Fuzzy modelling and identification with genetic algorithms based learning

Modelling is an essential step towards a solution to complex system problems. Traditional mathematical methods are inadequate in describing the complex systems when the complexity increases. Fuzzy logic has provided an alternative way in dealing with complexity in real world.

This thesis looks at a practical approach for complex system modelling using fuzzy logic. This approach is usually called fuzzy modelling. The main aim of this thesis is to explore the capabilities of fuzzy logic in complex system modelling using available data. The fuzzy model concerned is the Sugeno-Takage-Kang model (TSK model). A genetic algorithm based learning algorithm (GABL) is proposed for fuzzy modelling. It basically contains four blocks, namely the partition, GA, tuning and termination blocks. The functioning of each block is described and the proposed algorithm is tested using a number of examples from different applications such as function approximation and processing control.