A fuzzy logic-driven multiple knowledge integration framework for improving the performance of expert systems
Authors: Lee K.C.1, *; Han J.H.2; Song Y.U.3; Lee W.J.4
Source: International Journal of Intelligent Systems in Accounting, Finance & Management, Volume 7, Number 4, December 1998 , pp. 213-222(10)
Publisher: John Wiley & Sons, Ltd.
- Free Content
- New Content
- Subscribed Content
- Free Trial Content
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.
Keywords: expert systems; multiple knowledge integration; fuzzy logic; expert knowledge; user knowledge; machine knowledge
Language: English
Document Type: Research article
DOI: 10.1002/(SICI)1099-1174(199812)7:4<213::AID-ISAF145>3.0.CO;2-V
Affiliations: 1: Sung Kyun Kwan University, Korea 2: Pukyong National University, Korea 3: Korea Advanced Institute of Science and Technology, Korea 4: Inchon University, Korea *
- Free Content
- New Content
- Subscribed Content
- Free Trial Content
Click here for Page Help