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

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 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.

A New Approach for the Determination of Cam Follower System Parameters

1988 ◽  
Vol 202 (5) ◽  
pp. 361-368
Author(s):  
M K Baru ◽  
J Ellis
Keyword(s):  
Parametric Identification ◽  
Filter Method ◽  
New Approach ◽  
State Variable ◽  
Cam Follower ◽  
State Variable Filter ◽  
Stiffness And Damping ◽  
Design And Manufacture

This paper reports a first investigation in the use of the state variable filter method of parametric identification in the determination of the mass, stiffness and damping content of cam follower systems. Simulated and real experimental data are processed by the technique with good results. Further developments are necessary (extension to higher order systems and inclusion of Coulomb damping) with the long-term aim of applying the results in the design and manufacture of dynamically tuned cams.

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.

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