NDLI: Forecast of importance weights of customer requirements based on artificial immune system and least square support vector machine

Content Provider	IEEE Xplore Digital Library
Author	Ai-hua Huang Hong-bin Pu Wei-guang Li Guo-qiang Ye
Copyright Year	2012
Description	Author affiliation: School of Mechanical and Automotive Engineering, South China University of Technology, China, 510640 (Hong-bin Pu; Wei-guang Li; Guo-qiang Ye) \|\| School of Business Administration, South China University of Technology, China, 510640 (Ai-hua Huang)
Abstract	With view to satisfying customers, it is important to correctly ratify importance weights of customer requirements in quality function deployment (QFD). The twenty-first century is marked by fast evolution of customer tastes and needs. Customer requirements could vary with time, customers' preferences and competitive ability of product manufactures. It is urgent and critical to capture the dynamic customer requirements for new product design in QFD. To provide an effective method to predict the importance weights of customer requirements, the model was proposed for forecast of importance weights of customer requirements based on least square support vector machine (LSSVM). To acquire the better parameters of LSSVM, artificial immune system was used to optimize the parameters of LSSVM and the AIS based LSSVM was proposed for forecast of importance weights of customer requirements. To verify the approach, a case was used by comparison between AIS-LSSVM and LSSVM in this paper. The result showed the LSSVM optimized by AIS had better performance than the LSSVM without parameters of optimization by AIS.
Starting Page	83
Ending Page	88
File Size	362990
Page Count	6
File Format	PDF
ISBN	9781467330152
ISSN	21551855
e-ISBN	9781467330145
DOI	10.1109/ICMSE.2012.6414165
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-09-20
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Training Noise Predictive models importance weights least square support vector machine Kernel Optimization QFD Immune system customer requirements artificial immune system
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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