Twofold fuzzy sets and rough sets—Some issues in knowledge representation
Fuzzy Sets and Systems
Variable precision rough set model
Journal of Computer and System Sciences
Approximation of fuzzy concepts in decision making
Fuzzy Sets and Systems
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
VPRSM Approach to WEB Searching
TSCTC '02 Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing
Information Sciences—Informatics and Computer Science: An International Journal
Syntactic pattern model classification with total fuzzy grammars
ICCOMP'08 Proceedings of the 12th WSEAS international conference on Computers
Natural language processing implementation on Romanian Chatbot
SMO'09 Proceedings of the 9th WSEAS international conference on Simulation, modelling and optimization
The Knowledge Engineering Review
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Knowledge acquisition plays a significant role in the knowledge-based intelligent process planning system, but there remains a difficult issue. In manufacturing process planning, experts often make decisions based on different decision thresholds under uncertainty. Knowledge acquisition has been inclined towards a more complex but more necessary strategy to obtain such thresholds, including confidence, rule strength and decision precision. In this paper, a novel approach to integrating fuzzy clustering and VPRS (variable precision rough set) is proposed. As compared to the conventional fuzzy decision techniques and entropy-based analysis method, it can discover association rules more effectively and practically in process planning with such thresholds. Finally, the proposed approach is validated by the illustrative complexity analysis of manufacturing parts, and the analysis results of the preliminary tests are also reported.