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An adaptive hierarchical fuzzy logic system for modelling of financial systems

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  • M. Mohammadian
  • M. Kingham

Abstract

In this paper an intelligent hierarchical fuzzy logic system using genetic algorithms for the prediction and modelling of interest rates in Australia 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 that are used for prediction of interest rates in Australia. A hierarchical 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 novel architecture called a feed forward fuzzy logic system using fuzzy logic and genetic algorithms is also developed to predict interest rates. A back‐propagation hierarchical neural network system is also developed to predict interest rates for three‐month, six‐month and one‐year periods. The results obtained from these two systems are then compared with the hierarchical fuzzy logic system results and conclusions are drown on the accuracy of all systems for prediction of interest rates in Australia. Copyright © 2004 John Wiley & Sons, Ltd.

Suggested Citation

  • M. Mohammadian & M. Kingham, 2004. "An adaptive hierarchical fuzzy logic system for modelling of financial systems," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 12(1), pages 61-82, January.
    • Handle: RePEc:wly:isacfm:v:12:y:2004:i:1:p:61-82
    DOI: 10.1002/isaf.241
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