Results 111 to 120 of about 25,230 (166)

Recursive identification of Hammerstein models

2014 American Control Conference, 2014
The nonlinear Hammerstein model, which consists of a static nonlinear block followed by a linear dynamic block, is considered. A recursive prediction error algorithm is derived. The linear block is modelled as a single-input single-output transfer function, and the nonlinearity as a linear combination of basis functions.
Per Mattsson, Torbjörn Wigren
openaire   +1 more source

Recursive identification of a hybrid system

2009 European Control Conference (ECC), 2009
In this paper we consider the problem of retrieving information from a set of noisy and distorted measurements. More precisely we consider a scenario where a set of trajectories x 1 (t),…,x n (t) are observed using a single measuring device so that the output y(t i ) of the device at each sampling point is an observation of precisely one of x 1 (t),…,x
Tove Gustavi, Maja Karasalo, Xiaoming Hu 0001, Clyde F. Martin   +3 more
openaire   +1 more source

Recursive identification of lung parameters

Computer Methods and Programs in Biomedicine, 1989
Determination of lung capacity (FRC) using insoluble gas washout or equilibrium methods is a common procedure in respiratory tests. The lung model can be extended to include multiple compartments with differing volumes and ventilation fractions. A discrete-time mathematical model of a multi-compartment lung was developed based on mass conservation laws
C P, Valcke, J S, Jenkins, D S, Ward
openaire   +2 more sources

An approach to recursive subspace identification

2017 IEEE 56th Annual Conference on Decision and Control (CDC), 2017
In this paper, an approach to recursive subspace identification based on the coordinate-free framework of subspace identification is proposed. Herein, the predictor space serves as a natural basis for the formulation of a recursive approach. Compressing the necessary past information, the predictor space of a previous identification will be used for ...
Andreas Bathelt, Dirk Söffker, Mohieddine Jelali   +2 more
openaire   +1 more source

Recursive Identification of Quantized Linear Systems

Journal of Systems Science and Complexity, 2019
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Jianming Xiao, Qijiang Song
openaire   +1 more source

Recursive identification techniques

ICASSP '82. IEEE International Conference on Acoustics, Speech, and Signal Processing, 1983
Some basic results on recursive identification techniques and their properties are reviewed. The link between adaptive algorithms, recursive identification, and off-line identification is stressed. The fundamental character of the prediction and its gradient with respect to the adjustable parameters is pointed out.
openaire   +1 more source

Recursive Identification of Linear Systems

SIAM Journal on Control, 1971
Let the three matrices $\sum (N) = (G(N),F(N)H(N))$ define a linear constant system of least degree which realizes the set of numbers $f_1 , \cdots ,f_N $ regarded as a partial impulse response of a system. An algorithm has been developed for recursively calculating the minimal partial realizations for each $N = 1,2, \cdots $ such that \[ \cdots \sum {(
openaire   +1 more source

Circle criteria in recursive identification

IEEE Transactions on Automatic Control, 1997
Based on (a) positive real conditions and (b) differential sector conditions which imply (c) global convergence of recursive identification schemes (stochastic gradient and Gauss-Newton algorithms) designed from a nonlinear Wiener model (a linear dynamics followed by a static nonlinearity), one transforms (a) and (b) in circle criteria that can be used
openaire   +2 more sources

Recursive Identification of MIMO Wiener Systems

IEEE Transactions on Automatic Control, 2013
Stochastic approximation (SA) algorithms are proposed to identify a multi-input and multi-output (MIMO) Wiener system, in which the system input is taken to be a sequence of independent and identically distributed (i.i.d.) Gaussian random vectors uk ∈ N(0,I).
Bi-Qiang Mu, Han-Fu Chen
openaire   +1 more source

Recursive sparse identification for adaptive control

2017 25th Mediterranean Conference on Control and Automation (MED), 2017
Motivated by applications in adaptive control, this article compares two recursive estimation algorithms for sparse estimation of linear dynamical (ARX) models. In most practical situations an accurate mathematical model estimation of a real system using the least number of parameters is highly desirable.
Rui Bras, João Miranda Lemos
openaire   +1 more source