Regularized LTI system identification in the presence of outliers: A variational EM approach

Automatica ◽  
2020 ◽  
Vol 121 ◽  
pp. 109152 ◽  
Author(s):  
Martin Lindfors ◽  
Tianshi Chen
Keyword(s):  
System Identification ◽  
Lti System ◽  
Variational Em

scholarly journals Online LTI system identification and time delay estimation

2021 ◽  
Author(s):  
Seyed Hossein Rahnamaee
Keyword(s):  
Time Delay ◽  
System Identification ◽  
Information Criterion ◽  
Order Selection ◽  
Selection Methods ◽  
Model Order Selection ◽  
Data Set ◽  
Model Order ◽  
Lti System ◽  
Selection For

Model order selection for linear time-invariant (LTI) systems is an important system modeling concern and has been widely investigated through past decades. Different approaches of order selection such as Akaike information criterion (AIC), Bayesian information criterion (BIC), minimum description length (MDL) and reconstruction error LTI system identification (RE-LTI) propose different criteria to select the optimum order of a system. In many real life applications of model order selection the size of an observed data set is increasing. Thus, order selection methods need to adopt the best fit of a model as the data set size is increasing. This is our motivation to extend RE-LTI order selection for online application of order selection with lower computational cost and complexity. It has been shown previously that AIC, BIC, two-stage MDL and many existing order selection criteria are special cases of RE-LTI method. Our online order selection approach reduces the computational complexity of the offline approach from O(N3) to O(N2). It should be noted that RE-LTI and MNDL order selection methods have same fundamentals and consequently extending RE-LTI to online RE-LTI also extends MNDL to online MNDL. Another crucial issue in system identification and modeling is estimating the time delay of a system’s impulse response (or determining the start of its non-zero part). This problem is addressed in various areas including radar, sonar, acoustic source tracking, multipath channel identification, as well as many automatic control applications. Utilizing fundamentals of RE-LTI approach, here we introduce a new time-delay estimator. Simulation results show advantages of the proposed method and its superiority to existing approaches in accuracy and robustness in terms of the FIT index.

scholarly journals Regularized LTI System Identification with Multiple Regularization Matrix

2018 ◽  
Vol 51 (15) ◽  
pp. 180-185 ◽  
Author(s):  
Tianshi Chen ◽  
Martin S. Andersen ◽  
Biqiang Mu ◽  
Feng Yin ◽  
Lennart Ljung ◽  
...  
Keyword(s):  
System Identification ◽  
Lti System ◽  
Regularization Matrix

Compact Method to Calibrate Frequency Response of Ultrasonic Transducer

2010 ◽  
Vol 171-172 ◽  
pp. 416-420
Author(s):  
Zhi Wei Hou ◽  
Min Zhao ◽  
Ai Min Dong ◽  
Qiong Wu ◽  
Qiu Feng Li
Keyword(s):  
System Identification ◽  
Transfer Function ◽  
Frequency Response ◽  
Ultrasonic Transducer ◽  
Test Signal ◽  
Experiment Data ◽  
Lti System ◽  
Test Errors ◽  
Transducer Calibration ◽  
Discrete Transfer Function

Frequency response character of ultrasonic transducer often influences on the test signal partly, and then test errors arise. To the problem, a compact method is proposed to calibrate the frequency response in this paper. The experiment data was obtained in water- immerging test of the transducers, and a discrete transfer function is established based on system identification algorithms and then used for transducer calibration. The method is validated effective by experiment. Not only can the characteristic of transducers be indicated, but also a referenced method is presented for calibrating frequency response of LTI system.

scholarly journals Online LTI system identification and time delay estimation

2021 ◽  
Author(s):  
Seyed Hossein Rahnamaee
Keyword(s):  
Time Delay ◽  
System Identification ◽  
Information Criterion ◽  
Order Selection ◽  
Selection Methods ◽  
Model Order Selection ◽  
Data Set ◽  
Model Order ◽  
Lti System ◽  
Selection For

Model order selection for linear time-invariant (LTI) systems is an important system modeling concern and has been widely investigated through past decades. Different approaches of order selection such as Akaike information criterion (AIC), Bayesian information criterion (BIC), minimum description length (MDL) and reconstruction error LTI system identification (RE-LTI) propose different criteria to select the optimum order of a system. In many real life applications of model order selection the size of an observed data set is increasing. Thus, order selection methods need to adopt the best fit of a model as the data set size is increasing. This is our motivation to extend RE-LTI order selection for online application of order selection with lower computational cost and complexity. It has been shown previously that AIC, BIC, two-stage MDL and many existing order selection criteria are special cases of RE-LTI method. Our online order selection approach reduces the computational complexity of the offline approach from O(N3) to O(N2). It should be noted that RE-LTI and MNDL order selection methods have same fundamentals and consequently extending RE-LTI to online RE-LTI also extends MNDL to online MNDL. Another crucial issue in system identification and modeling is estimating the time delay of a system’s impulse response (or determining the start of its non-zero part). This problem is addressed in various areas including radar, sonar, acoustic source tracking, multipath channel identification, as well as many automatic control applications. Utilizing fundamentals of RE-LTI approach, here we introduce a new time-delay estimator. Simulation results show advantages of the proposed method and its superiority to existing approaches in accuracy and robustness in terms of the FIT index.

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