Smelyanskiy V N, Luchinsky D G, Timuçin D A, Bandrivskyy A
NASA Ames Research Center, Mail Stop 269-2, Moffett Field, California 94035, USA.
Phys Rev E Stat Nonlin Soft Matter Phys. 2005 Aug;72(2 Pt 2):026202. doi: 10.1103/PhysRevE.72.026202. Epub 2005 Aug 2.
An algorithm is presented for reconstructing stochastic nonlinear dynamical models from noisy time-series data. The approach is analytical; consequently, the resulting algorithm does not require an extensive global search for the model parameters, provides optimal compensation for the effects of dynamical noise, and is robust for a broad range of dynamical models. The strengths of the algorithm are illustrated by inferring the parameters of the stochastic Lorenz system and comparing the results with those of earlier research. The efficiency and accuracy of the algorithm are further demonstrated by inferring a model for a system of five globally and locally coupled noisy oscillators.
本文提出了一种从含噪时间序列数据重建随机非线性动力学模型的算法。该方法是解析性的；因此，所得算法不需要对模型参数进行广泛的全局搜索，能为动力学噪声的影响提供最优补偿，并且对广泛的动力学模型具有鲁棒性。通过推断随机洛伦兹系统的参数并将结果与早期研究结果进行比较，说明了该算法的优势。通过推断一个由五个全局和局部耦合的含噪振荡器组成的系统的模型，进一步证明了该算法的效率和准确性。
