Estimation of non-separable regressions containing parameter dependent exponential functions

This paper presents a method for generating a separable regression function from a nonseparable one, enabling theapplication of parameter estimation methods. In particular, we are interested in regressions containing parameter-dependent exponential functions - a scenario often encountered in physical systems. Our approach is based on algebraic techniques with the so-called annihilator theory, and utilizes an intermediate approximation of the nonlinear part by a polynomial function of the time. Two operators are proposed to define the annihilators: time delays and differential operators. The efficiency of the proposed approach is demonstrated in a nonlinearly parameterized fuel cell estimation problem.

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Published
2024-12-16
DOI
https://doi.org/10.1109/cdc56724.2024.10886686
Citations
1
Primary Topic
Advanced Statistical Methods and Models
Type
article
Field-Weighted Citation Impact
0.71
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article

Estimation of non-separable regressions containing parameter dependent exponential functions

Roméo Ortega, Denis V. Efimov, Rosane Ushirobira, José Guadalupe Romero
1 citations
Advanced Statistical Methods and Models
0.71
article

Estimation of non-separable regressions containing parameter dependent exponential functions

Roméo Ortega, Denis V. Efimov, Rosane Ushirobira, José Guadalupe Romero
article en
1 citations

Abstract

This paper presents a method for generating a separable regression function from a nonseparable one, enabling theapplication of parameter estimation methods. In particular, we are interested in regressions containing parameter-dependent exponential functions - a scenario often encountered in physical systems. Our approach is based on algebraic techniques with the so-called annihilator theory, and utilizes an intermediate approximation of the nonlinear part by a polynomial function of the time. Two operators are proposed to define the annihilators: time delays and differential operators. The efficiency of the proposed approach is demonstrated in a nonlinearly parameterized fuel cell estimation problem.

Centre National de la Recherche Scientifique (FR), Université de Lille (FR)
Reduced inequalities
Openalex Percentile: Top 24%
Advanced Statistical Methods and Models
0.71
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