Journal of South China University of Technology (Natural Science Edition) ›› 2013, Vol. 41 ›› Issue (1): 89-94.doi: 10.3969/j.issn.1000-565X.2013.01.014

• Computer Science & Technology • Previous Articles     Next Articles

Importance Forecast of Customer Requirements Based on ArtificialImmunity and Least Square Support Vector Machine

Huang Ai-hua1 Pu Hong-bin2 Li Wei-guang2 Hou Yue-en2   

  1. 1. School of Business Administration, South China University of Technology, Guangzhou 510640, Guangdong, China;2. School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510640, Guangdong, China
  • Received:2012-07-05 Revised:2012-09-21 Online:2013-01-25 Published:2012-12-03
  • Contact: 黄爱华(1962-),女,副教授,主要从事管理科学与工程、企业管理研究. E-mail:ahhuang@scut.edu.cn
  • About author:黄爱华(1962-),女,副教授,主要从事管理科学与工程、企业管理研究.
  • Supported by:

    广东省重大科技专项(2009A080202004);广东省粤港关键领域重点突破项目(2011BZ100012)

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Abstract:

In order to improve the dynamic decision level during the quality function deployment and effectivelypredict the importance of customer requirements, a forecast model based on the least square support vector machine(LS-SVM) is constructed. Then, by using the model parameters as the antibody and the importance forecast devia-tion as the antigen, the parameters of LS-SVM model are optimized by means of the clonal selection algorithm. Fi-nally, a numerical printing and die-cutting machinery of corrugated board is used as an example to verify the feasi-bility of the proposed method, and the results are compared with those obtained by the methods based on grey modeland neural network. It is found that the proposed method based on artificial immunity and LS-SVM is feasible andeffective in forecasting the importance of customer requirements.

Key words: quality function deployment, customer requirement, importance forecast, artificial immunity, least-square support vector machine

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