Adaptive prediction of stochastic differential equations with unknown parameters T. V. Dogadova, V. A. Vasiliev
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ArticleOther title: Адаптивное прогнозирование стохастических систем с непрерывным временем [Parallel title]Subject(s): адаптивные прогнозы | усеченное оценивание | системы с непрерывным временем | дифференциальные уравнения с запаздыванием | Орнштейна-Уленбека процессGenre/Form: статьи в журналах Online resources: Click here to access online
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Вестник Томского государственного университета. Управление, вычислительная техника и информатика № 38. С. 17-23Abstract: This paper proposes adaptive predictors of continuous-time dynamic systems with unknown parameters. Predictors are based on the truncated parameter estimators. In particular, there are considered the Ornstein-Uhlenbeck process and one-parameter stochastic delay differential equation. In this paper the truncated estimation method is first applied to continuous-time systems. Asymptotic and non-asymptotic properties of the predictors are investigated. There is also found the rate of convergence of the second moment of a prediction error to its minimum value.
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This paper proposes adaptive predictors of continuous-time dynamic systems with unknown parameters. Predictors are based on the truncated parameter estimators. In particular, there are considered the Ornstein-Uhlenbeck process and one-parameter stochastic delay differential equation. In this paper the truncated estimation method is first applied to continuous-time systems. Asymptotic and non-asymptotic properties of the predictors are investigated. There is also found the rate of convergence of the second moment of a prediction error to its minimum value.
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