首页|Data-driven discovery of linear dynamical systems from noisy data
Data-driven discovery of linear dynamical systems from noisy data
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In modem science and engineering disciplines,data-driven discovery methods play a fundamental role in system modeling,as data serve as the external representations of the intrinsic mechanisms within systems.However,empirical data contaminated by process and measurement noise remain a significant obstacle for this type of modeling.In this study,we have developed a data-driven method capable of directly uncovering linear dynamical systems from noisy data.This method combines the Kalman smoothing and sparse Bayesian learning to decouple process and measurement noise under the expectation-maximization frame-work,presenting an analytical method for alternate state estimation and system identification.Furthermore,the discovered model explicitly characterizes the probability distribution of process and measurement noise,as they are essential for filtering,smooth-ing,and stochastic control.We have successfully applied the proposed algorithm to several simulation systems.Experimental results demonstrate its potential to enable linear dynamical system discovery in practical applications where noise-free data are intractable to capture.
system identificationsparse Bayesian learningKalman smoothingprocess and measurement noise
WANG YaSen、YUAN Ye、FANG HuaZhen、DING Han
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School of Mechanical Science and Engineering,Huazhong University of Science and Technology,Wuhan 430074,China
State Key Lab of Digital Manufacturing Equipment and Technology,Huazhong University of Science and Technology,Wuhan 430074,China
School of Artificial Intelligence and Automation,Huazhong University of Science and Technology,Wuhan 430074,China
Department of Mechanical Engineering,University of Kansas,Lawrence 66045,USA
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