基于融合先验知识的机器学习的多传感器融合研究

项目名称： 基于融合先验知识的机器学习的多传感器融合研究

项目编号： No.61305065

项目类型： 青年科学基金项目

立项/批准年度： 2014

项目学科： 自动化技术、计算机技术

项目作者： 孙喆

作者单位： 清华大学

项目金额： 24万元

中文摘要： 基于机器学习的多传感器融合是一类新兴的融合方法。多传感器融合问题的特点和传统机器学习的特性决定了，基于传统机器学习的多传感器融合方法面临着本质性困难，其应用受到限制。另一方面，多传感器融合问题中包含丰富的先验知识，有效地利用这些知识能显著提高机器学习和以此为基础的多传感器融合的性能。本项目拟从理论、方法、应用三个层面对基于融合先验知识的机器学习的多传感器融合进行研究。首先，对于多传感器融合问题中的两类先验知识- - 模型类知识和分布类知识，分别以适当的数学模型加以描述，在此基础上对统计机器学习误差理论加以发展，建立多传感器融合背景下的融合先验知识的机器学习的误差界理论，同时建立训练样本分布与误差评价测度不一致条件下的误差界理论；然后在误差界的指导下，并考虑实际可操作性，建立基于融合先验知识的机器学习的多传感器融合方法；最后在电磁轴承系统实验平台上对提出的方法进行应用研究。

中文关键词： 先验知识；机器学习；电磁轴承；状态估计；

英文摘要： Machine-learning-based multisensor fusion is a new class of fusion methods. The characteristics of the multisensor fusion problems and the properties of conventional machine learning methods result in the fact that the fusion methods based on conventional machine learning will confront intrinsic difficulties and the application of these methods are very limited. On the other hand, in the multisensor fusion problems, much prior knowledge are available. If this knowledge is properly incorporated into learning methods, the performance of learning and the fusion based on learning will be significantly improved. This project focuses on multisensor fusion based on machine learning with incorporation of prior knowledge and intends to perform three levels of research: research on theories, on methods and on applications. More specifically, firstly, two kinds of knowledge in multisensor fusion problems, namely knowledge of model and knowledge of distribution, should be described by proper mathematical models; based on these descriptions, the theory of error bound in statistical learning should be improved and the error bound theory of learning with incorporation of prior knowledge in the context of multisensor fusion should be proposed, meanwhile, the error bound theory in the situation that measure of training sample di

英文关键词： Prior knowledge；Machine learning；Electromagnetic bearing；State estimation；