Identification of distributed-parameter systems from sparse measurements

Hidayat Z.; Babuška Robert; Núñez Alfredo; De Schutter B.

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Identification of distributed-parameter systems from sparse measurements

机译：从稀疏测量中识别分布参数系统

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摘要

In this paper, a method for the identification of distributed-parameter systems is proposed, based on finite-difference discretization on a grid in space and time. The method is suitable for the case when the partial differential equation describing the system is not known. The sensor locations are given and fixed, but not all grid points contain sensors. Per grid point, a model is constructed by means of lumped-parameter system identification, using measurements at neighboring grid points as inputs. As the resulting model might become overly complex due to the involvement of neighboring measurements along with their time lags, the Lasso method is used to select the most relevant measurements and so to simplify the model. Two examples are reported to illustrate the effectiveness of the method, a simulated two-dimensional heat conduction process and the construction of a greenhouse climate model from real measurements.

机译：提出了一种基于时空网格的有限差分离散化的分布式参数系统辨识方法。该方法适用于描述系统的偏微分方程未知的情况。传感器位置已给出并固定，但并非所有网格点都包含传感器。对于每个网格点，通过集总参数系统识别，使用相邻网格点处的测量值作为输入来构建模型。由于所产生的模型可能由于邻近测量值及其时间滞后的影响而变得过于复杂，因此使用Lasso方法选择最相关的测量值，从而简化了模型。据报道有两个例子来说明该方法的有效性，一个模拟的二维热传导过程和根据实际测量结果建立温室气候模型。

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来源
《Applied Mathematical Modelling》 |2017年第11期|605-625|共21页
作者
Hidayat Z.; Babuška Robert; Núñez Alfredo; De Schutter B.;
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作者单位

Delft Center for Systems and Control, Delft University of Technology, Mekelweg 2, Delft, Netherlands;

Delft Center for Systems and Control, Delft University of Technology, Mekelweg 2, Delft, Netherlands;

Section of Railway Engineering, Delft University of Technology, Stevinweg 1, Delft, Netherlands;

Delft Center for Systems and Control, Delft University of Technology, Mekelweg 2, Delft, Netherlands;

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收录信息美国《科学引文索引》(SCI);美国《工程索引》(EI);
原文格式 PDF
正文语种 eng
中图分类
关键词
Finite-difference method; Greenhouse climate model; Indoor climate modeling; Input selection; System identification;

机译：有限差分法;温室气候模型;室内气候模拟;输入选择;系统识别;

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1. SOLVING DIRECT AND INVERSE DYNAMIC PROBLEMS FOR DISTRIBUTED-PARAMETER SYSTEMS: IDENTIFICATIONAL PSEUDOINVERSE APPROACH [J] . V. V. Skopetskii, V. A. Stoyan, V. B. Zvaridchuk Cybernetics and Systems Analysis . 2004,第4期

机译：解决分布式参数系统的直接和逆动力学问题：拟伪逆方法
2. Filtering for linear distributed-parameter systems via boundary measurements [J] . Atre S.R., Lamba S.S. Electrical Engineers, Proceedings of the Institution of . 1972,第6期

机译：通过边界测量对线性分布参数系统进行滤波
3. Feasibility study of Monte Carlo modelling techniques for distributed-parameter systems. Part 2: Search problem involving a distributed-parameter system solved by Monte Carlo methods [J] . Edwards K.H., Khanna R.R. Electrical Engineers, Proceedings of the Institution of . 1970,第12期

机译：蒙特卡罗建模技术在分布式参数系统中的可行性研究。第2部分：涉及通过蒙特卡洛方法解决的分布式参数系统的搜索问题
4. Identification of distributed-parameter systems with missing data [C] . Hidayat Z., Nunez A., Babuska R., 2012 IEEE International Conference on Control Applications. . 2012

机译：识别缺少数据的分布式参数系统
5. Compressive System Identification (CSI): Theory and applications of exploiting sparsity in the analysis of high-dimensional dynamical systems. [D] . Sanandaji, Borhan M. 2013

机译：压缩系统识别（CSI）：利用稀疏性进行高维动力系统分析的理论和应用。
6. Outlier-Detection Methodology for Structural Identification Using Sparse Static Measurements [O] . Marco Proverbio, Numa J. Bertola, Ian F. C. Smith 2018

机译：使用稀疏静态测量进行结构识别的异常值检测方法
7. Robust Inference of Autonomic Nervous System Activation Using Skin Conductance Measurements: A Multi-Channel Sparse System Identification Approach [O] . MD. Rafiul Amin, Rose T. Faghih 2019

机译：使用皮肤电导测量的自主神经系统激活的鲁棒推理：多通道稀疏系统识别方法

Identification of distributed-parameter systems from sparse measurements

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