极限学习机拓展研究及其在近红外光谱分析中的应用

项目名称： 极限学习机拓展研究及其在近红外光谱分析中的应用

项目编号： No.11471010

项目类型： 面上项目

立项/批准年度： 2015

项目学科： 数理科学和化学

项目作者： 杨丽明

作者单位： 中国农业大学

项目金额： 70万元

中文摘要： 极限学习机是一种单隐层前馈神经网络，它较传统神经网络具有结构简单，训练速度快和良好的泛化性能等优点.因此，近年来极限学习机已成为大数据学习的热点并广泛应用。然而，但对于不同的数据集和不同的应用领域，无论极限学习机用于数据分类还是回归，传统的极限学习机还存在不足,例如在处理噪音数据、不确定性数据、高维数和大数据等问题。为了解决这些问题和进一步提高极限学习机的泛化能力，本项目以最优化理论和方法为基础，拓展现有极限学习机，构建若干个新的最优化模型及其有效算法,包括鲁棒性极限学习机、半监督极限学习机、稀疏极限学习机、不确定性极限学习机和集成极限学习机等，力求理论上的创新和算法有效性。并构建玉米种子近红外光谱无损检测系统，寻求玉米种子近红外光谱与其生理生化指标间的数量关系，进行图谱分析。这将促进近红外光谱技术发展，反之丰富和完善极限学习机和最优化理论。

中文关键词： 极限学习机；最优化；机器学习；数据挖掘；近外光谱分析

英文摘要： Extreme learning machine（ELM）is a kind of single layer feedforward neural networks. Comparing with traditional neural network algorithms，it is simpler in structure，with higher learning speed and good generalization performance．Thus ELM has become a popular topic for solving big data learning, and it has been widely applied in many fields．However, for different data setting and different applications, it is used for both data classification or regression，the traditional ELM encounters some problems such as dealing with the noise data, uncertain data and high-dimension data and big data etc. Thus, in order to solve these problems and improve ELM'generalization, this project presents some new ELM models based on optimization theory and method. These extension researches for ELM include semi-supervised ELM, uncertain ELM, robust ELM, sparse ELM and ensemble ELM. Moreover, theoretical innovation and effective algorithm are used as our goal in this study. And the proposed ELM models are directly applied to the non-destructive testing of maize seeds using near-infrared (NIR) spectroscopy analysis, in order to find the quantitative relation between the NIR spectrum of maize seed and its the physiological and biochemical indexes, and interpret NIR spectra. This will promote NIR spectroscopy analysis, and on the contrary, enrich and improve ELM theory.

英文关键词： Extreme learning machine;optimization;machine learning;data mining;near infrared spectroscopy