Building Logistics Cost Forecast Based on Wavelet Neural Network

The building logistics cost forecasting was 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 wavelet neural network (WNN) has the advantages of both wavelet analysis and neural network, in this paper, a modeling and forecasting method of building logistics cost based on WNN is presented. Moreover, we adopt a algorithm of reduce the number of the wavelet basic function by analysis the sparseness property of sample data which can optimize the wavelet network in a large extent. We discussed and analyzed the effect factor of building logistics cost. With the ability of strong nonlinear function approach and fast convergence rate of WNN, 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.