微型机与应用Issue(21)：51-54,4.
改进粒子群优化BP神经网络的旅游客流量预测
Prediction for tourist traffic based on improved particle swarm optimization BP neural network
摘要
Abstract
Tourist flow is influenced by many factors. The traditional time series prediction model cannot describe the laws of the forecasted object. Artificial intelligence methods such as BP neural network, the choice of its structure relies too much on experience. Based on these above, the improved particle swarm optimization (PSO) algorithm was used to optimize the BP neural network. It uses nonlinear decreasing inertia factor to improve the performance of particle swarm optimization. The prediction model is applied to the flow of Zigong Lantern Festival forecast analysis. Through simulation of 150 sets of training samples and 50 groups of test samples, the result shows that the improved method improves the accuracy of the prediction, and involves less parameters, simple and effective.关键词
旅游客流量预测/BP 神经网络/粒子群算法/非线性递减Key words
tourist flow forecast/BP neural network/particle swarm algorithm/nonlinear decreasing分类
计算机与自动化引用本文复制引用
于明涛,叶晓彤..改进粒子群优化BP神经网络的旅游客流量预测[J].微型机与应用,2015,(21):51-54,4.基金项目
四川省智慧旅游研究基地重点项目 （）
