A P-SVM and chaos based model for high-technology manufacturing labor productivity

Author

Zhao, Huifang ; Xu, Sheng ; Yang, Changhui

Author_Institution

Sch. of Manage., Hefei Univ. of Technol., Hefei

fYear

2008

fDate

1-8 June 2008

Firstpage

327

Lastpage

331

Abstract

Computing high-technology manufacturing (HTM) productivity level and growth rate have gained a renewed interest in both growth economists and trade economists. Measuring productivity performance has become an area of concern for companies and policy makers. A novel way about nonlinear regression modeling of high-technology manufacturing (HTM) productivity with the potential support vector machines (P-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 P-SVM regression model as the objective function.

Keywords

chaos; optimisation; productivity; regression analysis; support vector machines; chaos based model; growth economist; high-technology manufacturing labor productivity; nonlinear regression model; optimization; potential support vector machines; trade economist; Aggregates; Chaos; Magnetization; Neural networks; Productivity; Research and development; Support vector machine classification; Support vector machines; Training data; Virtual manufacturing;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 2008. IJCNN 2008. (IEEE World Congress on Computational Intelligence). IEEE International Joint Conference on

Conference_Location

Hong Kong

ISSN

1098-7576

Print_ISBN

978-1-4244-1820-6

Electronic_ISBN

1098-7576

Type

conf

DOI

10.1109/IJCNN.2008.4633812

Filename

4633812