A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Parameter Identification for Linear System Using Multiple Model Estimation
Kalman filter (KF) has gained wide adoption in system identification of engineering systems. It is a recursive estimation method under linear and Gaussian assumptions. In practice, a single model based on KF may not be able to capture the structural performance well for complex systems. To address this problem, KF estimation using multiple models is proposed. This method employs KF with different transition and measurement matrices, each of which can be assigned (if necessary) with different initial states, process and measurement noises to describe the system. The outputs of these models are then integrated to obtain the overall estimates through a weighted combination, where the weights are determined using the likelihood function. A numerical model is employed to illustrate the procedure and evaluate the accuracy of the proposed KF estimation with multiple models. The estimated results indicate that the proposed method is robust and reliable, with potential for system identification under a wider variety of situations.
Parameter Identification for Linear System Using Multiple Model Estimation
Kalman filter (KF) has gained wide adoption in system identification of engineering systems. It is a recursive estimation method under linear and Gaussian assumptions. In practice, a single model based on KF may not be able to capture the structural performance well for complex systems. To address this problem, KF estimation using multiple models is proposed. This method employs KF with different transition and measurement matrices, each of which can be assigned (if necessary) with different initial states, process and measurement noises to describe the system. The outputs of these models are then integrated to obtain the overall estimates through a weighted combination, where the weights are determined using the likelihood function. A numerical model is employed to illustrate the procedure and evaluate the accuracy of the proposed KF estimation with multiple models. The estimated results indicate that the proposed method is robust and reliable, with potential for system identification under a wider variety of situations.
Parameter Identification for Linear System Using Multiple Model Estimation
Lecture Notes in Civil Engineering
Reddy, J. N. (editor) / Wang, Chien Ming (editor) / Luong, Van Hai (editor) / Le, Anh Tuan (editor) / Cao, Jixing (author) / Quek, Ser-Tong (author)
2022-09-21
16 pages
Article/Chapter (Book)
Electronic Resource
English
System identification , Kalman filter , Multiple model estimation , Likelihood function Energy , Sustainable Architecture/Green Buildings , Structural Materials , Geotechnical Engineering & Applied Earth Sciences , Building Construction and Design , Construction Management , Environmental Policy , Engineering
Model Migration Schedules: Three Alternative Linear Parameter Estimation Methods
| Online Contents | 2005
Parameter estimation of the storage function model using fuzzy linear regression approach
| British Library Conference Proceedings | 2002
Structural Damage Identification Using Time-Domain Parameter Estimation Techniques
| British Library Conference Proceedings | 1999
Stochastic structural system identification. Part 2: Variance parameter estimation
| British Library Online Contents | 1999
Stochastic structural system identification. Part 1: Mean parameter estimation
| British Library Online Contents | 1999