Statement and solution of the identification problem of regression analysis method
Background. The object of the research is discrete and continuous models of nonlinear dynamic objects. The subject of the research is the method of constructing models using direct and inverse Laplace transforms, decomposition of the model into linear and nonlinear components, decomposition of th...
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| Format: | Article |
| Language: | English |
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Penza State University Publishing House
2022-04-01
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| Series: | Известия высших учебных заведений. Поволжский регион:Технические науки |
| Subjects: |
| _version_ | 1818523988474724352 |
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| author | P.P. Makarychev S.V. Shibanov A.Yu. Afonin |
| author_facet | P.P. Makarychev S.V. Shibanov A.Yu. Afonin |
| author_sort | P.P. Makarychev |
| collection | DOAJ |
| description | Background. The object of the research is discrete and continuous models of nonlinear
dynamic objects. The subject of the research is the method of constructing models using
direct and inverse Laplace transforms, decomposition of the model into linear and nonlinear
components, decomposition of the linear part of the model into input and output components.
The purpose of this research is to develop a method that provides the construction of
both discrete and continuous models of dynamic nonlinear objects for solving problems of
structural and parametric identification of parameters by the method of regression analysis of
time series based on the results of recording the values of input and output signals with a given
time interval. Materials and methods. Developing a method for identifying structures and
parameters of dynamic objects’ models, the main provisions of the theory of systems, direct
and inverse Laplace transforms, the theory of constructing discrete models, regression and
system analysis of time series were used. Results. A method for identifying structures, parameters
of discrete and continuous models of objects using regression analysis has been developed.
When identifying models, the method provides a search for the number and values of
poles, zeros of the transfer function, non-linearity coefficients of the object according to the
criterion of the minimum standard deviation of the calculated values from the recorded values
of the output signal. Conclusions. The method provides identification of the structure and
parameters of discrete and continuous models by the criterion of the minimum standard deviation
of the recorded and calculated values of the output signal. The application of the method
of constructing and transforming models is possible in combination with various methods of
integrating time series. |
| first_indexed | 2024-12-11T05:51:30Z |
| format | Article |
| id | doaj.art-6bb0a57526554136ac993a6d6cb25728 |
| institution | Directory Open Access Journal |
| issn | 2072-3059 |
| language | English |
| last_indexed | 2024-12-11T05:51:30Z |
| publishDate | 2022-04-01 |
| publisher | Penza State University Publishing House |
| record_format | Article |
| series | Известия высших учебных заведений. Поволжский регион:Технические науки |
| spelling | doaj.art-6bb0a57526554136ac993a6d6cb257282022-12-22T01:18:49ZengPenza State University Publishing HouseИзвестия высших учебных заведений. Поволжский регион:Технические науки2072-30592022-04-01110.21685/2072-3059-2022-1-1Statement and solution of the identification problem of regression analysis methodP.P. Makarychev0S.V. Shibanov1A.Yu. Afonin2Penza State UniversityPenza State UniversityPenza State UniversityBackground. The object of the research is discrete and continuous models of nonlinear dynamic objects. The subject of the research is the method of constructing models using direct and inverse Laplace transforms, decomposition of the model into linear and nonlinear components, decomposition of the linear part of the model into input and output components. The purpose of this research is to develop a method that provides the construction of both discrete and continuous models of dynamic nonlinear objects for solving problems of structural and parametric identification of parameters by the method of regression analysis of time series based on the results of recording the values of input and output signals with a given time interval. Materials and methods. Developing a method for identifying structures and parameters of dynamic objects’ models, the main provisions of the theory of systems, direct and inverse Laplace transforms, the theory of constructing discrete models, regression and system analysis of time series were used. Results. A method for identifying structures, parameters of discrete and continuous models of objects using regression analysis has been developed. When identifying models, the method provides a search for the number and values of poles, zeros of the transfer function, non-linearity coefficients of the object according to the criterion of the minimum standard deviation of the calculated values from the recorded values of the output signal. Conclusions. The method provides identification of the structure and parameters of discrete and continuous models by the criterion of the minimum standard deviation of the recorded and calculated values of the output signal. The application of the method of constructing and transforming models is possible in combination with various methods of integrating time series.nonlinear dynamic objectparametric identificationleast squares regression analysisdiscrete and continuous object models |
| spellingShingle | P.P. Makarychev S.V. Shibanov A.Yu. Afonin Statement and solution of the identification problem of regression analysis method Известия высших учебных заведений. Поволжский регион:Технические науки nonlinear dynamic object parametric identification least squares regression analysis discrete and continuous object models |
| title | Statement and solution of the identification problem of regression analysis method |
| title_full | Statement and solution of the identification problem of regression analysis method |
| title_fullStr | Statement and solution of the identification problem of regression analysis method |
| title_full_unstemmed | Statement and solution of the identification problem of regression analysis method |
| title_short | Statement and solution of the identification problem of regression analysis method |
| title_sort | statement and solution of the identification problem of regression analysis method |
| topic | nonlinear dynamic object parametric identification least squares regression analysis discrete and continuous object models |
| work_keys_str_mv | AT ppmakarychev statementandsolutionoftheidentificationproblemofregressionanalysismethod AT svshibanov statementandsolutionoftheidentificationproblemofregressionanalysismethod AT ayuafonin statementandsolutionoftheidentificationproblemofregressionanalysismethod |