Global asymptotic convergence of nonlinear relaxation equations realised through a recurrent perceptron

2010-01-18T13:51:24Z (GMT) by Danilo P. Mandic Jonathon Chambers
Conditions for global asymptotic stability (GAS) of a nonlinear relaxation equation realised by a nonlinear autoregressive moving average (NARMA) recurrent perceptron are provided. Convergence is derived through fixed point iteration (FPI) techniques, based upon a contraction mapping feature of a nonlinear activation function of a neuron. Furthermore, nesting is shown to be a spatial interpretation of an FPI, which underpins a pipelined recurrent neural network (PRNN) for nonlinear signal processing