Global asymptotic convergence of nonlinear relaxation equations realised through a recurrent perceptron
نویسندگان
چکیده
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 recently proposed Pipelined Recurrent Neural Network (PRNN) for nonlinear signal processing.
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