长春工程学院学报（自然科学版）2024，Vol.25Issue(2)：114-118,5.DOI:10.3969/j.issn.1009-8984.2024.02.020
基于PCA-BPNN算法的房价预测应用研究
Research on the Application of PCA-BPNN Algorithm in Housing Price Prediction
摘要
Abstract
Housing prices is an important factor affecting people's happiness index,so it is of great signifi-cance to predict housing prices reasonably.Taking the classic prediction datasets-the Boston House Price Datasets-as an example,an improved algorithm PCA BPNN based on principal component analysis(PCA) for a 3-layer BP neural network model is proposed for house price prediction.On the basis of data standard-ization and principal component analysis dimensionality reduction on the datasets,the prediction model is optimized by adjusting parameters such as the number of hidden layer neurons and learning times of the BP neural network model.Finally,it uses MATLAB to conduct simulation experiments on the data.The ex-perimental results show that the proposed model has improved prediction accuracy compared to the original BP neural network model,with a maximum improvement of 90.4772％.关键词
BP神经网络/房价预测/数据预处理/主成分分析/累计贡献率Key words
Back Propagation Neural Network/housing price forecast/data preprocessing/principal component analysis/cumulative contribution rate分类
信息技术与安全科学引用本文复制引用
张璐璐,麻晓敏,王星月,孙俊杰..基于PCA-BPNN算法的房价预测应用研究[J].长春工程学院学报（自然科学版）,2024,25(2):114-118,5.基金项目
安徽省职业与成人教育学会教研规划重点课题(azcg44)安徽省高校人文社会科学研究重点项目(2022AH053106)安徽省教育厅高校质量工程项目(2022jpkc041) （azcg44）
