The Research of Building Logistics Cost Forecast Based on Radial Basic Probabilistic Neural Network

The 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. Radial basic probabilistic neural network (RBPNN) is one of the neural networks used widely and it has the ability of strong function approach and fast convergence, in this paper, a modeling and forecasting method of building logistics cost based on RBPNN is presented. We construct the structure of radial basic probabilistic neural network that used for forecasting building logistics cost, and adopt the K-Nearest Neighbor algorithm and least square method to train the network. We discussed and analyzed the effect factor of building logistics cost. With the ability of strong function approach and fast convergence of radial basic probabilistic neural network, 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.