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Single-Molecule Tracking Microscopy - A Tool for Determining the Diffusive States of Cytosolic Molecules
Published on: September 5, 2019
Moving data window-based partially-coupled estimation approach for modeling a dynamical system involving unmeasurable
Ting Cui1, Feng Ding2, Tasawar Hayat3
1Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, PR China.
This study introduces a novel algorithm for simultaneous parameter and state estimation in multi-input multi-output (MIMO) systems. The proposed method enhances data utilization and estimation accuracy compared to existing approaches.
Area of Science:
- Control Systems Engineering
- Signal Processing
- System Identification
Background:
- Simultaneous parameter and state estimation is crucial for advanced control and monitoring of complex systems.
- Existing methods often face challenges with high dimensionality and efficient data utilization in Multi-Input Multi-Output (MIMO) systems.
Purpose of the Study:
- To develop an effective algorithm for simultaneous parameter and state estimation in MIMO state space systems.
- To improve data utilization, parameter estimation accuracy, and model fitting ability.
Main Methods:
- Decomposition of MIMO systems into smaller subsystems to reduce estimation complexity.
- Development of a moving data window-based partially-coupled average extended stochastic gradient (MDW-PC-A-ESG) algorithm for parameter estimation.
- Design of a Kalman filter-based state filter for estimating unmeasurable states.
- Proposal of a combined state filtering and MDW-PC-A-ESG (CSF-MDW-PC-A-ESG) algorithm for simultaneous estimation.
Main Results:
- The proposed CSF-MDW-PC-A-ESG algorithm effectively estimates both parameters and states in MIMO systems.
- The algorithm demonstrates superior data utilization, parameter estimation accuracy, and model fitting compared to a baseline algorithm (CSF-PC-A-ESG).
Conclusions:
- The CSF-MDW-PC-A-ESG algorithm is effective and superior for simultaneous parameter and state estimation in MIMO systems.
- The method offers significant advantages in terms of efficiency and accuracy for system identification and state reconstruction.
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