Automatic differential equations identification by self-configuring genetic programming algorithm

Karaseva, Tatiana

doi:10.1088/1757-899x/734/1/012093

Cited by 5 publications

(2 citation statements)

References 7 publications

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“…The authors' approach based on a self-configuring genetic programming algorithm to identify a system in ODE form of arbitrary structure is presented in detail in [19]. The paper denotes this approach as EvolODE (evolutionary approach, ordinary differential equation).…”

Section: The Evolutionary Approach Evolode For the Identification Of ...mentioning

confidence: 99%

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Evolutionary Approaches to the Identification of Dynamic Processes in the Form of Differential Equations and Their Systems

Karaseva

Semenkin

2022

Algorithms

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Evolutionary approaches are widely applied in solving various types of problems. The paper considers the application of EvolODE and EvolODES approaches to the identification of dynamic systems. EvolODE helps to obtain a model in the form of an ordinary differential equation without restrictions on the type of the equation. EvolODES searches for a model in the form of an ordinary differential equation system. The algorithmic basis of these approaches is a modified genetic programming algorithm for finding the structure of ordinary differential equations and differential evolution to optimize the values of numerical constants used in the equation. Testing for these approaches on problems in the form of ordinary differential equations and their systems was conducted. The influence of noise present in the data and the sample size on the model error was considered for each of the approaches. The symbolic accuracy of the resulting equations was studied. The proposed approaches make it possible to obtain models in symbolic form. They will provide opportunities for further interpretation and application.

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Section: The Evolutionary Approach Evolode For the Identification Of ...mentioning

confidence: 99%

“…Both proposed approaches apply a self-configuring type of evolutionary algorithms (genetic programming and differential evolution) [19,21]. The success history adaptation algorithm is applied for genetic programming and differential evolution parameters containing numerical values [23].…”

Section: The Evolutionary Approach Evolodes For the Identification Of...mentioning

confidence: 99%

Evolutionary Approaches to the Identification of Dynamic Processes in the Form of Differential Equations and Their Systems

Karaseva

Semenkin

2022

Algorithms

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Application of the evolutionary approach to structural and parametric identification of dynamic objects

Karaseva

2024

ITM Web Conf.

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The paper examines the evolutionary approach to structural and parametric identification of dynamic systems in the form of differential equations. The approach is based on a genetic programming algorithm to determine a structure of the equation and differential evolution method for parameters selection. The author proposed approach based on such evolutionary algorithms as genetic programming and differential evolution. The search for the structure is carried out by genetic programming. The selection of numerical parameters and initial conditions is implemented by a method of differential evolution. The problem of finding a model that describes changes in the efficiency of a hydraulic system is solved with the help of this approach. The proposed approach is compared with a recurrent neural network and a nonparametric kernel regression estimation.

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Application of evolutionary algorithms for the identification of the first-order differential equations systems

Karaseva

Semenkin

2023

Vii International Conference “Safety Problems of Civil Engineering Critical Infrastructures” (Spceci2021)

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Automatic differential equations identification by self-configuring genetic programming algorithm

Cited by 5 publications

References 7 publications

Evolutionary Approaches to the Identification of Dynamic Processes in the Form of Differential Equations and Their Systems

Evolutionary Approaches to the Identification of Dynamic Processes in the Form of Differential Equations and Their Systems

Application of the evolutionary approach to structural and parametric identification of dynamic objects

Application of evolutionary algorithms for the identification of the first-order differential equations systems

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