کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
5011375 | 1462591 | 2018 | 19 صفحه PDF | دانلود رایگان |
- The paper introduces new method of reconstructing an unknown one-dimensional transformation that is subject to constantly applied stochastic perturbations based on temporal sequences of probability density functions.
- The main assumption is that the one-dimensional transformation that generated the densities is piecewise-linear, semi-Markov.
- A matrix approximation of the transfer operator associated with the stochastically perturbed transformation, which forms the basis for the reconstruction algorithm, is introduced.
- A practical algorithm to estimate the matrix-representation of the Frobenius-Perron operator associated with the unperturbed transformation and reconstruct the onedimensional map is proposed.
- The algorithm is extended to nonlinear continuous maps.
- Numerical simulation examples are provided to demonstrate the performance of the approach and to compare it with that of an existing algorithm.
The paper introduces a matrix-based approach to estimate the unique one-dimensional discrete-time dynamical system that generated a given sequence of probability density functions whilst subjected to an additive stochastic perturbation with known density.
Journal: Communications in Nonlinear Science and Numerical Simulation - Volume 54, January 2018, Pages 248-266