摘要
极限学习机(ELM)作为一种单隐层前馈神经网络已成为大数据分析的重要工具。与传统神经网络相比,ELM具有结构简单、学习速度快和推广性较好等优势。但是,ELM的输出权值是基于最小二乘法估计的,容易夸大离群点和噪声的影响,导致其预测性能的不稳定。提出一种新的稳健的极限学习机——基于最小一乘回归的极限学习机(LAD-ELM),而且问题被转化为线性规划,能够简单、快速求解其全局最优解。进一步将LAD-ELM应用于近红外光谱数据建模,构建了基于LAD-ELM和近红外光谱数据的乌拉尔甘草种子硬实性分析系统。与传统的方法相比,在不同光谱范围的数值实验显示了提出方法的可行性和有效性,为利用近红外光谱和ELM技术进行种子硬实性研究提供了理论依据和实用方法。
Extreme learning machine （ELM）, as a kind of single hidden layer feedforword neural networks, is an important tool in big data analysis. Compared with traditional neural network methods, it has simple structure, high learning speed and good generalization performance. However, the output weight of ELM is estimated by the least squares estimation （LSE） method, and thus ELM network lacks of robustness since LSE is relatively sensitive to outlier. A new robust ELM based on least absolute deviations （LAD） regression, called LAD-ELM, is presented. Moreover, the proposed LAD-ELM is posed as a linear program with global optimal solution. Furthermore, the proposed LAD-ELM is directly used for near-infrared （NIR） spectral analysis, and an analysis system for hardness of licorice seeds is built based on LAD-ELM and NIR data. Compared with the traditional methods, the experimental results in different spectral regions show the feasibility and effectiveness of the proposed method. Moreover, the investigation provides theoretical support and practical method for studies on licorice seed hardness using ELM and NIR technology.
出处
《激光与光电子学进展》
CSCD
北大核心
2015年第10期285-290,共6页
Laser & Optoelectronics Progress
基金
国家自然科学基金(11471010
11271367)
关键词
光谱学
近红外光谱
极限学习机
最小一乘回归
稳健性
spectroscopy
near-infrared spectroscopy
extreme learning machine
least absolute deviationregression
robustness
