线性建筑动力模型的同时辨识与 基于稀疏性促进优化的扰动

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摘要翻译：
我们提出了一种同时识别建筑物温度动态的线性时不变模型和影响建筑物的未测量扰动的变换版本的方法。我们的方法使用L1-正则化来鼓励识别的扰动近似稀疏，这是由决定扰动的占用率缓慢变化的性质所驱动的。该方法包括求解一个凸优化问题，该问题保证所辨识的黑箱模型具有被控对象的已知性质，特别是输入输出稳定性和正直流增益。这些特征使人们能够使用该方法作为自学习控制系统的一部分，在该系统中，建筑物的模型定期更新，而不需要人工干预。文中给出了该方法在模拟和真实建筑数据上的应用结果。
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英文标题：
《Simultaneous identification of linear building dynamic model and
  disturbance using sparsity-promoting optimization》
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作者：
Tingting Zeng, Jonathan Brooks, and Prabir Barooah
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最新提交年份：
2020
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分类信息：

一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Signal Processing        信号处理
分类描述：Theory, algorithms, performance analysis and applications of signal and data analysis, including physical modeling, processing, detection and parameter estimation, learning, mining, retrieval, and information extraction. The term "signal" includes speech, audio, sonar, radar, geophysical, physiological, (bio-) medical, image, video, and multimodal natural and man-made signals, including communication signals and data. Topics of interest include: statistical signal processing, spectral estimation and system identification; filter design, adaptive filtering / stochastic learning; (compressive) sampling, sensing, and transform-domain methods including fast algorithms; signal processing for machine learning and machine learning for signal processing applications; in-network and graph signal processing; convex and nonconvex optimization methods for signal processing applications; radar, sonar, and sensor array beamforming and direction finding; communications signal processing; low power, multi-core and system-on-chip signal processing; sensing, communication, analysis and optimization for cyber-physical systems such as power grids and the Internet of Things.
信号和数据分析的理论、算法、性能分析和应用，包括物理建模、处理、检测和参数估计、学习、挖掘、检索和信息提取。“信号”一词包括语音、音频、声纳、雷达、地球物理、生理、（生物）医学、图像、视频和多模态自然和人为信号，包括通信信号和数据。感兴趣的主题包括：统计信号处理、谱估计和系统辨识；滤波器设计；自适应滤波/随机学习；（压缩）采样、传感和变换域方法，包括快速算法；用于机器学习的信号处理和用于信号处理应用的机器学习；网络与图形信号处理；信号处理中的凸和非凸优化方法；雷达、声纳和传感器阵列波束形成和测向；通信信号处理；低功耗、多核、片上系统信号处理；信息物理系统的传感、通信、分析和优化，如电网和物联网。
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英文摘要：
  We propose a method that simultaneously identifies a linear time-invariant model of a building's temperature dynamics and a transformed version of the unmeasured disturbance affecting the building. Our method uses l1-regularization to encourage the identified disturbance to be approximately sparse, which is motivated by the slowly-varying nature of occupancy that determines the disturbance. The proposed method involves solving a convex optimization problem that guarantees the identified black-box model possesses known properties of the plant, especially input-output stability and positive DC gains. These features enable one to use the method as part of a self-learning control system in which the model of the building is updated periodically without requiring human intervention. Results from the application of the method on data from a simulated and real building are provided.
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PDF链接：
https://arxiv.org/pdf/1711.06386

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关键词：Optimization Applications SIMULTANEOUS intervention periodically 扰动 辨识 被控 方法 linear