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A fuzzy logic‐driven multiple knowledge integration framework for improving the performance of expert systems

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  • Kun Chang Lee
  • Jae Ho Han
  • Yong Uk Song
  • Won Jun Lee

Abstract

To maintain a high performance in an ill‐structured situation, expert systems should depend on multiple sources of knowledge rather than a single type. For this reason, we propose multiple knowledge integration by using a fuzzy logic‐driven framework. Types of knowledge being considered here are threefold: machine, expert and user. Machine knowledge is obtained by a back‐ propagation neural network model from historical instances of a target problem domain. Expert knowledge is related to interpreting the trends of external factors that seem to affect the target problem domain. User knowledge represents a user’s personal views about information given by both expert knowledge and machine knowledge. The target problem domain of this paper is one‐week‐ahead stock market stage prediction: Bull, Edged‐up, Edged‐down, and Bear. Extensive experiments with real data proved that the proposed fuzzy logic‐driven framework for multiple knowledge integration can contribute significantly to improving the performance of expert systems. Copyright © 1998 John Wiley & Sons, Ltd.

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  • Kun Chang Lee & Jae Ho Han & Yong Uk Song & Won Jun Lee, 1998. "A fuzzy logic‐driven multiple knowledge integration framework for improving the performance of expert systems," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 7(4), pages 213-222, December.
    • Handle: RePEc:wly:isacfm:v:7:y:1998:i:4:p:213-222
    DOI: 10.1002/(SICI)1099-1174(199812)7:43.0.CO;2-V
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    Cited by:

    1. S. Aly & I. Vrana, 2006. "Toward efficient modeling of fuzzy expert systems: a survey," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 52(10), pages 456-460.

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