摘要
为进一步提高住宅价格预测精度,进而为有关部门提供相关数据参考,帮助其及时准确地制定相关政策,提出了基于Pearson系数和萤火虫算法优化BP神经网络的住宅价格预测模型。该模型首先使用Pearson系数对影响房价的相关因素进行特征筛选,舍弃与住宅价格关联性不强的因素;然后使用萤火虫算法对BP神经网络进行优化,建立基于萤火虫算法优化的BP神经网络预测模型;最后以上海市统计局最新发布的相关数据进行实验验证。实验结果表明,所提模型优于其他5种模型,能够实现住宅价格的有效预测。
In order to further improve the accuracy of housing price prediction,provide data reference for relevant departments and help relevant departments timely and accurately formulate relevant policies,a housing price prediction model based on Pearson coefficient and firefly algorithm optimized BP neural network is proposed.In this model,Pearson coefficient is firstly used to screen the features of relevant factors affecting housing price and the factors that are not strongly correlated with housing price are discarded.Then the firefly algorithm is used to optimize the BP neural network and the prediction model based on the firefly algorithm optimization is established.Finally,the relevant data released by Shanghai Municipal Bureau of Statistics were used for experimental verification.The experimental results show that the proposed model is superior to the other five models and can effectively predict the housing price.
作者
江雨燕
刘昊
JIANG Yu-yan;LIU Hao(School of Management Science and Engineering,Anhui University of Technology,Maanshan 243000,China)
出处
《南阳理工学院学报》
2023年第2期1-6,24,共7页
Journal of Nanyang Institute of Technology
