Abstract
A nonlinear extension of the discrete linear autoregressive moving average (ARMA) model is presented for the identification of nonlinear systems from measurements of input and output signals. This model is linear In the parameters and is shown to be applicable to a broad class of interconnected inear and memoryless nonlinear subsystems. Conditions for identifiability of nonlinear systems and memory requirements for the model are presented. The model contains Volterra and cross product expansions of the measured signals, which in many cases avoids the need for the large number of parameters required in a traditional Volterra expansion. It is also shown that when the model equations are recast in "regular" form, the model parameters can be obtained using multichannel lattice methods based upon the Levinson algorithm.
Published Version
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