适应性强大的随机配置网络用于近红外多变量分析
概括
本研究介绍了一种适应性强大的随机配置网络 (AR-SCN),用于近红外 (NIR) 光谱分析. 在高维数据中,AR-SCN提高了模型构建效率和稳定性.
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:
- 近红外 (NIR) 光谱被广泛使用,但面临着高维数据的挑战.
- 现有的随机配置网络 (SCN) 在NIR分析中难以实现快速的融合和强大的权重估计.
- 在NIR数据中的异常值和噪声可能会降低预测模型的性能.
研究的目的:
- 开发一种适应性强大的SCN (AR-SCN) 算法,以加速模型构建和提高高维NIR光谱分析中的性能.
- 提高NIR应用中的预测模型的稳定性和通用性.
主要方法:
- 提出了一个自适应性强大的SCN (AR-SCN) 算法.
- 基于预测剩余的实现自适应增量学习.
- 采用全球-本地收缩策略来进行可靠的输出重量估计.
主要成果:
- 该AR-SCN算法在基准NIR数据集和现实世界汽油混合过程中表现出有效性.
- 与最先进的SCNs相比,在施工效率和稳固性方面实现了同时的改进.
- 验证了该方法处理高维光谱和抵抗异常值/噪声的能力.
结论:
- 拟议的AR-SCN算法为NIR光谱分析提供了重大进展.
- 在高维建模中,AR-SCN有效地解决了现有的SCN的局限性.
- 这种方法提高了NIR应用中的预测建模的速度和可靠性.
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