A manufacturing labor productivity model based on chaos and PSVM with SA

Measuring productivity performance has become an area of concern for companies and policy makers. Computing high-technology manufacturing (HTM) productivity level and growth rate have gained a renewed interest in both growth economists and trade economists. A novel way about nonlinear regression modeling of high-technology manufacturing (HTM) productivity with the potential support vector machines (PSVM) is presented in this paper. Simulated annealing (SA) algorithm is employed to choose the parameters of a PSVM model. Optimization of labor productivity (LP) is also presented in this paper, which is based on chaos and uses the PSVM regression model as the objective function. The results of simulations show that the generalization performance of the methods based on PSVM with SA is higher than the ones based on standard SVM with SA.