Publication Type
Journal Article
Version
publishedVersion
Publication Date
6-1991
Abstract
A neural network expert system called adaptive connectionist expert system (ACES) which will learn adaptively from past experience is described. ACES is based on the neural logic network, which is capable of doing both pattern processing and logical inferencing. The authors discuss two strategies, pattern matching ACES and rule inferencing ACES. The pattern matching ACES makes use of past examples to construct its neural logic network and fine-tunes itself adaptively during its use by further examples supplied. The rule inferencing ACES conceptualizes new rules based on the frequencies of use on the rule-based neural logic network. A new rule could be considered as a pattern matching example and be incorporated into pattern matching ACES.
Keywords
Hybrid intelligent systems, Adaptive systems, Pattern matching, Diagnostic expert systems, Neural networks, Logic programming, Expert systems, Knowledge acquisition, Medical expert systems, Frequency
Discipline
Databases and Information Systems
Research Areas
Data Science and Engineering
Publication
IEEE Transactions on Knowledge and Data Engineering
Volume
3
Issue
2
First Page
200
Last Page
207
ISSN
1041-4347
Identifier
10.1109/69.88000
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
LOW, B. T.; LUI, Hochung; TAN, Ah-hwee; and TEH, Hoonheng.
Connectionist expert system with adaptive learning capability. (1991). IEEE Transactions on Knowledge and Data Engineering. 3, (2), 200-207.
Available at: https://ink.library.smu.edu.sg/sis_research/5200
Copyright Owner and License
Publisher
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1109/69.88000