摘要
入境旅游人数的预测结果对于旅游管理部门和政府部门有着重要的参考意义,寻求科学合理的预测模型是保障预测结果准确可靠的关键。针对这一问题,作者利用人工神经网络理论建立了BP(Back-Propagation)网络预测模型,并与logistic模型、指数平滑模型、自回归模型的预测结果进行比较,结果表明应用BP神经网络对入境旅游人数进行预测精度更高、效果更好。
The result of inbound tourism population forecast can be used as a significant reference by tourism administration units and govemrnent departments. A scientific and reasonable forecast model can guarantee the accuracy and reliability of the forecast results. The author applied the artificial neural network theory to build a Back-Propagation （BP） network forecast model, and compared the forecast result with that of Logistic Model, Exponential Smoothing Model, and Autoregressive Model. It shows that the result of BP neutral network forecast model is more accurate and effective than the above mentioned traditional prediction methods.
出处
《旅游科学》
CSSCI
2006年第4期49-53,共5页
Tourism Science
基金
国家自然科学基金项目(40371044)
关键词
BP神经网络
入境旅游
预测
BP neural network
inbound tourism
forecast
