Improved dynamic process neural network and its application

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.