Lyapunov exponents as a nonparametric diagnostic for stability analysis
提出一种非参数算法，用多层前馈网络从有限观测数据中准确估计未知n维动力系统的所有李雅普诺夫指数，适用于经济和金融时间序列的稳定性分析。
The common observation made in the empirical nonlinear dynamics literature is the constraints imposed by the availability of a limited number of observations in the implementation of the existing algorithms of Lyapunov exponents. The algorithm discussed here can estimate all n Lyapunov exponents of an unknown n-dimensional dynamical system accurately with limited number of observations. This makes the algorithm attractive for applications to economic as well as financial time-series data. The implementation of the algorithm is carried out by multilayer feedforward networks which are capable of approximating any function and its derivatives to any degree of accuracy.