EAGER:Incremental and Distributed Learning in Nonstationary Environments with Applications to Wind Forecasting

EAGER：非平稳环境中的增量和分布式学习及其在风预报中的应用

• 批准号: 0938344
• 负责人: Anthony Kuh
• 金额: $ 15.03万
• 依托单位: University of Hawaii
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0938344&HistoricalAwards=false
• 关键词: EAGER; Incremental; Distributed; Learning; Nonstationary

Project SummaryThis proposal examines novel on-line kernel learning algorithms for applications in renewable energy. The PI has experience and previous funded NSF research in the machine learning and signal processing areas. The PI is now moving into new areas of renewable energy (specifically wind forecasting) attempting to do both basic and applied research in this area by applying machine learning and signal processing knowledge. This EAGER proposal provides an opportunity to move into this area of renewable energy and sustainability that is of key importance both nationally and in the state of Hawaii (with the Hawaii Clean Energy Initiative (goal of having 70% of energy generated from renewable sources and energy efficient practices by 2030). This proposal will also assist the PI in becoming a technical and administrative leader in the renewable energy area as he collaborates with other University of Hawaii researchers and industry leaders in pursuing more research and educational opportunities. Intellectual Merit: The PI has previously studied least squares kernel algorithms, but there are still concerns about their implementation. This proposal addresses many of these issues by considering algorithms that balance performance, space complexity, and computational complexity. We develop a suite of on-line kernel algorithms varying from subspace least squares algorithms to variants of kernel LMS algorithms. The on-line kernel algorithms work in the dual space where dimensionality of systems increase as we add training examples. Data must be windowed. For subspace algorithms the number of support vectors, and the dimensionality of the matrices, LS is controlled to balance performance and complexity. We also develop distributed on-line kernel algorithms combining results from signal processing and machine learning. The distributed algorithms are ensemble learning algorithms that are well suited for operation in sensor networks where information is gathered in a distributed manner. The sensor networks may have physical constraints (communication and energy costs) associated which make distributed processing and learning more attractive. To understand these different algorithms we analyze their performance and determine the complexity of the different algorithms. A system identification approach is used to analyze the mean and mean squared error performance of algorithms. We also study the convergence properties of the adaptive on-line learning algorithms. Distributed learning algorithms performance, complexity, and physical costs are studied and analyzed. Some of the distributed learning algorithms are similar to boosting and we study the convergence behavior of these algorithms. We also look at the performance of learning algorithms in nonstationary environments. A focus will be on studying the performance of on-line kernel learning algorithms in drifting environments. With a development and analysis of the on-line kernel learning algorithms we will be in a better position to apply the learning algorithms in a variety of applications. In particular we look at applying these algorithm to wind forecasting. We have some preliminary results in this area that show that on-line kernel algorithms can accurately make short term wind prediction. We will consider prediction from multiple sensors using distributed kernel algorithms and longer term prediction. Distributed on-line kernel algorithms are also well suited to extract other information from sensor networks. Broader Impacts: The proposal is an integrated research and educational effort that can have major impacts to machine learning, signal processing, and renewable energy. Research from this proposal will add to the understanding and adoption of adaptive on-line nonlinear kernel algorithms in different application areas. As the United States and Hawaii move towards more clean energy solutions (renewable energy and energy efficiency) more intelligence will need to be deployed in making decisions about energy storage and usage. This proposal shows how machine learning algorithms combined with signal processing can be used for accurate wind prediction. The proposed research will also have major educational benefits to both undergraduate and graduate students at the University of Hawaii. Special effort will be given to work with the Native Hawaiian Science and Engineering Mentorship Program (NHSEMP) in the College of Engineering to encourage Native Hawaiian, Pacific Islander, and women students to enter graduate research programs. This proposal will complement other group proposals that the PI has recently written in the renewable energy area. A goal is to have the University of Hawaii, College of Engineering being a leader player in using information technology in the development of clean energy solutions. The PI will also look at collaborations with international researchers in Imperial College, London and Japan.

本提案研究了可再生能源领域应用的新型在线核学习算法。PI在机器学习和信号处理领域有经验，以前资助过NSF的研究。PI现在正在进入可再生能源的新领域（特别是风力预测），试图通过应用机器学习和信号处理知识在该领域进行基础和应用研究。这项EAGER提案为进入可再生能源和可持续发展领域提供了一个机会，这对全国和夏威夷州都至关重要(夏威夷清洁能源倡议（目标是到2030年可再生能源和节能实践产生70%的能源）。该提案还将帮助PI成为可再生能源领域的技术和管理领导者，因为他与夏威夷大学的其他研究人员和行业领导者合作，寻求更多的研究和教育机会。智力优势：PI以前研究过最小二乘核算法，但仍然存在对其实现的担忧。本提案通过考虑平衡性能、空间复杂性和计算复杂性的算法来解决许多这些问题。我们开发了一套在线核算法，从子空间最小二乘算法到核LMS算法的变体。在线核算法在对偶空间中工作，当我们增加训练样本时，系统的维数会增加。数据必须有窗口。对于子空间算法，控制支持向量的数量和矩阵的维数，LS以平衡性能和复杂性。我们还开发了分布式在线核算法，结合了信号处理和机器学习的结果。分布式算法是集成学习算法，非常适合在以分布式方式收集信息的传感器网络中运行。传感器网络可能有物理限制（通信和能源成本），这使得分布式处理和学习更有吸引力。为了理解这些不同的算法，我们分析了它们的性能并确定了不同算法的复杂度。采用系统辨识的方法分析了算法的平均误差和均方误差性能。研究了自适应在线学习算法的收敛性。对分布式学习算法的性能、复杂性和物理成本进行了研究和分析。一些分布式学习算法类似于boosting算法，我们研究了这些算法的收敛行为。我们还研究了学习算法在非平稳环境中的性能。重点是研究在线核学习算法在漂移环境中的性能。通过对在线核学习算法的开发和分析，我们将能够更好地将学习算法应用于各种应用中。我们特别关注将这些算法应用于风力预报。我们在这方面已经有了一些初步的结果，表明在线核算法可以准确地进行短期风的预测。我们将考虑使用分布式核算法和长期预测从多个传感器进行预测。分布式在线核算法也适用于从传感器网络中提取其他信息。更广泛的影响：该提案是一项综合研究和教育工作，可以对机器学习，信号处理和可再生能源产生重大影响。本文的研究将有助于对自适应在线非线性核算法在不同应用领域的理解和采用。随着美国和夏威夷向更清洁的能源解决方案（可再生能源和能源效率）迈进，在制定能源储存和使用决策时，需要部署更多的智能。该提案展示了如何将机器学习算法与信号处理相结合用于准确的风力预测。这项拟议中的研究还将对夏威夷大学的本科生和研究生都有重大的教育意义。将特别努力与工程学院的夏威夷土著科学和工程指导计划（NHSEMP）合作，鼓励夏威夷土著、太平洋岛民和女学生参加研究生研究项目。该提案将补充PI最近在可再生能源领域撰写的其他小组提案。目标是让夏威夷大学工程学院成为利用信息技术开发清洁能源解决方案的领导者。该项目还将考虑与帝国理工学院、伦敦和日本的国际研究人员合作。