Robust Neuroevolutionary Identification of Nonlinear Nonstationary Objects

Rudenko, Oleg; Bezsonov, Oleksandr

doi:10.1007/s10559-014-9589-5

Cited by 4 publications

(5 citation statements)

References 23 publications

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“…The practical application of the considered functionals for solving the identification problem was considered in many works. In particular, in [21][22][23][24] the robust approach was applied to the identification of nonlinear systems. For this purpose, radial basis function networks [21,22], evolving networks [23], and evolving radial basis function networks [24] were used.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

“…In particular, in [21][22][23][24] the robust approach was applied to the identification of nonlinear systems. For this purpose, radial basis function networks [21,22], evolving networks [23], and evolving radial basis function networks [24] were used. Learning of these networks was carried out on the basis of minimizing the robust functionals considered above.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

See 1 more Smart Citation

Robust identification of non-stationary objects with nongaussian interference

Rudenko¹,

Bezsonov²,

Lebediev³

et al. 2019

EEJET

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Section: Literature Review and Problem Statementmentioning

confidence: 99%

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Robust identification of non-stationary objects with nongaussian interference

Rudenko¹,

Bezsonov²,

Lebediev³

et al. 2019

EEJET

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“…Although there are some recommendations for choosing these parameters, in most cases they are chosen based on the experience of the researcher [9]. Some practical recommendations have been developed in [10][11][12] for the choice of functional parameters for robust neural network training. The more common problem of robust estimation in the presence of interference with asymmetrical distributions was investigated in [13].…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

“…Many of the tasks related to information processing (identification, management, forecasting, classification, filtering, etc.) [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15] are reduced to constructing and analyzing a model in the following form…”

Section: Introductionmentioning

confidence: 99%

Developing a multi-step recurrent algorithm to maximize the criteria of correntropy

Rudenko

Bezsonov

Borysenko

et al. 2021

EEJET

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This paper considers the task of constructing a linear model of the object studied using a robust criterion. The functionality applied, in this case, is correntropy. That makes it possible to obtain estimates that have robust properties. The evaluation algorithm is a multi-step procedure that employs a limited number of information measurements, that is, it has limited memory. The feature of the algorithm is that the matrices and observation vectors involved in estimate construction are formed in the following way: they include information about the newly arrived measurements and exclude information about the oldest ones. Depending on the way these matrices and vectors are built (new information is added first, and then outdated is excluded, or the outdated is first excluded, and then a new one is added), two estimate forms are possible. The second Lyapunov method is used to study the convergence of the algorithm. The conditions of convergence for a multi-step algorithm have been defined. The analysis of the established regime has revealed that the algorithm ensures that unbiased estimates are obtained. It should be noted that all the estimates reported in this work depend on the choice of the width of the nucleus, the information weighting factor, and the algorithm memory, the task of determining which remains open. Therefore, these parameters' estimates should be applied for the practical use of such multi-step algorithms. The estimates obtained in this paper allow the researcher to pre-evaluate the possibilities of identification using a multi-step algorithm, as well as the effectiveness of its application when solving practical tasks

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“…In most cases, however, they are selected based on the experience of the researcher [9]. The task of robust neural network training based on the functionals [6,7] by Huber and Hempel is considered in [10][11][12]; some practical recommendations on the choice of the functionals' parameters are devised. A more general issue of robust assessment in the presence of interference with asymmetrical distributions was investigated in [13].…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Studying the properties of a robust algorithm for identifying linear objects, which minimizes a combined functional

Rudenko

Bezsonov

Lebediev

et al. 2020

EEJET

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This paper addresses the task of identifying the parameters of a linear object in the presence of non-Gaussian interference. The identification algorithm is a gradient procedure for minimizing the combined functional. The combined functional, in turn, consists of the fourth-degree functional and a modular functional, whose weights are set using a mixing parameter. Such a combination of functionals makes it possible to obtain estimates that demonstrate robust properties. We have determined the conditions for the convergence of the applied procedure in the mean and root-mean-square measurements in the presence of non-Gaussian interference. In addition, expressions have been obtained to determine the optimal values of the algorithm's parameters, which ensure its maximum convergence rate. Based on the estimates obtained, the asymptomatic and non-asymptotic values of errors in estimating the parameters and identification errors. Because the resulting expressions contain a series of unknown parameters (the values of signal and interference variances), their practical application requires that the estimates of these parameters should be used. We have investigated the issue of stability of the steady identification process and determined the conditions for this stability. It has been shown that determining these conditions necessitates solving the third-degree equations, whose coefficients depend on the specificity of the problem to be solved. The resulting ratios are rather cumbersome but their simplification allows for a qualitative analysis of stability issues. It should be noted that all the estimates reported in this work depend on the choice of a mixing parameter, the task of determining which remains to be explored. The estimates obtained in this paper allow the researcher to pre-evaluate the capabilities of the identification algorithm and the effectiveness of its use in solving practical problems

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Robust Neuroevolutionary Identification of Nonlinear Nonstationary Objects

Cited by 4 publications

References 23 publications

Robust identification of non-stationary objects with nongaussian interference

Robust identification of non-stationary objects with nongaussian interference

Developing a multi-step recurrent algorithm to maximize the criteria of correntropy

Studying the properties of a robust algorithm for identifying linear objects, which minimizes a combined functional

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