Using Neural Networks to Estimate Regions of Stability

This paper presents a new method to estimate the region of stability of an asymptotically stable equilibrium point of an autonomous nonlinear system using a neural network. In contrast to model-based analytical methods, this approach uses empirical data from the system to train the neural network. The neural network results are compared with estimates obtained by previously proposed methods for some sample two dimensional problems and for an inverted pendulum.