Modelling of Complex Systems using Computational Intelligence Approaches
This project involves study of computational intelligence (CI) approaches for the purposes of modelling the characteristics of
non-linear complex systems. The CI based system modelling consists of a systematic procedure involving:
 appropriate data generation and acquisition,
 data pre-processing,
 normalisation of data,
 selection of appropriate combination of input-output variables
 selection of appropriate topology
 network training using an efficient algorithm
 regularisation to reduce the effect of noise
 and model validation.
The student will be required to research thoroughly all the above aspects. A set of typical example problems will be used to
make a comparative evaluation of important modelling approaches proposed in the literature. Finally, the most appropriate
modelling strategy will be selected for developing a life-long learning system that can facilitate teaching of a robot by
demonstration in the following way:

A learning network can be designed and developed for a mobile robot, so that it emulates the life-long learning behaviour
of a human being. For this, training data needs to be generated by first moving the robot around in a desired way. The data
will include motion commands and reading of all the sensors. A network will then be trained to predict next command, so
that robot can safely move around in its environment. It can also be updated through on-line learning.
Resources Needed
A PC, MATLAB and its toolboxes, a robotic system.
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