The Development of Neural Network Based System Identification ...

2.3 NEURAL NETWORK BASED SYSTEM IDENTIFICATION 31
better generalisation performance compared with RBF network but at the expense of
much longer training time [San Martin et al., 2006]. Results show that NNARX network
produces better global approximation for different flight manoeuvres compared with
RBF network which produces a lower prediction error in certain flight manoeuvres.
The RNN architectures with dynamic memories have become a popular choice for
system identification applications. They have the primary advantage in identifying
dynamical system without prior knowledge about the model structure in contrast to
NNARX or RBF network architectures, and incorporate dynamics information into the
model using feedback from the output neurons (Jordan type networks) or the output
of hidden neurons (Elman type networks) into the context units. These units are also
known as the memory units which store past output values from the hidden or output
neurons. The RNN architectures and their variant forms have been introduced into
various system identification applications where the methods are found to be capable of
representing a non-linear dynamical system with exceptional accuracy [Pham and Liu,
1993, Kalinli and Sagiroglu, 2006, Suresh et al., 2003, Samarasinghe, 2007].
Another type of recurrent NN model called Memory Neural Network (MNN) was
introduced by Sastry et al. [1994] for identification and control of dynamic systems.
This neural network modelling approach was later employed by Suresh et al. [2002]
to model the longitudinal and lateral dynamics of a helicopter system. The operating
concept of the MNN is quite similar to the Jordan and Elman networks as the MNN
has several internal temporal memory units that are trainable to represent the dynamic
systems without depending on the past output and input measurements. Each neuron
in MNN is associated with a memory unit which stores the past values of the network
neurons.
The RNN architectures suffer several drawbacks associated with the network’s
insufficient memory capacity which limits the prediction capability to lower system order.
Several modifications have been suggested to increase the performance and memory
capacity of the networks [Dong et al., 1994, Pham and Liu, 1993, Kalinli and Sagiroglu,
2006]. Moreover, Horne and Giles [1995] have shown that NNARX network performed
better in some system identification problems than many conventional recurrent networks