arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 1601.01568math.DS
收藏

噪声数据的李雅普诺夫函数逼近

Approximation of Lyapunov Functions from Noisy Data

Peter Giesl, Boumediene Hamzi, Martin Rasmussen, Kevin N. Webster

摘要:

先前已经开发了使用径向基函数近似李雅普诺夫函数的方法。然而,这些方法假设发展方程是已知的。我们考虑的问题,近似一个给定的李雅普诺夫函数使用径向基函数的发展方程是未知的,但我们,而不是采样数据被噪声污染。我们提出了一种算法,在该算法中,我们首先近似的基本向量场,并使用这种近似,然后近似的李雅普诺夫函数。我们的方法结合了机器学习/统计学习理论的元素与现有的李雅普诺夫函数近似理论。为我们的算法提供了误差估计。

英文摘要:

Methods have previously been developed for the approximation of Lyapunov functions using radial basis functions. However these methods assume that the evolution equations are known. We consider the problem of approximating a given Lyapunov function using radial basis functions where the evolution equations are not known, but we instead have sampled data which is contaminated with noise. We propose an algorithm in which we first approximate the underlying vector field, and use this approximation to then approximate the Lyapunov function. Our approach combines elements of machine learning/statistical learning theory with the existing theory of Lyapunov function approximation. Error estimates are provided for our algorithm.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑