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    block this user An-Ping Li

    Research Fellow

    Beijing 100085, P.R.China

    Use of Decision-Tree Induction for Process Optimization and Knowledge Refinement of an Industrial Process

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    Development of expert systems involves knowledge acquisition which can be supported by applying machine learning techniques. This paper presents the basic idea of using decision-tree induction in process optimization and development of the domain model of electrochemical machining (ECM). It further discusses how decision-tree induction is used to build and refine the knowledge base of the process. The idea of developing an intelligent supervisory system with a learning component (IMAFO, Intelligent MAnufacturing FOreman) that is already implemented, is briefly introduced. The results of applying IMAFO for analyzing data form the ECM process are presented. How the domain model of the process (electrochemical machining) is built from the initial known information and how the results of decision-tree induction can be used to optimize the model of the process and further refine the knowledge base are shown. Two examples are given to demonstrate how new rules (to be included in the knowledg...

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    Title : Use of Decision-Tree Induction for Process Optimization and Knowledge Refinement of an Industrial Process
    Abstract : Development of expert systems involves knowledge acquisition which can be supported by applying machine learning techniques. This paper presents the basic idea of using decision-tree induction in process optimization and development of the domain model of electrochemical machining (ECM). It further discusses how decision-tree induction is used to build and refine the knowledge base of the process. The idea of developing an intelligent supervisory system with a learning component (IMAFO, Intelligent MAnufacturing FOreman) that is already implemented, is briefly introduced. The results of applying IMAFO for analyzing data form the ECM process are presented. How the domain model of the process (electrochemical machining) is built from the initial known information and how the results of decision-tree induction can be used to optimize the model of the process and further refine the knowledge base are shown. Two examples are given to demonstrate how new rules (to be included in the knowledg...
    Subject : unspecified
    Area : Mathematics
    Language : English
    Affiliations
    Url : http://ftp://ai.iit.nrc.ca/pub/ksl-papers/NRC-35070.ps.Z
    Doi : 10.1.1.51.3516

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    An-Ping's Peer Evaluation activity

    Emailed by 1
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    Downloads 761
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    Full text requests 9
    Followed by 2

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