The Development of Neural Network Based System Identification ...

28 CHAPTER 2 LITERATURE REVIEW
1. Dendrites: Accept Inputs
3. Axon: Turn the
processed inputs
to outputs
Direction of
signal transmission
Nucleus
2. Cell Body/Soma:
Process the Inputs
4. Axon terminal buttons:
The electrochemical
signal transfer point
Figure 2.5
A simplified representation of a biological neuron.
NN architecture such as Multi-layered Perceptron (MLP) network has been theoretically
proven to be an universal approximator, thus capable of approximating any non-linear
function of interest to any desired degree of accuracy with sufficiently available neurons
[Hornik et al., 1989]. Furthermore, the NN models are also able to establish the required
input-output relationship without a priori assumptions about the properties of the data
or the governing mathematical models as in first principle modelling [Abas et al., 2011].
The NN modelling approach has been used to perform a diverse range of tasks
including prediction, function approximation, pattern classification and clustering. It
has been proven in many research investigations and industrial applications as an
efficient tool to handle complex input-output mapping. Paliwal and Kumar [2009]
carried out a comprehensive literature review of over 100 comparative studies on the
ANN and traditional statistical techniques used for prediction and classification tasks in
various fields of applications. The review clearly highlights the potential and usefulness
of the NN approach to approximate any non-linear mathematical function when it is
difficult to handle the task statistically. It can be concluded from the review that NN
models outperform the traditional statistical methods in most of the cases or at least
performed as well as other statistical methods.
The literature also includes numerous applications of NN to address a wide range of