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Modeling and Optimization of High-Technology Manufacturing Productivity

  • Sheng Xu
  • Hui-Fang Zhao
  • Zhao-Hua Sun
  • Xiao-Hua Bao
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3973)

Abstract

As more and more industries experience the globalization of business activities, measuring productivity performance has become an area of concern for companies and policy makers in Europe, the United States, Japan and so on. A novel way about nonlinear regression modeling of high-technology manufacturing (HTM) productivity with the support vector machines (SVM) is presented in this paper. Optimization of labor productivity (LP) is also presented in this paper, which is based on chaos and uses the SVM regression model as the objective function.

Keywords

Support Vector Machine Labor Productivity Productivity Growth Labor Productivity Growth Support Vector Machine Regression 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Sheng Xu
    • 1
    • 2
  • Hui-Fang Zhao
    • 1
  • Zhao-Hua Sun
    • 2
  • Xiao-Hua Bao
    • 3
  1. 1.School of ManagementHefei University of TechnologyHefeiChina
  2. 2.School of ManagementXi’an Jiaotong UniversityXi’anChina
  3. 3.School of Electrical Engineering and AutomatizationHefei University of TechnologyHefeiChina

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