Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/6056
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dc.contributor.authorLiu, Peng.en_US
dc.date.accessioned2008-09-17T11:05:46Z-
dc.date.available2008-09-17T11:05:46Z-
dc.date.copyright2005en_US
dc.date.issued2005-
dc.identifier.urihttp://hdl.handle.net/10356/6056-
dc.description.abstractAdvanced Product Quality Planning (APQP), which is one of QS9000 requirements applied to automotive industries, performs an important role in quality assurance activities. It’s aroutine job in most of the companies. A group of employees fulfill the job following designated procedures and produce a lot of reports and forms. In this dissertation, one model of problem solving that involves choosing an action based on past experiments in similar situations is presented. The problem to be solved in actual business activities is the APQP implementation. This model realized the advantages of some Artificial Intelligence (AI) technologies; these AI technologies include Artificial Neural Network (ANN), Case-Based Reasoning (CBR) and Fuzzy Logic (FL).en_US
dc.rightsNanyang Technological Universityen_US
dc.subjectDRNTU::Engineering::Industrial engineering::Quality engineering-
dc.titleHybrid system of artificial neural network, case based reasoning and fuzzy logic for QS9000 implementationen_US
dc.typeThesisen_US
dc.contributor.supervisorHuin, Seng Fatten_US
dc.contributor.schoolSchool of Mechanical and Aerospace Engineeringen_US
dc.description.degreeMaster of Science (Computer Integrated Manufacturing)en_US
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