학술논문
Missing Output Identification Model Based Recursive Least Squares Algorithm for a Distributed Parameter System
Document Type
Article
Source
(2022): 150-157.
Subject
Language
Korean
ISSN
15986446
Abstract
This paper proposes a recursive least squares algorithm for a distributed parameter system with missing observations. By using the finite difference method, the distributed parameter system can be turned into a lumped parameter system. Then a missing output identification model based recursive least squares algorithm is derived to estimate the unknown parameters of the lumped parameter system. Furthermore, the parameters of the distributed parameter system can be computed by the estimated parameters of the lumped parameter system. The simulation results indicate that the proposed method is effective.
This paper proposes a recursive least squares algorithm for a distributed parameter system with missing observations. By using the finite difference method, the distributed parameter system can be turned into a lumped parameter system. Then a missing output identification model based recursive least squares algorithm is derived to estimate the unknown parameters of the lumped parameter system. Furthermore, the parameters of the distributed parameter system can be computed by the estimated parameters of the lumped parameter system. The simulation results indicate that the proposed method is effective.
This paper proposes a recursive least squares algorithm for a distributed parameter system with missing observations. By using the finite difference method, the distributed parameter system can be turned into a lumped parameter system. Then a missing output identification model based recursive least squares algorithm is derived to estimate the unknown parameters of the lumped parameter system. Furthermore, the parameters of the distributed parameter system can be computed by the estimated parameters of the lumped parameter system. The simulation results indicate that the proposed method is effective.