Suchergebnisse - Data-driven control with missing data

Data-driven continuous-time Hammerstein modeling with missing data using improved Archimedes optimization algorithm von Islam, Muhammad Shafiqul, Ahmad, Mohd Ashraf

“… •Data-driven modelling based on IAOA is more robust than AOA for missing data cases. This research introduces the improved Archimedes optimization algorithm (IAOA …”
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Data-driven fault diagnosis of control valve with missing data based on modeling and deep residual shrinkage network von Sun, Feng, Xu, He, Zhao, Yu-han, Zhang, Yu-dong

“… ) is proposed to solve the problem that data-driven models for control valves are susceptible to changing operating conditions and missing data …”
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A Missing Data Approach to Data-Driven Filtering and Control von Markovsky, Ivan

“… , the model is not optimized for its intended use. This technical note proposes an approach for data-driven filtering and control that combines the identification and the model-based design into one joint problem …”
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Behavioral systems theory in data-driven analysis, signal processing, and control von Markovsky, Ivan, Dörfler, Florian

“… Till recently, it was an unorthodox niche of research but has gained renewed interest for the newly emerged data-driven paradigm, for which it is uniquely suited due to the representation-free …”
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Data‐driven Buck converter model identification method with missing outputs von Hou, Jie, Zhang, Xinhua, Wang, Huiming, Wang, Shiwei

“… A data‐driven Buck converter model identification method is proposed to deal with missing (incomplete …”
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Data-driven adaptive-critic optimal output regulation towards water level control of boiler-turbine systems von Wei, Qinglai, Wang, Xin, Liu, Yu, Xiong, Gang

“… The problem is solved by employing an approximate optimal regulator that consist of optimal feedback control obtained by a data-driven learning algorithm and optimal feedforward control achieved …”
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A study of data-driven distributionally robust optimization with incomplete joint data under finite support von Ren, Ke, Bidkhori, Hoda

“… •Addressing the missing data issue in data-driven stochastic programming problems …”
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Kalman Filter-Based Data-Driven Robust Model-Free Adaptive Predictive Control of a Complicated Industrial Process von Zhou, Ping, Zhang, Shuai, Wen, Liang, Fu, Jun, Chai, Tianyou, Wang, Hong

“… In this article, a novel Kalman filter-based robust model-free adaptive predictive control (MFAPC) method is proposed for the direct data-driven control of molten iron quality in BF ironmaking …”
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Data-driven dynamic interpolation and approximation von Markovsky, Ivan, Dörfler, Florian

“… These “data-driven” representations led in turn to new system identification, signal processing, and control methods …”
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Subspace identification for data-driven modeling and quality control of batch processes von Corbett, Brandon, Mhaskar, Prashant

“… In this work, we present a novel, data‐driven, quality modeling, and control approach for batch processes …”
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Rebooting data-driven soft-sensors in process industries: A review of kernel methods von Liu, Yiqi, Xie, Min

“… However, nonlinear, non-stationary, ill-data, auto-correlated and co-correlated behaviors in industrial data always make general data-driven methods inadequate, thus resorting to kernel-based methods …”
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A review of the Expectation Maximization algorithm in data-driven process identification von Sammaknejad, Nima, Zhao, Yujia, Huang, Biao

“… The Expectation Maximization (EM) algorithm has been widely used for parameter estimation in data-driven process identification …”
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Data-driven Soft Sensors in the process industry von Kadlec, Petr, Gabrys, Bogdan, Strandt, Sibylle

“… which are related to process control. This paper discusses characteristics of the process industry data which are critical for the development of data-driven Soft Sensors …”
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Synchrophasor Missing Data Recovery via Data-Driven Filtering von Konstantinopoulos, Stavros, De Mijolla, Genevieve M., Chow, Joe H., Lev-Ari, Hanoch, Wang, Meng

“… ) to monitor dynamic behaviors. For real-time applications, the PMU data are streamed via the Internet from the substations to the phasor data concentrators, in the control centers …”
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Time Series Data-Driven Online Prognosis of Wind Turbine Faults in Presence of SCADA Data Loss von Zhu, Lipeng, Zhang, Xinran

“… To address this issue, this paper develops an intelligent time series (TS) data analytics approach for missing data-tolerant WTFP …”
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Data-driven soft sensor approach for online quality prediction using state dependent parameter models von Bidar, Bahareh, Sadeghi, Jafar, Shahraki, Farhad, Khalilipour, Mir Mohammad

“… The goal of this paper is to design and implementation of a new data-driven soft sensor that uses state dependent parameter (SDP …”
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A standardized workflow for long-term longitudinal actigraphy data processing using one year of continuous actigraphy from the CAN-BIND Wellness Monitoring Study von Slyepchenko, Anastasiya, Uher, Rudolf, Ho, Keith, Hassel, Stefanie, Matthews, Craig, Lukus, Patricia K., Daros, Alexander R., Minarik, Anna, Placenza, Franca, Li, Qingqin S., Rotzinger, Susan, Parikh, Sagar V., Foster, Jane A., Turecki, Gustavo, Müller, Daniel J., Taylor, Valerie H., Quilty, Lena C., Milev, Roumen, Soares, Claudio N., Kennedy, Sidney H., Lam, Raymond W., Frey, Benicio N.

“… In this study, we used a data-driven approach to quality control, pre-processing and analysis of longitudinal actigraphy data collected over the course of 1 …”
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A data-driven persistence test for robust (probabilistic) quality control of measured environmental time series: constant value episodes von Kaffashzadeh, Najmeh

“… Robust quality control is a prerequisite and an essential component in any data application …”
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Data-driven sensor delay estimation in industrial processes using multivariate projection methods von Offermans, Tim, van Son, Bente, Bertinetto, Carlo G., Bot, Arjen, Brussee, Rogier, Jansen, Jeroen J.

“… A key step in preparing industrial data for multivariate statistical modelling of (batch-)continuous processes is the estimation of sensor delays along the production line, to use as a correction for understanding relationships …”
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A Hybrid Missing Data Imputation Method for Batch Process Monitoring Dataset von Gan, Qihong, Gong, Lang, Hu, Dasha, Jiang, Yuming, Ding, Xuefeng

“… Batch process monitoring datasets usually contain missing data, which decreases the performance of data-driven modeling for fault identification and optimal control …”
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