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

Kun Chang Lee, Jae Ho Han, Yong Uk Song and Won Jun Lee

Intelligent Systems in Accounting, Finance and Management, 1998, vol. 7, issue 4, 213-222

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.

Date: 1998
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https://doi.org/10.1002/(SICI)1099-1174(199812)7:43.0.CO;2-V

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