title: Separable least squares identification of long memory block structured models: application to lung tissue viscoelasticity.
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.
authors.affiliation: Dept. of Electr. & Comput. Eng., Schulich Sch. of Eng., Calgary, Alta, Canada.
authors.forename: David T
authors.initials: DT
authors.lastname: Westwick
authors.forename: Bela
authors.initials: B
authors.lastname: Suki
mesh_terms: D000465:Algorithms; D000818:Animals; D003198:Computer Simulation; D004548:Elasticity; D006801:Humans; D016018:Least-Squares Analysis; D008168:Lung / Q000502:physiology*; D008954:Models, Biological; D015233:Models, Statistical; D013997:Time Factors; D014783:Viscosity