Estimating properties of chaotic system based on S-NURBS resampling method

C. Shao, Qingqing Liu, Tingting Wang, Binghong Wang
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引用次数: 0

Abstract

Physical properties of the chaotic system play important roles on studying the innate character and deciding the practical predictability of the dynamics. For a short piece of undersampled chaotic signals, it is very hard to abstract the physical properties of the signal source from the sequence. In this paper, we model this type of data with global S-NURBS method to reconstruct a smooth trajectory and resample the trajectory to get enough series. In this way, the problem of estimating the physical properties from small undersampled data is turned to the work of calculating the properties of the resampled time series. The new interpolation method is named as S-NURBS resampling method, and the simulation experiment demonstrates that the new method has a good performance in studying physical systems from the observed time series.
基于S-NURBS重采样方法的混沌系统性质估计
混沌系统的物理性质对研究混沌系统的固有特性和决定混沌系统动力学的实际可预测性具有重要意义。对于短段欠采样混沌信号,很难从序列中抽象出信号源的物理性质。本文采用全局S-NURBS方法对这类数据进行建模,重建光滑轨迹,并对轨迹进行重新采样以获得足够的序列。这样,从小的欠采样数据中估计物理性质的问题就变成了计算重采样时间序列的性质的工作。新的插值方法被命名为S-NURBS重采样方法,仿真实验表明,新方法在从观测时间序列研究物理系统方面具有良好的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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