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- W2189245005 abstract "This report is devoted to developing of an approach to construction of an optimal model reproducing basic dynamical properties of the system underlying observed time series. We represent the evolution operator of investigated system in a form of RDS including deterministic and stochastic parts, both parameterized by artificial neural network. Then we use Bayesian information criterion for estimation of optimal complexity of the model, i.e. both number of parameters and dimension corresponding to most probable model yielding the analyzed process. We demonstrate on the number of model examples that the model with non-uniformly distributed stochastic part (which corresponds to non-Gaussian perturbations of evolution operator) is optimal in general case. Further, we show that simple stochastic model (including a few variables) can be most preferred for reconstruction of the evolution operator underlying complex observed dynamics even in a case of deterministic high-dimensional system. Workability of suggested approach for modeling and prognosis of real-measured geophysical dynamics is investigated." @default.
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- W2189245005 date "2011-01-01" @default.
- W2189245005 modified "2023-10-14" @default.
- W2189245005 title "Reconstruction of a dynamical system underlying an observed time series by optimal stochastic models" @default.
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