✦ LIBER ✦

On-line identification of a class of non-linear systems from noisy measurements

✍ Scribed by D.W. Ricker; G.N. Saridis

Publisher: Elsevier Science
Year: 1971
Tongue: English
Weight: 461 KB
Volume: 7
Category: Article
ISSN: 0005-1098
DOI: 10.1016/0005-1098(71)90102-6

No coin nor oath required. For personal study only.

✦ Synopsis

Brief Paper On-line Identification of a Class of Non-linear Systems from Noisy Measurements* Identification en circuit d'une catrgorie de syst~mes non-linraires fi partir de mesures ent~chres de bruit On-line Identifikation einer Klasse von nichtlinearen Systemen aus mit Rauschen behafteten Messungen Ono3HaBaHHe B rOHWype O)~HOrO raacca HemtHefiHblX CHCTeM acxo)Ifl H3 H3MepeHrlfi c LHyMOM D. W. RICKER and G. N. SARIDIS

Summary--A method is presented for on-line parameter identification of systems in which a parameter vector is premultiplied by a matrix of non-linear state functions. Use is made of delay filters and a multiple observation technique to assure unbiased identification in the presence of measurement noise. A stochastic approximation algorithm is utilized to perform the identification and experimental results are presented for hybrid computer simulation of chemical process models.

📜 SIMILAR VOLUMES

A method for parameter estimation of a c

A method for parameter estimation of a class of non-linear distributed systems under noisy observations

✍ Yoshifumi Sunahara; Shinichi Aihara; Fumio Kojima 📂 Article 📅 1986 🏛 Elsevier Science 🌐 English ⚖ 393 KB

A method is presented for estimating an unknown parameter of a distributed parameter system which depends on the system state. The system considered is modelled by a class of non-linear partial differential equations of a parabolic type. Noisy observations are assumed to be taken through an arbitrar

On least-squares identification of stoch

On least-squares identification of stochastic linear systems with noisy input–output data

✍ Wei Xing Zheng 📂 Article 📅 1999 🏛 John Wiley and Sons 🌐 English ⚖ 119 KB 👁 2 views

In a recent paper, two least-squares (LS) based methods, which do not involve prefiltering of noisy measurements or parameter extraction, are established for unbiased identification of linear noisy input-output systems. This paper introduces more computationally efficient estimation schemes for the

Identification of a class of non-linear

Identification of a class of non-linear parametrically varying models

✍ F. Previdi; M. Lovera 📂 Article 📅 2003 🏛 John Wiley and Sons 🌐 English ⚖ 211 KB 👁 2 views

IN-SERVICE IDENTIFICATION OF NON-LINEAR

IN-SERVICE IDENTIFICATION OF NON-LINEAR DAMPING FROM MEASURED RANDOM VIBRATION

✍ D.V. IOURTCHENKO; M.F. DIMENTBERG 📂 Article 📅 2002 🏛 Elsevier Science 🌐 English ⚖ 103 KB

A non-linearly damped single-degree-of-freedom (s.d.o.f.) system under broadband random excitation is considered. A procedure for in-service identi"cation of the damping characteristic from measured stationary response is described. The procedure is based on the stochastic averaging method. The expl

ON-LINE IDENTIFICATION AND ESTIMATION OF

ON-LINE IDENTIFICATION AND ESTIMATION OF NON-LINEAR STIFFNESS PARAMETERS OF BEARINGS

✍ R. TIWARI 📂 Article 📅 2000 🏛 Elsevier Science 🌐 English ⚖ 87 KB

SUPPRESSION OF EFFECTS OF NON-LINEARITIE

SUPPRESSION OF EFFECTS OF NON-LINEARITIES IN A CLASS OF NON-LINEAR SYSTEMS BY DISTURBANCE OBSERVERS

✍ S.M. SHAHRUZ; C. CLOET; M. TOMIZUKA 📂 Article 📅 2002 🏛 Elsevier Science 🌐 English ⚖ 281 KB

Perhaps all natural and physical systems are governed by non-linear laws of nature. The dynamics of most of such systems can be mathematically represented by non-linear di!erential or integral equations, which can be studied by analytical or numerical techniques. These techniques, in many instances,