The Identification of a Class of Nonlinear Systems using a Correlation Analysis Approach

In this paper, a new correlation analysis based algorithm is proposed for the identification of a class of nonlinear systems which can be described by the NARX (Nonlinear AutoRegressive with eXogeneous input) model with input nonlinearities. Without any assumptions about the structure of an approximating function for the system nonlineariy, the algorithm recovers the functional values of the nonlinearity over a discrete point set associated with the levels of the applied input and estimates the model parameters from the system input output data. An optimal approximating polynomial can then be determined from the nonlinear functional values to produce an optimal estimate for the system nonlinearity. Simulation studies are included to demonstrate the effectiveness of the new method.