NDLI: Research on knowledge hierarchical induction for injection mould repairs based on rough set

Content Provider	IEEE Xplore Digital Library
Author	Chen Chen Ning Mao Qing-xin Chen
Copyright Year	2010
Description	Author affiliation: Department of Mechanical Engineering, Guangdong University of Technology, Guangzhou, 510006, China (Ning Mao; Qing-xin Chen) \|\| College of Economics and Management, China University of Petroleum, Dongying, ShanDong 257061, China (Chen Chen)
Abstract	By the combination of feature and concept hierarchy model and the definition of innovation about concept, the method of structured processing and knowledge hierarchical representation for injection mould repair schemes is put forward under the condition of non-fuzzy or fuzzy data. Rule sets can be provided by knowledge induction for injection mould repairs based on basic rough set, but the rule sets are large-scale and the applicability is poor in practice. Aiming at achieving the rule sets more efficiently and applicably, a new method of knowledge hierarchical induction based on variable precision rough set is proposed. For injection mould repair schemes based on fuzzy data, by the abstraction of innovation about concept from the feature decision tables, feature fuzzy similar matrix is constructed and the feature decision table is divided into some fuzzy equivalent subsets by introducing confidence level vector. Finally, the algorithm of feature reduction and knowledge induction based on fuzzy rough set is put forward by defining single or multi-fuzzy feature equivalence relations. The feasibility and effectiveness of two methods for knowledge hierarchical induction under different data environments are both analyzed.
Starting Page	2900
Ending Page	2904
File Size	240416
Page Count	5
File Format	PDF
ISBN	9781424459315
e-ISBN	9781424459346
DOI	10.1109/FSKD.2010.5569274
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-08-10
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Technological innovation variable precision rough set Rough sets Support vector machine classification Knowledge representation Maintenance engineering Classification algorithms knowledge hierarchical induction fuzzy rough set
Content Type	Text
Resource Type	Article

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