基于回归向量的加权平均值的规范化随机配置网络
Yang Wang1, Tao Zhou1, Guanci Yang2
1State Key Laboratory of Public Big Data, Guizhou University, Guiyang, Guizhou, China.
PeerJ. Computer science
|June 22, 2023
概括
这项研究引入了一种新的规范化随机配置网络 (RSCN-INFO),该网络通过向量加权平均值 (INFO) 算法进行了优化. RSCN-INFO提高了预测准确度和融合速度,以实现高效的数据建模.
科学领域:
- 机器学习 机器学习
- 人工智能的人工智能
- 计算科学 计算科学
背景情况:
- 随机配置网络 (SCN) 提供快速的数据建模,但对参数设置敏感.
- 由于参数调整,现有的SCN模型在预测准确性和融合率方面面临挑战.
- 群体智能算法为复杂模型提供了新的优化方法.
研究的目的:
- 通过整合矢量的加权平均值 (INFO) 算法,开发一种新的规范化SCN (RSCN-INFO).
- 优化参数选择和网络结构,以提高SCN性能.
- 提高SCN模型的预测准确度和趋同率.
主要方法:
- 引入了一个规范化术语,将回归和残余错误反结合到SCN目标函数中.
- 采用矢量的加权平均值 (INFO) 算法来自动探索一个四维参数矢量.
- 实施了INFO的三阶段优化程序:更新规则,矢量组合和本地搜索.
主要成果:
- 拟议的RSCN-INFO在参数设置和网络紧性方面表现出卓越的性能.
- 与其他算法相比,实现了更快地减少网络剩余错误.
- 在基准数据集上显示了增强的融合率和预测准确性.
结论:
- RSCN-INFO有效地优化了SCN参数选择和网络结构.
- 整合INFO算法显著提高了SCN的性能.
- 拟议的方法为高效和准确的数据建模提供了一个有前途的方法.
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