On-Line Algorithms for Initial State Identification in Linear Systems

1982 ◽  
Vol 104 (1) ◽  
pp. 114-117 ◽  
Author(s):  
H. Sehitoglu
Keyword(s):  
Linear System ◽  
State Vector ◽  
Identification Problem ◽  
Initial State ◽  
State Identification ◽  
Equation Error ◽  
Identification Technique ◽  
Parameter Identification Problem ◽  
On Line ◽  
Unknown Initial State

This paper presents on-line algorithms to identify the unknown initial state of a linear system. It is shown that estimation of an initial state vector can be treated as a parameter identification problem. The algorithms are developed by using the equation error identification technique in connection with the Lyapunov design approach. An example problem is considered to illustrate the concepts discussed in the paper.

KETEROBSERVASIAN SISTEM LINIER DISKRIT

2015 ◽  
Vol 4 (1) ◽  
pp. 108
Author(s):  
Midian Manurung
Keyword(s):  
Linear System ◽  
Discrete Time ◽  
State Vector ◽  
The State ◽  
Input Vector ◽  
Linear Control ◽  
Time Interval ◽  
Initial State ◽  
Time Invariant ◽  
Time Linear

Given the following discrete time-invariant linear control systems:where x 2 Rnx(t + 1) = Ax(t) + Bu(t);y(t) = Cx(t);is the state vector, u 2 Rmis an input vector, y 2 Rris dened as anoutput, A 2 Rnn, B 2 Rnm, and t 2 Zis dened as time. Linear system is said to beobservable on the nite time interval [t0; t+f] if any initial state xis uniquely determinedby the output y(t) over the same time interval. In order to examine the observabilityof the system, we will use a criteria, that is by determining the observability Gramianmatrix of the system is nonsingular and rank of the observability matrix for the systemis n.

Multi-Scale Virtual Reality of Sintering

2007 ◽  
Vol 534-536 ◽  
pp. 573-576
Author(s):  
Eugene Olevsky
Keyword(s):  
Global Scale ◽  
Initial State ◽  
Model Framework ◽  
Time Step ◽  
Computational Framework ◽  
Multi Scale ◽  
Scale Modeling ◽  
History Of ◽  
On Line ◽  
Damage Criteria

The directions of further developments in the modeling of sintering are pointed out, including multi-scale modeling of sintering, on-line sintering damage criteria, particle agglomeration, sintering with phase transformations. A true multi-scale approach is applied for the development of a new meso-macro methodology for modeling of sintering. The developed macroscopic level computational framework envelopes the mesoscopic simulators. No closed forms of constitutive relationships are assumed for the parameters of the material. When a time-step of the calculations is finished for one macroscopic element, the mesostructures of the next element are restored from the initial state according to the history of loading. The model framework is able to predict the final dimensions of the sintered specimen on a global scale and identify the granular structure in any localized area for prediction of the material properties.

scholarly journals Multilayer method for solving a problem of metals rupture under creep conditions

2019 ◽  
Vol 23 (Suppl. 2) ◽  
pp. 575-582 ◽  
Author(s):  
Evgenii Kuznetsov ◽  
Sergey Leonov ◽  
Dmitry Tarkhov ◽  
Alexander Vasilyev
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Parameter Identification ◽  
Uniaxial Tension ◽  
Network Modeling ◽  
Identification Problem ◽  
Neural Network Modeling ◽  
Creep Theory ◽  
Parameter Identification Problem ◽  
Kinetic Creep

The paper deals with a parameter identification problem for creep and fracture model. The system of ordinary differential equations of kinetic creep theory is applied for describing this model. As for solving the parameter identification problem, we proposed to use the technique of neural network modeling, as well as the multilayer approach. The procedures of neural network modeling and multilayer approximation constructing application is demonstrated by the example of finding parameters for uniaxial tension model for isotropic steel 45 specimens at creep conditions. The solution corresponding to the obtained parameters agrees well with theoretical strain-damage characteristics, experimental data, and results of other authors.

scholarly journals Range-relaxed criteria for choosing the Lagrange multipliers in nonstationary iterated Tikhonov method

2018 ◽  
Vol 40 (1) ◽  
pp. 606-627 ◽  
Author(s):  
R Boiger ◽  
A Leitão ◽  
B F Svaiter
Keyword(s):  
Lagrange Multipliers ◽  
Approximate Solutions ◽  
Identification Problem ◽  
Linear Operators ◽  
Type Method ◽  
Geometrical Properties ◽  
Regularization Parameters ◽  
New Strategy ◽  
Parameter Identification Problem ◽  
Ill Posed

Abstract In this article we propose a novel nonstationary iterated Tikhonov (NIT)-type method for obtaining stable approximate solutions to ill-posed operator equations modeled by linear operators acting between Hilbert spaces. Geometrical properties of the problem are used to derive a new strategy for choosing the sequence of regularization parameters (Lagrange multipliers) for the NIT iteration. Convergence analysis for this new method is provided. Numerical experiments are presented for two distinct applications: (I) a two-dimensional elliptic parameter identification problem (inverse potential problem); and (II) an image-deblurring problem. The results obtained validate the efficiency of our method compared with standard implementations of the NIT method (where a geometrical choice is typically used for the sequence of Lagrange multipliers).

scholarly journals The Pole Position in October 1980 as Determined from Lageos Laser Data

1981 ◽  
Vol 63 ◽  
pp. 139-139 ◽  
Author(s):  
Ch. Reigber ◽  
H. Mueller ◽  
W. Wende
Keyword(s):  
Time Resolution ◽  
State Vector ◽  
Pole Position ◽  
Tracking Data ◽  
Initial State ◽  
Mean Values ◽  
Laser Tracking ◽  
Laser Data ◽  
Correction Program ◽  
Short Arc

On the basis of 223 passes of Lageos Laser Tracking data taken in October 1980 during the MERIT short arc campaign from 13 tracking sites, the pole position was determined in the orbit correction program MGM along with the initial state vector. This analysis was done for a varying time resolution (5-1 days).Basis of our computation is the GRIM3P gravity model and a station position set derived by UTEX and partly by the DGF1. The formal sigma of the 5 and 2.5 mean values for the pole coordinates is generally about 0.005 arc-seconds.

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