The APA based time-variant system identification

Semushin, I. V.

doi:10.1109/cdc.2014.7040033

Cited by 6 publications

(3 citation statements)

References 7 publications

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“…Thus, we develop the existing APA approach for the systems with the high level of uncertainty. In all previous related works [Semushin, 2011a;Semushin, 2011b;Tsyganova, 2011;Semushin and Tsyganova, 2013;Semushin, 2014;Semushin et al, 2018] it was assumed that exogenous inputs are known or they are Gaussian white noises.…”

Section: Resultsmentioning

confidence: 99%

“…In this paper, for solving the parameter identification problem, we use an alternative approach that is the Active Principle of Adaptation (APA) [Semushin, 2011a;Semushin, 2011b;Semushin and Tsyganova, 2013;Semushin, 2014]. The main idea for it is to construct an auxiliary identification criterion J ε (θ) [Tsyganova, 2011;Semushin and Tsyganova, 2013;Semushin et al, 2018] which is instrumental because it depends on only directly observed values and can be minimized with the use of a known numerical optimization methods.…”

Section: Parameter Identification Problemmentioning

confidence: 99%

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Parameter identification of the linear discrete-time stochastic systems with unknown exogenous inputs

Tsyganova,

Tsyganov

2023

CAP

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The paper addresses a parameter identification problem for linear discrete-time stochastic systems with unknown exogenous inputs. Such systems are considered when solving practical problems related to the measurements processing in the case when it is impossible to do any assumptions about the evolution of unknown input signal or its statistical characteristics that can change over time. We consider a class of discrete time linear stochastic systems with unknown exogenous inputs, where an additional source of a priori uncertainty of the system model is introduced, namely, the unknown parameter, on the elements of which the system model matrices can depend. This formulation of the parameter identification problem under the conditions of unknown inputs and the presence of random noises describes a high degree of uncertainty of a discrete time linear stochastic system. We propose a novel solution to this problem based on the construction of a new instrumental identification criterion. Minimization of this criterion allows for evaluating the unknown system model parameters simultaneously with the estimating of the state vector and unknown exogenous inputs of the system. Numerical experiments confirm the validity and efficiency of the proposed parameter identification method.

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Section: Resultsmentioning

confidence: 99%

Section: Parameter Identification Problemmentioning

confidence: 99%

Parameter identification of the linear discrete-time stochastic systems with unknown exogenous inputs

Tsyganova,

Tsyganov

2023

CAP

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“…2), в которых неопределенность описания выражена наличием неизвестных значений параметров θ Обычно уравнения продиктованы действием тех или иных законов физики (или живой природы, если есть веские основания их применять). Поэтому в литературе для представления источника данных (управляемой части системы) применяют физически структурированное параметризованное (ФСП) описание, или ФСП-модель, или «серый ящик» в отличие от «черного ящика» -полностью неизвестного описания (Merc`ere et al, 2014). С конца 1960-х годов ряд решений для идентификации систем был построен и впоследствии описан в известных монографиях (Graupe, 1976;Ljung & Söderström, 1983;Ljung, 1987;Södersröm & Stoica, 1989).…”

Section: идентификация управляемой модели системыunclassified