The tourism demand is essential in terms of national economy and the improvement of
people’ income. But it is difficult for traditional methods to predict the tendency of the tourism
demand. In this paper, a time series prediction method based on dynamic process neural network
(DPNN) is proposed to solve this problem. An improved particle swarm optimization (IPSO) is
developed. By tuning the structure and improving the connection weights of PNN simultaneously, a
partially connected DPNN can be obtained. The effectiveness of the proposed DPNN is proved by
Henon system. Finally, the proposed DPNN is utilized to predict the tourism demand, and the test
results indicate that the proposed model seems to perform well and appears suitable for using as a
predictive maintenance tool.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.