Please use this identifier to cite or link to this item: https://doi.org/10.1016/0167-9236(95)00016-X
Title: Towards integrating rule-based expert systems and neural networks
Authors: Quah, T.-S. 
Tan, C.-L. 
Raman, K.S. 
Srinivasan, B.
Keywords: Inferencing mechanism
Learning
Network element
Neural network expert system
Rule editor
Semantic structure
Issue Date: 21-May-1996
Source: Quah, T.-S., Tan, C.-L., Raman, K.S., Srinivasan, B. (1996-05-21). Towards integrating rule-based expert systems and neural networks. Decision Support Systems 17 (2) : 99-118. ScholarBank@NUS Repository. https://doi.org/10.1016/0167-9236(95)00016-X
Abstract: This research explores a new approach to integrate neural networks and expert systems. The integrated system combines the strength of rule-based semantic structure and the learning capability of connectionist architecture. In addition, the approach allows users to define logical operators that behave much similar to that of human expert decision making process. Neural Logic Network (NEULONET) is used as the underlying building unit. A rule-based shell like environment is developed. The shell is used to built a prototype expert decision support system for future bonds trading. The system also provides a way to behave like different experts responding to different users and giving advice according to different environmental situations.
Source Title: Decision Support Systems
URI: http://scholarbank.nus.edu.sg/handle/10635/53237
ISSN: 01679236
DOI: 10.1016/0167-9236(95)00016-X
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