A structure detection algorithm for nonlinear dynamic rational models

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Abstract
A general structure detection and parameter estimation algorithm is derived for the identification of nonlinear systems that can be approximated by a stochastic nonlinear rational model defined as the ratio of two polynomial expansions of past inputs, outputs and noise sequences. The algorithm includes an intelligent structure detection module that learns the structure of the model from the input/output data. Simulation results are included to illustrate the application of the new algorithm.