Title :
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.
fDate :
Aug. 30 2006-Sept. 3 2006
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;
Conference_Titel :
Engineering in Medicine and Biology Society, 2006. EMBS '06. 28th Annual International Conference of the IEEE
Conference_Location :
New York, NY
Print_ISBN :
1-4244-0032-5
Electronic_ISBN :
1557-170X
DOI :
10.1109/IEMBS.2006.260176
