Parameter Estimation for Nonlinear Models with Emphasis on Compartmental Models

Biometrics ◽  
1983 ◽  
Vol 39 (3) ◽  
pp. 629 ◽  
Author(s):  
David M. Allen
Keyword(s):  
Parameter Estimation ◽  
Nonlinear Models ◽  
Compartmental Models

scholarly journals Set-membership identifiability of nonlinear models and related parameter estimation properties

2016 ◽  
Vol 26 (4) ◽  
pp. 803-813 ◽  
Author(s):  
Carine Jauberthie ◽  
Louise Travé-MassuyèEs ◽  
Nathalie Verdière
Keyword(s):  
Mathematical Model ◽  
Parameter Estimation ◽  
Dynamic System ◽  
Nonlinear Models ◽  
Differential Algebra ◽  
Related Parameter ◽  
Set Membership ◽  
Estimation Problems ◽  
The Mathematical Model

Abstract Identifiability guarantees that the mathematical model of a dynamic system is well defined in the sense that it maps unambiguously its parameters to the output trajectories. This paper casts identifiability in a set-membership (SM) framework and relates recently introduced properties, namely, SM-identifiability, μ-SM-identifiability, and ε-SM-identifiability, to the properties of parameter estimation problems. Soundness and ε-consistency are proposed to characterize these problems and the solution returned by the algorithm used to solve them. This paper also contributes by carefully motivating and comparing SM-identifiability, μ-SM-identifiability and ε-SM-identifiability with related properties found in the literature, and by providing a method based on differential algebra to check these properties.

scholarly journals Design of delayed fractional state variable filter for parameter estimation of fractional nonlinear models

2018 ◽  
Vol 94 (4) ◽  
pp. 2697-2713 ◽  
Author(s):  
Walid Allafi ◽  
Ivan Zajic ◽  
Kotub Uddin ◽  
Zhonghua Shen ◽  
James Marco ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Nonlinear Models ◽  
State Variable ◽  
State Variable Filter

Parameter Estimation in Compartmental Models

2002 ◽  
pp. 307-335
Author(s):  
Claudio Cobelli ◽  
David Foster ◽  
Gianna Toffolo
Keyword(s):  
Parameter Estimation ◽  
Compartmental Models

scholarly journals Combinatorial Algorithm In Linear Model

2018 ◽  
Vol 196 ◽  
pp. 03017
Author(s):  
Jana Ižvoltová ◽  
Peter Pisca
Keyword(s):  
Parameter Estimation ◽  
Linear Model ◽  
Markov Models ◽  
Nonlinear Models ◽  
The Other ◽  
Estimation Methods ◽  
Combinatorial Algorithm ◽  
Alternative Approach ◽  
Parameter Estimation Methods ◽  
Iterative Numerical Methods

Gauss-jacobi combinatorial algorithm is an alternative approach to traditional iterative numerical methods, which is primary oriented for parameter estimation in nonlinear models. The combinatorial algorithm is often exploited for outlier diagnosis in nonlinear models, where the other parameter estimation methods lose their efficiency. The paper describes comparison of both of gauss-jacobi combinatorial and gauss-markov models executed on parameter estimation process of levelling network for the reason to find the efficiency of combinatorial algorithm in simply linear model.

Parameter Estimation for Linear Compartmental Models—A Sensitivity Analysis Approach

2009 ◽  
Vol 37 (5) ◽  
pp. 1028-1042 ◽  
Author(s):  
Barbara Juillet ◽  
Cécile Bos ◽  
Claire Gaudichon ◽  
Daniel Tomé ◽  
Hélène Fouillet
Keyword(s):  
Sensitivity Analysis ◽  
Parameter Estimation ◽  
Compartmental Models ◽  
Analysis Approach

SOME REMARKS ON STRUCTURE SELECTION FOR NONLINEAR MODELS

1994 ◽  
Vol 04 (06) ◽  
pp. 1707-1714 ◽  
Author(s):  
LUIS ANTONIO AGUIRRE
Keyword(s):  
Parameter Estimation ◽  
Nonlinear Model ◽  
Nonlinear Models ◽  
Sampling Period ◽  
Information Criteria ◽  
Structure Selection ◽  
Selection For ◽  
Selection Of

This note is concerned with structure selection of nonlinear models. By structure selection it is meant the choice of the model basis prior to parameter estimation. It is argued that the effect of the sampling period and the noise on the data may in some cases preclude adequate structure selection. Other issues which are briefly discussed include the selection of the sampling period and the effectiveness of information criteria in indicating the best size of a nonlinear model.

Modeling of Oil-Film Forces in Squeeze-Film Bearings

1986 ◽  
Vol 108 (2) ◽  
pp. 262-269 ◽  
Author(s):  
C. R. Burrows ◽  
M. N. Sahinkaya ◽  
N. C. Kucuk
Keyword(s):  
Parameter Estimation ◽  
System Performance ◽  
Critical Speed ◽  
Nonlinear Models ◽  
Squeeze Film ◽  
Model Structure ◽  
Oil Film ◽  
Model Oil ◽  
Stiffness And Damping ◽  
Parameter Estimation Techniques

The role played by bearings in determining the dynamic characteristics of rotor-bearing systems is well known. This has led to various attempts to model oil-film force coefficients in terms of linearized stiffness and damping elements. The inadequacy of these theoretical coefficients to predict performance under certain conditions has led some authors to propose the use of nonlinear models. An alternative philosophy, developed in this paper, is to retain a linear model structure and seek to determine optimized coefficient values using modern parameter estimation techniques. It is shown that these estimated linearized parameters predict system performance more accurately than the theoretical linear coefficients; particularly when the rotor is operating near a critical speed.

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