Top Tweets for #SystemIdentification
π Welcome to our new Editorial Board Member
We are pleased to welcome Prof. Dr. David Chelidze as a new Editorial Board Member of Dynamics.
π Read more: https://t.co/BeD0LscJ08
#EditorialBoard #NonlinearDynamics #Vibrations #SystemIdentification
#Callforpaper πππ
#systemidentification #AI
#seakeeping
#maneuvering
πSpecial Issue "#MachineLearning for #Prediction of #ShipMotion"
π¨βπGuest Editor:
Dr. Yangjun Ahn
Prof. Dr. Kwang-Jun Paik
π»More at: https://t.co/mGvcrX9Nz5
β°Close date: 15 September 2026
π’ #highlycited paper
πΒ #SystemIdentification and Modeling of Temperature and Flow Rate Dynamics in #FusedFilamentFabrication Process
πΒ https://t.co/o6OTsN2kmF
π¨βπ¬Β by Rakshith Badarinath et al.
π« The Pennsylvania State University
#additivemanufacturing #robotics
#Article
π Practical System Identification and Incremental Control Design for a Subscale Fixed-Wing Aircraft
by Rasmus Steffensen, Kilian Ginnell and Florian Holzapfel
π https://t.co/6tP05lW6CP
@TU_Muenchen
#incrementalflightcontrol #systemidentification #flighttestresults
[Series 4 | System Identification & Estimation | #15]
#Benchmark #Modeling #Validation #SystemIdentification
βοΈπ― How can a classic benchmark system be modeled from first principles and validated experimentally?
This work presents a complete nonlinear model of the ball-and-plate system, with validation through correlation function analysis.
π
Title: Mathematical modelling of ball and plate system with experimental and correlation function based model validation
Authors: Dil Kumar T R, Mija S J*
Full Text: https://t.co/yaPK8IwVgh
![CTT_Journal's tweet photo. [Series 4 | System Identification & Estimation | #15]
#Benchmark #Modeling #Validation #SystemIdentification
βοΈπ― How can a classic benchmark system be modeled from first principles and validated experimentally?
This work presents a complete nonlinear model of the ball-and-plate system, with validation through correlation function analysis.
π
Title: Mathematical modelling of ball and plate system with experimental and correlation function based model validation
Authors: Dil Kumar T R, Mija S J*
Full Text: https://t.co/yaPK8IwVgh](https://pbs.twimg.com/media/HGDf9TZXUAAEhxo.png)
![CTT_Journal's tweet photo. [Series 4 | System Identification & Estimation | #15]
#Benchmark #Modeling #Validation #SystemIdentification
βοΈπ― How can a classic benchmark system be modeled from first principles and validated experimentally?
This work presents a complete nonlinear model of the ball-and-plate system, with validation through correlation function analysis.
π
Title: Mathematical modelling of ball and plate system with experimental and correlation function based model validation
Authors: Dil Kumar T R, Mija S J*
Full Text: https://t.co/yaPK8IwVgh](https://pbs.twimg.com/media/HGDf9TYXkAAfdNK.png)
![CTT_Journal's tweet photo. [Series 4 | System Identification & Estimation | #15]
#Benchmark #Modeling #Validation #SystemIdentification
βοΈπ― How can a classic benchmark system be modeled from first principles and validated experimentally?
This work presents a complete nonlinear model of the ball-and-plate system, with validation through correlation function analysis.
π
Title: Mathematical modelling of ball and plate system with experimental and correlation function based model validation
Authors: Dil Kumar T R, Mija S J*
Full Text: https://t.co/yaPK8IwVgh](https://pbs.twimg.com/media/HGDf9TWW8AAw5Yh.png)
[Series 4 | System Identification & Estimation | #14]
#Biomedical #Modeling #Physiology #SystemIdentification
π«π How can we model the bodyβs oxygen response during changing exercise conditions?
This work applies non-parametric kernel methods to capture the dynamics of oxygen uptake during switching stair exercise.
π§ββοΈπ©Ίπ
Title: Oxygen uptake response to switching stairs exercise by non-parametric modeling
Authors: Hairong Yu, Chenyu Zhang, Hamzah M Alqudah, Steven Su
Full Text: https://t.co/T5dfK0p4cy
![CTT_Journal's tweet photo. [Series 4 | System Identification & Estimation | #14]
#Biomedical #Modeling #Physiology #SystemIdentification
π«π How can we model the bodyβs oxygen response during changing exercise conditions?
This work applies non-parametric kernel methods to capture the dynamics of oxygen uptake during switching stair exercise.
π§ββοΈπ©Ίπ
Title: Oxygen uptake response to switching stairs exercise by non-parametric modeling
Authors: Hairong Yu, Chenyu Zhang, Hamzah M Alqudah, Steven Su
Full Text: https://t.co/T5dfK0p4cy](https://pbs.twimg.com/media/HF3amzuasAI9ASp.png)
![CTT_Journal's tweet photo. [Series 4 | System Identification & Estimation | #14]
#Biomedical #Modeling #Physiology #SystemIdentification
π«π How can we model the bodyβs oxygen response during changing exercise conditions?
This work applies non-parametric kernel methods to capture the dynamics of oxygen uptake during switching stair exercise.
π§ββοΈπ©Ίπ
Title: Oxygen uptake response to switching stairs exercise by non-parametric modeling
Authors: Hairong Yu, Chenyu Zhang, Hamzah M Alqudah, Steven Su
Full Text: https://t.co/T5dfK0p4cy](https://pbs.twimg.com/media/HF3amySXkAAKCHk.png)
![CTT_Journal's tweet photo. [Series 4 | System Identification & Estimation | #14]
#Biomedical #Modeling #Physiology #SystemIdentification
π«π How can we model the bodyβs oxygen response during changing exercise conditions?
This work applies non-parametric kernel methods to capture the dynamics of oxygen uptake during switching stair exercise.
π§ββοΈπ©Ίπ
Title: Oxygen uptake response to switching stairs exercise by non-parametric modeling
Authors: Hairong Yu, Chenyu Zhang, Hamzah M Alqudah, Steven Su
Full Text: https://t.co/T5dfK0p4cy](https://pbs.twimg.com/media/HF3amyQWkAANYm1.png)
[Series 4 | System Identification & Estimation | #12]
#ActiveNoiseControl #Acoustics #Applications #SystemIdentification
πποΈ How can system identification help reduce real-world urban noise?
This work presents the core design of a practical Active Noise Control system, achieving up to 19 dB(A) noise reduction for traffic noise through an open window.
βοΈβοΈβοΈ
Title: Application of system identification in a practical active noise control system
Authors: Yu Yuan, Zhaoyang Sun, Hongwei Zhang
Full Text: https://t.co/OYNsguJkPD
![CTT_Journal's tweet photo. [Series 4 | System Identification & Estimation | #12]
#ActiveNoiseControl #Acoustics #Applications #SystemIdentification
πποΈ How can system identification help reduce real-world urban noise?
This work presents the core design of a practical Active Noise Control system, achieving up to 19 dB(A) noise reduction for traffic noise through an open window.
βοΈβοΈβοΈ
Title: Application of system identification in a practical active noise control system
Authors: Yu Yuan, Zhaoyang Sun, Hongwei Zhang
Full Text: https://t.co/OYNsguJkPD](https://pbs.twimg.com/media/HFpII7kakAIt_rb.jpg)
![CTT_Journal's tweet photo. [Series 4 | System Identification & Estimation | #12]
#ActiveNoiseControl #Acoustics #Applications #SystemIdentification
πποΈ How can system identification help reduce real-world urban noise?
This work presents the core design of a practical Active Noise Control system, achieving up to 19 dB(A) noise reduction for traffic noise through an open window.
βοΈβοΈβοΈ
Title: Application of system identification in a practical active noise control system
Authors: Yu Yuan, Zhaoyang Sun, Hongwei Zhang
Full Text: https://t.co/OYNsguJkPD](https://pbs.twimg.com/media/HFpII7KWsAAyV65.png)
[Series 4 | System Identification & Estimation | #7]
#SparseIdentification #ARX #MachineLearning #SystemIdentification
π§©οΏ½οΏ½ How can sparse structures be identified in multivariate dynamic systems?
This work proposes a stochastic gradient-based two-step algorithm that efficiently recovers zero parameters in ARX models.
π§π§π§
Title: A stochastic gradient-based two-step sparse identification algorithm for multivariate ARX systems
Authors: Yanxin Fu and Wenxiao Zhao
Full Text: https://t.co/Xp5489BqGP
![CTT_Journal's tweet photo. [Series 4 | System Identification & Estimation | #7]
#SparseIdentification #ARX #MachineLearning #SystemIdentification
π§©π How can sparse structures be identified in multivariate dynamic systems?
This work proposes a stochastic gradient-based two-step algorithm that efficiently recovers zero parameters in ARX models.
π§π§π§
Title: A stochastic gradient-based two-step sparse identification algorithm for multivariate ARX systems
Authors: Yanxin Fu and Wenxiao Zhao
Full Text: https://t.co/Xp5489BqGP](https://pbs.twimg.com/media/HFPHyy2WAAEI397.png)
![CTT_Journal's tweet photo. [Series 4 | System Identification & Estimation | #7]
#SparseIdentification #ARX #MachineLearning #SystemIdentification
π§©π How can sparse structures be identified in multivariate dynamic systems?
This work proposes a stochastic gradient-based two-step algorithm that efficiently recovers zero parameters in ARX models.
π§π§π§
Title: A stochastic gradient-based two-step sparse identification algorithm for multivariate ARX systems
Authors: Yanxin Fu and Wenxiao Zhao
Full Text: https://t.co/Xp5489BqGP](https://pbs.twimg.com/media/HFPHyyyWYAA1LQy.png)
[Series 4 | System Identification & Estimation | #6]
#NonlinearSystemID #Hammerstein #Noise #SystemIdentification
ποΏ½οΏ½ How can we identify nonlinear systems hidden under autoregressive output noise?
This work targets Hammerstein systems, combining correlation analysis with particle swarm optimization (PSO) to recover accurate models despite strong noise.
βοΈβοΈβοΈ
Title: Identification of the Hammerstein nonlinear system with noisy output measurements
Authors: Qiming Zha, Feng Li, Ranran Liu
Full Text: https://t.co/Luj2J64LPO
![CTT_Journal's tweet photo. [Series 4 | System Identification & Estimation | #6]
#NonlinearSystemID #Hammerstein #Noise #SystemIdentification
ππ How can we identify nonlinear systems hidden under autoregressive output noise?
This work targets Hammerstein systems, combining correlation analysis with particle swarm optimization (PSO) to recover accurate models despite strong noise.
βοΈβοΈβοΈ
Title: Identification of the Hammerstein nonlinear system with noisy output measurements
Authors: Qiming Zha, Feng Li, Ranran Liu
Full Text: https://t.co/Luj2J64LPO](https://pbs.twimg.com/media/HFIJc6eWQAASr2Z.png)
![CTT_Journal's tweet photo. [Series 4 | System Identification & Estimation | #6]
#NonlinearSystemID #Hammerstein #Noise #SystemIdentification
ππ How can we identify nonlinear systems hidden under autoregressive output noise?
This work targets Hammerstein systems, combining correlation analysis with particle swarm optimization (PSO) to recover accurate models despite strong noise.
βοΈβοΈβοΈ
Title: Identification of the Hammerstein nonlinear system with noisy output measurements
Authors: Qiming Zha, Feng Li, Ranran Liu
Full Text: https://t.co/Luj2J64LPO](https://pbs.twimg.com/media/HFIJaruWkAAyNgf.png)
![CTT_Journal's tweet photo. [Series 4 | System Identification & Estimation | #6]
#NonlinearSystemID #Hammerstein #Noise #SystemIdentification
ππ How can we identify nonlinear systems hidden under autoregressive output noise?
This work targets Hammerstein systems, combining correlation analysis with particle swarm optimization (PSO) to recover accurate models despite strong noise.
βοΈβοΈβοΈ
Title: Identification of the Hammerstein nonlinear system with noisy output measurements
Authors: Qiming Zha, Feng Li, Ranran Liu
Full Text: https://t.co/Luj2J64LPO](https://pbs.twimg.com/media/HFIJarGWQAAcI7G.png)
[Series 4 | System Identification & Estimation | #4]
#Regularization #Optimization #LeastSquares #SystemIdentification
π―οΏ½οΏ½ Can hyperparameters be selected optimallyβwithout guesswork?
This work proposes a posterior selection method for regularized least squares, enabling optimal hyperparameter choices under given constraints.
ππ
Title: The posterior selection method for hyperparameters in regularized least squares method
Authors: Yanxin Zhang, Jing Chen, Yawen Mao & Quanmin Zhu
Full Text: https://t.co/t36SRRSKRuβ
![CTT_Journal's tweet photo. [Series 4 | System Identification & Estimation | #4]
#Regularization #Optimization #LeastSquares #SystemIdentification
π―π Can hyperparameters be selected optimallyβwithout guesswork?
This work proposes a posterior selection method for regularized least squares, enabling optimal hyperparameter choices under given constraints.
ππ
Title: The posterior selection method for hyperparameters in regularized least squares method
Authors: Yanxin Zhang, Jing Chen, Yawen Mao & Quanmin Zhu
Full Text: https://t.co/t36SRRSKRuβ](https://pbs.twimg.com/media/HE4rYwcbsAAbXHl.png)
![CTT_Journal's tweet photo. [Series 4 | System Identification & Estimation | #4]
#Regularization #Optimization #LeastSquares #SystemIdentification
π―π Can hyperparameters be selected optimallyβwithout guesswork?
This work proposes a posterior selection method for regularized least squares, enabling optimal hyperparameter choices under given constraints.
ππ
Title: The posterior selection method for hyperparameters in regularized least squares method
Authors: Yanxin Zhang, Jing Chen, Yawen Mao & Quanmin Zhu
Full Text: https://t.co/t36SRRSKRuβ](https://pbs.twimg.com/media/HE4rYwVaIAEJERr.png)
[Series 4 | System Identification & Estimation | #3]
#PowerElectronics #SubspaceID #Modeling #SystemIdentification
β‘π How can we obtain accurate yet simple models for power converters?
This work develops a full-parameter constrained parsimonious subspace identification method, incorporating steady-state information for improved DCβDC converter modeling.
π§π§π§
Title: Full-parameter constrained parsimonious subspace identification with steady-state information for DC-DC converters
Authors: Jie Hou, Zhen Yang, Taifu Li, Huiming Wang, Jincheng Jiang, Xiaolei Chen
Full Text: https://rdcuβ.be/dHywA
![CTT_Journal's tweet photo. [Series 4 | System Identification & Estimation | #3]
#PowerElectronics #SubspaceID #Modeling #SystemIdentification
β‘π How can we obtain accurate yet simple models for power converters?
This work develops a full-parameter constrained parsimonious subspace identification method, incorporating steady-state information for improved DCβDC converter modeling.
π§π§π§
Title: Full-parameter constrained parsimonious subspace identification with steady-state information for DC-DC converters
Authors: Jie Hou, Zhen Yang, Taifu Li, Huiming Wang, Jincheng Jiang, Xiaolei Chen
Full Text: https://rdcuβ.be/dHywA](https://pbs.twimg.com/media/HEz0KabbQAAcYg6.png)
![CTT_Journal's tweet photo. [Series 4 | System Identification & Estimation | #3]
#PowerElectronics #SubspaceID #Modeling #SystemIdentification
β‘π How can we obtain accurate yet simple models for power converters?
This work develops a full-parameter constrained parsimonious subspace identification method, incorporating steady-state information for improved DCβDC converter modeling.
π§π§π§
Title: Full-parameter constrained parsimonious subspace identification with steady-state information for DC-DC converters
Authors: Jie Hou, Zhen Yang, Taifu Li, Huiming Wang, Jincheng Jiang, Xiaolei Chen
Full Text: https://rdcuβ.be/dHywA](https://pbs.twimg.com/media/HEz0KaTaQAAiNeY.png)
![CTT_Journal's tweet photo. [Series 4 | System Identification & Estimation | #3]
#PowerElectronics #SubspaceID #Modeling #SystemIdentification
β‘π How can we obtain accurate yet simple models for power converters?
This work develops a full-parameter constrained parsimonious subspace identification method, incorporating steady-state information for improved DCβDC converter modeling.
π§π§π§
Title: Full-parameter constrained parsimonious subspace identification with steady-state information for DC-DC converters
Authors: Jie Hou, Zhen Yang, Taifu Li, Huiming Wang, Jincheng Jiang, Xiaolei Chen
Full Text: https://rdcuβ.be/dHywA](https://pbs.twimg.com/media/HEz0KZ7WQAAeXl0.png)
Finite Sample Analysis of System Poles for Ho-Kalman Algorithm
#HoKalman #SystemIdentification #FiniteSampleAnalysis #ControlTheory #StateSpaceModels
International Robotics and Automation Awards
Visit Us: https://t.co/tsUXfhL8k8
Nomination:https://t.co/SHfgLi28AO
β‘π Design of PID Speed Controller for BLDC Motor
π Product Link:https://t.co/2io1rPWuVe
π§ PI speed controller tuned from identified transfer function
βοΈ Closed-loop BLDC drive with VSI and Hall sensors
#BLDCMotor #PIDController #SystemIdentification
#Article
π Practical System Identification and Incremental Control Design for a Subscale Fixed-Wing Aircraft
by Rasmus Steffensen, Kilian Ginnell and Florian Holzapfel
https://t.co/6tP05lW6CP
@TU_Muenchen
#incrementalflightcontrol #systemidentification #flighttestresults
6/ This work is a step towards reconciling #SciML and Modern Control.
π Read the full paper: https://t.co/FVLxFAzwYO
into #ControlTheory, #SystemIdentification & Applied Math?
Follow @AndreaGourion for more breakdowns!
Asymmetric Flow Phenomena Affecting the Characterization of the Control Plant of an Altitude Test Facility for #AircraftEngines
βοΈ Christopher Roth et al.
π https://t.co/jnhFu54D6w
Viewed: 2244; Cited: 3
#mdpisymmetry #automation #systemmodeling #systemidentification
An #Iterative Wiener Filter Based on a Fourth-Order Tensor Decomposition
βοΈ Jacob Benesty, Constantin Paleologu and Laura-Maria Dogariu
π https://t.co/wL8YeOJRyH
Viewed: 4145; Cited: 2
#mdpisymmetry #systemidentification
@ReseauUQ
@MDPIEngineering
Design PI Controller for Buck Converter (MATLAB Toolbox) β‘π§
https://t.co/xya1nbpRAX
PI controller tuning & modeling for buck converters
Boost converter efficiency, accuracy, and stability
#BuckConverter #PIcontroller #MATLAB #Simulink #SystemIdentification
Real-time Hybrid System Identification with Online Deterministic Annealing
π₯ Christos Mavridis & Karl Henrik Johansson
#AIResearch #MachineLearning #SystemIdentification #StochasticApproximation #RealTimeAlgorithms
π https://t.co/fiPGm0FUT0
Real-time Hybrid System Identification with Online Deterministic Annealing
π₯ Christos Mavridis & Karl Henrik Johansson
#AIResearch #MachineLearning #SystemIdentification #StochasticApproximation #RealTimeAlgorithms
π https://t.co/fiPGm0FUT0
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