基于随机配置网络的光谱数据的定量分析
Lixin Zhang1,2,3, Zhensheng Huang1, Xiao Zhang2
1School of Mathematics and Statistics, Nanjing University of Science and Technology, Nanjing 210014, Jiangsu 210014, China. stahzs@126.com.
概括
随机配置网络 (SCN) 为定量光谱数据分析提供了一种新的方法. 这种方法将线性模型的速度与非线性模型的准确性相结合,证明了卓越的预测能力.
科学领域:
- 化学测量 化学测量 化学测量
- 机器学习 机器学习
- 频谱学是一种光谱学.
背景情况:
- 在光谱数据分析中的传统线性模型是快速的,但对非线性问题缺乏准确性.
- 非线性模型提供更高的准确性,但可以是缓慢的,容易局部最佳.
- 需要采用混合方法来利用线性和非线性方法的优势.
研究的目的:
- 将静态配置网络 (SCN) 引入化学测量中,这是一种单一隐藏层前神经网络.
- 分析SCN模型终结参数,包括容错率和最大隐藏节点.
- 确定最佳的随机配置设置,以提高SCN的效率和稳定性.
主要方法:
- 实施用于定量光谱数据分析的随机配置网络 (SCN).
- 分析和确定关键的SCN参数:容错率,最大隐藏节点和随机配置代.
- 在两个公共光谱数据集上验证SCN性能,与主要组件回归 (PCR),部分最小平方 (PLS),逆向传播神经网络 (BPNN) 和极端学习机器 (ELM) 进行比较.
主要成果:
- 在光谱数据集上,SCN表现出良好的稳定性和高预测准确性.
- 与其他测试技术相比,SCN方法表现出更高的效率.
- 确定了SCN的最佳模型终结和随机配置参数.
结论:
- 随机配置网络 (SCN) 对于光谱数据的定量分析是有效的.
- 在化学测量应用中,SCN提供了一个强大的替代方案,平衡速度和准确性.
- 这些发现支持SCN对于复杂的光谱数据挑战的适用性.
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