The Research of Building Logistics Cost Forecast Based on Regression Support Vector Machine

Abstract

Building logistics cost forecasting is a complicated nonlinear problem, due to the factors that influence building logistics cost are anfratuous, so it was difficult to describe it by traditional methods. The support vector machine (SVM) has the ability of strong nonlinear function approach and strong generalization and it also has the feature of global optimization, in this paper, a modeling and forecasting method of building logistics cost based on SVM is presented. The SVM network structure for forecasting building logistics cost is established. Moreover, we propose a self-adaptive parameter adjust iterative algorithm to confirm SVM parameters, thereby enhancing the convergence rate and the forecasting accuracy. We discussed and analyzed the effect factor of building logistics cost. With the ability of strong self-learning and well generalization of SVM, the modeling and forecasting method can truly forecast the building logistics cost by learning the index information. The actual forecasting results show that this method is feasible and effective.