Title: Prediction of rural residents' tourism demand based on back propagation neural network
Authors: Jing Sun; Tao Chang
Addresses: School of Mathematics and Economics, Hubei University of Education, Wuhan, China ' School of Science, Hubei University of Technology, Wuhan, China
Abstract: The accurate forecasting of the future demand for rural residents tourism provides an important basis for the sustainable development of social economy. In this paper, the authors exert an impressionable attempt to apply back propagation (BP) neural network for efficient demand forecasting of rural resident tourism. The BP neural network is a kind of multilayer feedforward network trained by the error back propagation algorithm, which is one of the most widely used neural network models. The model takes the statistical data after normalisation as BP neural network input, adjusts to the network through MATLAB simulation, and uses the trained network to forecast the demand for tourism. Results obtained from the study reveal the fact that this method is having high feasibility, fast convergence rate and high prediction accuracy, which provide a new way for the forecasting of demand of tourism.
Keywords: BP neural networks; rural residents; tourism demand; prediction models; rural communities; demand forecasting; sustainable development; sustainability; social economy; simulation.
International Journal of Applied Decision Sciences, 2016 Vol.9 No.3, pp.320 - 331
Received: 20 Jul 2016
Accepted: 09 Sep 2016
Published online: 20 Dec 2016 *