Identification of Stochastically Perturbed Autonomous Systems from Temporal Sequences of Probability Density Functions
The paper introduces a method for reconstructing one-dimensional iterated maps that are driven by an external control input and subjected to an additive stochastic perturbation, from sequences of probability density functions that are generated by the stochastic dynamical systems and observed experi...
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| Veröffentlicht in: | Journal of nonlinear science 2018, Vol.28 (4), p.1467-1487 |
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| container_end_page | 1487 |
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| container_issue | 4 |
| container_start_page | 1467 |
| container_title | Journal of nonlinear science |
| container_volume | 28 |
| creator | Nie, Xiaokai Luo, Jingjing Coca, Daniel Birkin, Mark Chen, Jing |
| description | The paper introduces a method for reconstructing one-dimensional iterated maps that are driven by an external control input and subjected to an additive stochastic perturbation, from sequences of probability density functions that are generated by the stochastic dynamical systems and observed experimentally. |
| doi_str_mv | 10.1007/s00332-018-9455-0 |
| format | Article |
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| language | eng |
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| source | SpringerNature Journals |
| subjects | Analysis Classical Mechanics Economic Theory/Quantitative Economics/Mathematical Methods Mathematical and Computational Engineering Mathematical and Computational Physics Mathematics Mathematics and Statistics Probability density functions Probability theory Theoretical |
| title | Identification of Stochastically Perturbed Autonomous Systems from Temporal Sequences of Probability Density Functions |
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