✦ LIBER ✦

Least-squares filtering and smoothing for linear distributed parameter systems

✍ Scribed by J.S. Meditch

Publisher: Elsevier Science
Year: 1971
Tongue: English
Weight: 628 KB
Volume: 7
Category: Article
ISSN: 0005-1098
DOI: 10.1016/0005-1098(71)90123-3

No coin nor oath required. For personal study only.

📜 SIMILAR VOLUMES

Least squared estimation for distributed

Least squared estimation for distributed parameter systems with uncertain observations: Part 1: linear prediction and filtering

✍ García-Ligero, M. J. ;Hermoso, A. ;Linares, J. 📂 Article 📅 1998 🏛 John Wiley and Sons 🌐 English ⚖ 80 KB 👁 2 views

The least mean squared error linear one-stage predictor and filter are derived for discrete-time distributed parameter systems with uncertain observations. The measurements are taken at several fixed points of the spatial domain. We have used an orthogonal projection approach.

Sequential square root filtering and smo

Sequential square root filtering and smoothing of discrete linear systems

✍ Gerald J. Bierman 📂 Article 📅 1974 🏛 Elsevier Science 🌐 English ⚖ 979 KB

Optimal state filtering and parameter id

Optimal state filtering and parameter identification for linear systems

✍ Michael Basin; Joel Perez; Mikhail Skliar 📂 Article 📅 2008 🏛 John Wiley and Sons 🌐 English ⚖ 99 KB

Semidiscrete least squares methods for l

Semidiscrete least squares methods for linear hyperbolic systems

✍ Tsu-Fen Chen 📂 Article 📅 1992 🏛 John Wiley and Sons 🌐 English ⚖ 685 KB

Least-squares parameter estimation for s

Least-squares parameter estimation for systems with irregularly missing data

✍ Feng Ding; Jie Ding 📂 Article 📅 2009 🏛 John Wiley and Sons 🌐 English ⚖ 180 KB 👁 2 views

Robust failure detection for linear dist

Robust failure detection for linear distributed parameter systems

✍ Sheldon X. C. Lou; Jiong Jiang 📂 Article 📅 1991 🏛 John Wiley and Sons 🌐 English ⚖ 878 KB

This paper discusses the robust failure detection methods in general separable Hilbert space using an operator approximation approach. It is simple and provides an intuitive geometric interpretation. The computational procedure required is the Schmit expansion of an operator. An example arising from