摘要
针对传统的境外旅游人数预测模型预测误差大的情况,设计了基于RBF神经网络的境外旅游人数预测模型。首先对旅游样本数据进行归一化处理,对境外旅游人数变动统计。然后通过预测种群构建、评估适应值、惩罚项设置、预测序列平稳性检验、模型预测五个过程完成了基于RBF神经网络的境外旅游人数预测模型的设计。最后,实验证明,RBF神经网络的境外旅游人数预测模型比传统的境外旅游人数预测模型误差小,能够准确对境外旅游人数预测。
in view of the large prediction error of the traditional prediction model of the number of overseas tourists,a RBF neural network prediction model of the number of overseas tourists is designed.Firstly,the tourism sample data is normalized,and the number of overseas tourists is counted.Then,the prediction model of overseas tourists based on RBF neural network is designed through five processes:prediction population construction,evaluation of fitness value,penalty item setting,prediction sequence stability test and model prediction.Finally,experiments show that the RBF neural network model designed in this paper has less error than the traditional model,and can accurately predict the number of overseas tourists.
作者
宫园园
艾宏志
GONG Yuan-yuan;AI Hong-zhi(Yulin University,Yulin,Shaanxi 719000,China)
出处
《计算技术与自动化》
2020年第4期159-162,共4页
Computing Technology and Automation
关键词
RBF神经网络
旅游
人数
预测
节点
误差
RBF neural network
tourism
population
prediction
node
error
