN method are able to produce satisfactory prediction performance even with large
deviation of weights set derived from the training process.
Validation results conducted in this study confirmed that the off-line NN model
is suitable for modelling the helicopter’s attitude dynamics correctly. However, the
dynamic model identified using the off-line NN model has a major drawback such that
the method’s inability to represent the entire flight operation very well because of the
time varying nature of helicopter flight dynamics. Recursive type training such as the
rGN method was used in this study to overcome such problems in system identification.
Results indicate that the rGN algorithm is more adaptive to the changes in dynamic
properties, although the generalisation error of repeated rGN is slightly higher than
off-line LM method. The rGN method is also found capable of producing a satisfactory
prediction quality even though the model structure was incorrectly selected.
The
generalisation and adaptability performance of the model can be further improved by
properly selecting the optimised network structure with the aid of k-fold cross validation
method.
The recursive method presented in this work is suitable for modelling the helicopter
in real-time within the control sampling time and computational resource constraints.
Recursive Gauss-Newton method used in the NN training demonstrates faster prediction
updates and offers rapid computation of weight adaptation with average training time of
3.88 ms. The average training time for rGN algorithm is well below the targeted control
loop sampling period (22 ms). This indicates that such recursive training algorithms are
well suited for real-time applications. Furthermore, the proposed HMLP and modified
Elman networks are found to improve the learning rate of NN prediction and this