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Australian Joint Conference on Artificial Intelligence

AI 1997: Advanced Topics in Artificial Intelligence pp 147–156Cite as

Hierarchical and feed-forward fuzzy Logic for financial modelling and prediction

  • Evolutionary Computation
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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1342))

Abstract

In this paper, the development of a Hierarchical and Feed-Forward intelligent Fuzzy Logic system using Genetic Algorithms for prediction and modelling of fluctuations in interest rates in Australia is discussed. The system developed is used to predict quarterly and half yearly interest rates using fuzzy logic. The fuzzy rules for fuzzy logic predictor are unknown. A knowledge base must be created from the available data. In the paper Genetic Algorithm is proposed as a method for learning the fuzzy rules of the fuzzy logic predictor. A Hierarchical and Feed-Forward Fuzzy Logic system consisting of five fuzzy knowledge bases is developed to solve the prediction problem.

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Author information

Authors and Affiliations

  1. School of Computing & Information Technology, Monash University, Churchill, Australia

    Masoud Mohammadian

  2. Department of Computer Science, Edith Cowan University, Perth, Western Australia

    Mark Kingham

Authors
  1. Masoud Mohammadian
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  2. Mark Kingham
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Editor information

Abdul Sattar

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© 1997 Springer-Verlag Berlin Heidelberg

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Mohammadian, M., Kingham, M. (1997). Hierarchical and feed-forward fuzzy Logic for financial modelling and prediction. In: Sattar, A. (eds) Advanced Topics in Artificial Intelligence. AI 1997. Lecture Notes in Computer Science, vol 1342. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-63797-4_67

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  • DOI: https://doi.org/10.1007/3-540-63797-4_67

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-63797-4

  • Online ISBN: 978-3-540-69649-0

  • eBook Packages: Springer Book Archive

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