Moving data window-based partially-coupled estimation approach for modeling a dynamical system involving unmeasurable states
Related Experiment Video
Updated: Oct 9, 2025

Single-Molecule Tracking Microscopy - A Tool for Determining the Diffusive States of Cytosolic Molecules
Published on: September 5, 2019
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.
Abstract:
The simultaneous parameter and state estimation for a multi-input multi-output (MIMO) state space system from a set of measurement data is taken into account in this paper. Firstly, in line with the number of the system outputs, the considered MIMO system is transformed to some subsystems, which lessens the dimensions and the number of the parameters to be estimated. Secondly, by designing the moving data window that contains the latest batch of collected data, we develop a moving data window-based partially-coupled average extended stochastic gradient algorithm for parameter estimation. Thirdly, once the parameter estimates are obtained, a new state filter is designed to produce the estimates of the unmeasurable states by means of the Kalman filtering principle. Then we propose a combined state filtering and moving data window-based partially-coupled average extended stochastic gradient (CSF-MDW-PC-A-ESG) algorithm to produce the estimates of the parameters and states simultaneously. To reveal the superiority of the CSF-MDW-PC-A-ESG algorithm, a combined state filtering and partially-coupled average extended stochastic gradient (CSF-PC-A-ESG) algorithm is given to make a comparison. Finally, the effectiveness and superiority of the proposed CSF-MDW-PC-A-ESG algorithm are proved in a simulation example. The results from the illustrative example show that the CSF-MDW-PC-A-ESG algorithm is effective to produce the estimates of the parameters and states and that the CSF-MDW-PC-A-ESG algorithm has the higher efficient data utilization, the more accurate parameter estimation capability and the better model fitting ability than the CSF-PC-A-ESG algorithm.
More Related Videos
Related Concept Videos
Linear Approximation in Time Domain
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation
On...
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
Mechanistic Models: Compartment Models in Individual and Population Analysis
Mechanistic Models: Overview of Compartment Models

