相关实验视频
一个改进的人工大猩猩部队优化器用于BP基于神经网络的住房价格预测
1University of Birmingham Joint Institute at Jinan University, Jinan University, Jinan, China.
PloS one
|September 17, 2025
概括
这项研究引入了改进的大猩猩部队优化器 (IGTO),以提高房屋价格预测的准确性. IGTO-BPNN模型显著优于现有方法,提供更可靠的房地产市场预测.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 计算经济学计算经济学
背景情况:
- 在全球经济紧缩的背景下,准确的房价预测至关重要.
- 对于有效的预测,BP神经网络需要参数优化.
- 像GTO这样的现有元启发算法在多样性和融合方面存在局限性.
研究的目的:
- 开发一个增强的大猩猩部队优化器 (IGTO),解决GTO的局限性.
- 构建和评估基于IGTO的BP神经网络 (IGTO-BPNN) 用于房价预测.
- 为了证明IGTO-BPNN在其他算法和机器学习模型中的优势.
主要方法:
- 建议对GTO进行改进:健身分配,考奇变异和精英进化.
- 开发了一个IGTO-BPNN模型用于房价预测.
- 在四个数据集 (波士顿,加利福尼亚-海湾,加利福尼亚-陆地,台湾) 上进行了实验,并与11个群体智能和4个机器学习模型进行了比较.
主要成果:
- 在五个评估指标 (MAE,MAPE,R2,RMSE,SMAPE) 中,IGTO-BPNN表现出显著的改善.
- 具体改善率在17.66% (MAE) 到49.35% (RMSE) 之间.
- 改进后的模型始终优于现有的算法和机器学习方法.
结论:
- 拟议的IGTO有效地优化了BP神经网络参数,用于房价预测.
- 与传统方法相比,IGTO-BPNN提供了更高的准确性和有效性.
- 这个模型为导航房地产市场动态提供了一个强大的工具.
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