使用GA-PSO-BP神经网络模型预测中国二手房价
Jining Wang1, Huabin Ji1, Lei Wang1
1School of Economics and Management, Nanjing Tech University, Nanjing, China.
PloS one
|May 7, 2025
概括
本研究引入了一种新的GA-PSO-BP神经网络模型,以提高房价预测的准确性. 改进的模型克服了传统遗传算法的局限性,为二手房屋提供可靠的预测.
科学领域:
- 计算智能是一种计算智能.
- 房地产经济学 房地产经济学
- 机器学习 机器学习
背景情况:
- 传统的遗传算法经常面临过早的融合,影响房价预测的可靠性.
- 准确的房价预测对于房地产投资和城市规划至关重要.
研究的目的:
- 开发和评估一种新的GA-PSO-BP神经网络模型,用于增强房价预测.
- 解决现有的算法在预测二手房价的局限性.
主要方法:
- 将基因粒子群优化 (GA-PSO) 与反向传播 (BP) 神经网络集成.
- 使用来自Lianjia.com (2023-2024) 的1,824个二手房屋交易数据集.
- 分析影响中国房价的关键因素.
主要成果:
- 该GA-PSO-BP模型在复杂的高维数据上表现出优异的预测性能.
- 在测试组件上获得0.786的根平均平方误差 (RMSE) 和8.9%的平均绝对百分比误差 (MAPE).
- 通过单个算法优化的传统BP神经网络表现优于传统BP神经网络.
结论:
- GA-PSO-BP神经网络模型显著降低了二手房价预测错误.
- 在广州等快速增长的城市地区提供更准确的价格预测.
- 为动态市场的房地产投资者提供有价值的见解.
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