Application of Particle Swarm Optimized Wavelet Neural Network in the Warning System of Tour Sustainable Development

In this paper, a warning system is constructed using wavelet neural network (WNN), which is trained via particle swarm optimization (PSO) algorithm. First, the history data of each index are normalized to (0, 1). Then, the weights of each index are determined by using grey relationship theory, and meanwhile, the degrees of sustainable development of each year are calculated. The sustainable development of each year is used as sample to train the WNN. The trained WNN is utilized as the warning model to predict the degree of sustainable development of future years. The proposed method is applied to the early warning of tour sustainable development of Qinhuangdao. The simulation results show the effectiveness of the proposed method.