旅游关联数据实体模型的模糊决策仿真研究

Research on Fuzzy Decision and Simulation of Tourism Related Data Entity Model

对诸如交通、季节、物流和人流等旅游关联数据的实体模型构建和模糊决策算法设计,是现实旅游规划和管理的基础。传统的旅游关联数据实体模型挖掘和决策算法采用的是支持向量回归机学习的模糊决策模型实现对旅游关联数据的实体模型构建方法,参数设置复杂,并具有不稳定性。提出一种基于粒子群参数寻优的旅游关联数据实体模型的模糊决策算法。首先进行旅游关联数据的实体模型构建,采用粒子群参数寻优方法进行旅游关联数据信息流的预测算法设计,设计PSO-SVR模型,在Matlab平台上编写程序并迭代计算,运用PSO算法对SVR模型进行参数寻优,由此实现了对旅游关联数据粒子群寻优和模糊决策。仿真结果表明,该算法能有效修正因季节等关联因素和原因产生的旅游预测值偏高或偏低现象,旅游客流量预测均方误差明显降低,决策控制稳定性较好,提高旅游规划和管理能力。

The construction and the design of fuzzy decision algorithm of entity model such as traffic, logistics and the flow of the tourism season, associated data, is the reality of the tourism planning and management. With tourism related entity data model mining and decision algorithm of traditional is the construction method of entity model of tourism related data fuzzy decision model of support vector regression machine learning implementations, parameter setting is complex, and has not stability. Put forward the fuzzy decision algorithm of a kind of tourism related data entities based on particle swarm pa-rameters optimization model. Firstly constructing solid model of tourism related data, design of predictive algorithm particle swarm parameters optimization methods for tourism related data information flow using the PSO-SVR model, design, pro-gramming and iterative computation on the Matlab platform, using PSO algorithm for parameters of SVR model optimiza-tion, thus realized to tourism related data of particle swarm optimization and fuzzy decision. The simulation results show that, the algorithm can effectively modify due to seasonal and other related factors and reasons of tourism forecasting value of high or low phenomenon, tourism passenger flow forecast mean square error decreases, decision control stability is better, improve the tourism planning and management ability.