DocumentCode :
1993051
Title :
Research of Model Intelligent Selection Based on Rule Reasoning
Author :
Weidong, Chen ; Qinghe, Hu ; Jiazhuo, Xu ; Dalei, Yang
Author_Institution :
BaoSteel Ind. Inspection Corp., Shanghai
Volume :
2
fYear :
2008
fDate :
21-22 Dec. 2008
Firstpage :
752
Lastpage :
756
Abstract :
Decision-making model management is the key to success and practicality of decision-making support system. Model selection plays an important role in model management as foundation. Expert system theory is adopted to model selection. Model selection knowledge is obtained for each node of model classification tree, represented by architecture and production rules. Model selection knowledge tree is setup to make knowledge structuring and systematic. On the basic of model selection knowledge tree, build up model selection reasoning machine adopting framework reasoning and local-preference searching strategy. The method is applied successfully to product quality defects warning model of a management platform for a steel company.
Keywords :
decision making; decision support systems; expert systems; inference mechanisms; pattern classification; quality management; steel industry; tree searching; classification tree; decision-making model management; decision-making support system; expert system theory; intelligent model selection knowledge tree; knowledge structure; knowledge systematic; local-preference searching strategy; product quality defect warning model; production rule reasoning; steel company management; Classification tree analysis; Concrete; Decision making; Educational technology; Expert systems; Industrial training; Management training; Object oriented modeling; Predictive models; Relational databases; Decision-Making Support System; Expert System; Knowledge Tree; Model Selection;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Education Technology and Training, 2008. and 2008 International Workshop on Geoscience and Remote Sensing. ETT and GRS 2008. International Workshop on
Conference_Location :
Shanghai
Print_ISBN :
978-0-7695-3563-0
Type :
conf
DOI :
10.1109/ETTandGRS.2008.386
Filename :
5070471
Link To Document :
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