A unified approach to discrete-time distributed parameter estimation by the least-squares method

DOI10.1080/00207728108963776zbMath0467.93060OpenAlexW2079134788MaRDI QIDQ3921114

John H. Seinfeld, Shigeru Omatu

Publication date: 1981

Published in: International Journal of Systems Science (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00207728108963776

zbMATH Keywords

predictionGaussian processsmoothersleast squarespredictorsdiscrete-time distributed parameter systemleast squares predictor problempartial difference-differential equations

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Gaussian processes (60G15) Filtering in stochastic control theory (93E11) Control/observation systems governed by partial differential equations (93C20) Estimation and detection in stochastic control theory (93E10)

Related Items (4)

State estimation of a non-linear stochastic distributed parameter system by discrete-time models ⋮ Discrete-time modelling of distributed parameter systems for state estimator design ⋮ Unnamed Item ⋮ Derivation of fixed-interval smoothing algorithm using covariance information in distributed parameter systems

Cites Work

• Modeling, estimation, and their applications for distributed parameter systems
• Distributed parameter filtering: boundary noise and discrete observations
• The optimal filtering problem for a discrete-time distributed parameter system
• Bayesian approach to distributed-parameter filtering and smoothing†

This page was built for publication: A unified approach to discrete-time distributed parameter estimation by the least-squares method