Modeling and Forecasting of High-Technology Manufacturing Labor Productivity Based on Grey Support Vector Machines with Genetic Algorithms

In recent years, computing high-technology manufacturing (HTM) labor productivity (LP) level and growth rate has gained a renewed interest in both growth economists and trade economists. Measuring LP performance has become an area of concern for companies and policy makers. HTM LP is complex to conduct due to its nonlinearity of influenced factors. Support vector machines (SVM) have been successfully employed to solve nonlinear regression and time series problems. Grey system theory successfully utilizes accumulated generating data instead of original data to build forecasting model, which makes raw data stochastic weak, or reduces noise influence in a certain extent. However, the application combining grey system theory and SVM for LP forecasting is rare. In this study, a grey support vector machines with genetic algorithms (GSVMG) is proposed to forecast HTM LP. In addition, GM (1, N) model of grey system is used to add a grey layer before neural input layer and white layer after SVM layer. Genetic algorithms (GAs) are used to determine free parameters of support vector machines. Evaluation method has been used for comparing the performance of forecasting techniques. The experiments show that the GSVMG model is outperformed GM (1, N) model and SVM with genetic algorithms (SVMG) model, and HTM LP forecasting based on GSVMG is of validity and feasibility

[1]  Jan Fagerberg,et al.  Technological progress, structural change and productivity growth: a comparative study , 2000 .

[2]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

[3]  Vladimir Vapnik,et al.  The Nature of Statistical Learning , 1995 .

[4]  Michael Kuby,et al.  The effect of restructuring on US coal mining labor productivity, 1980–1995 , 2001 .

[5]  James T. Kwok.  Support vector mixture for classification and regression problems , 1998, ICPR.

[6]  Shuyan Chen,et al.  Traffic flow forecasting based on grey neural network model , 2003, Proceedings of the 2003 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.03EX693).

[7]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[8]  Peter Knights,et al.  Labor productivity and comparative advantage in mining:: the copper industry in Chile , 2001 .

[9]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[10]  Ping-Feng Pai,et al.  Forecasting regional electricity load based on recurrent support vector machines with genetic algorithms , 2005 .

[11]  Xiao-Hua Bao,et al.  Modeling and optimization of the claw-pole alternator based on support vector machines and chaos , 2005, 2005 International Conference on Machine Learning and Cybernetics.

[12]  Jung-Hsien Chiang,et al.  A fuzzy model of support vector machine regression , 2003, The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03..