Training asymptotically stable recurrent neural networks

Presents a class of recurrent networks which are asymptotically stable. For these networks, the authors discuss their similarity with certain structures in the central nervous system, and prove that if an interconnection pattern that does not allow excitatory feedback is used, then the resulting recurrent neural network is stable. The authors introduce a training methodology for networks belonging to this class, and use it to train networks that successfully identify a number nonlinear systems