Modeling and optimization of high-technology manufacturing productivity

Authors:
Sheng Xu;Hui-Fang Zhao;Zhao-Hua Sun;Xiao-Hua Bao
Affiliations:
School of Management, Hefei University of Technology, Hefei, China;School of Management, Hefei University of Technology, Hefei, China;School of Management, Xi’an Jiaotong University, Xi’an, China;School of Electrical Engineering and Automatization, Hefei University of Technology, Hefei, China
Venue:
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
Year:
2006

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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.