Separable Least Squares Identification of Long Memory Block Structured Models: Application to Lung Tissue Viscoelasticity

Author

Westwick, David T. ; Suki, Bela

Author_Institution

Dept. of Electr. & Comput. Eng., Schulich Sch. of Eng., Calgary, Alta.

fYear

2006

fDate

Aug. 30 2006-Sept. 3 2006

Firstpage

2130

Lastpage

2133

Abstract

A separable least squares algorithm is developed for the identification of a Wiener model whose dynamic element is a constant phase model that has been modified to include a purely viscous term. The separation of variables reduces the dimensionality of the search space from 5 to 2, greatly simplifying the optimization procedure used to estimate the parameters, The algorithm is tested on experimental stress/strain data from a strip of lung parenchyma

Keywords

biological tissues; biomechanics; least squares approximations; lung; optimisation; parameter estimation; stochastic processes; viscoelasticity; Wiener model identification; constant phase model; dynamic element; least squares identification; long memory block structured models; lung parenchyma; lung tissue viscoelasticity; nonlinear system; optimization procedure; parameter estimation; power law; search space; stress relaxation; Capacitive sensors; Elasticity; Finite impulse response filter; Least squares methods; Lungs; Nonlinear dynamical systems; Polynomials; Stress; Strips; Viscosity; Constant Phase Model; Nonlinear System; Optimization; Power Law; Stress Relaxation; Tissue Strips;

fLanguage

English

Publisher

ieee

Conference_Titel

Engineering in Medicine and Biology Society, 2006. EMBS '06. 28th Annual International Conference of the IEEE

Conference_Location

New York, NY

ISSN

1557-170X

Print_ISBN

1-4244-0032-5

Electronic_ISBN

1557-170X

Type

conf

DOI

10.1109/IEMBS.2006.260176

Filename

4462209