An Adaptive Hierarchical Fuzzy Logic System for Modelling and Prediction of Financial Systems

In this thesis, an intelligent fuzzy logic system using genetic algorithms for the prediction and modelling of interest rates is developed. The proposed system uses a Hierarchical Fuzzy Logic system in which a genetic algorithm is used as a training method for learning the fuzzy rules knowledge bases. A fuzzy logic system is developed to model and predict three month quarterly interest rate fluctuations. The system is further trained to model and predict interest rates for six month and one year periods. The proposed system is developed with first two, three, then four and finally five hierarchical knowledge bases to model and predict interest rates. A Feed Forward Fuzzy Logic system using fuzzy logic and genetic algorithms is developed to predict interest rates for three months periods. A back-propagation Hierarchical Neural Network system is further developed to predict interest rates for three months, six months and one year periods. These two systems are then compared with the Hierarchical Fuzzy Logic system results and conclusions on their accuracy of prediction are compared.