Prediction of rural residents' tourism demand based on back propagation neural network
by Jing Sun; Tao Chang
International Journal of Applied Decision Sciences (IJADS), Vol. 9, No. 3, 2016

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.

Online publication date: Tue, 20-Dec-2016

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Applied Decision Sciences (IJADS):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email