IDENTIFICATION OF LINEARLY OVERPARAMETRIZED NONLINEAR-SYSTEMS

Often, a dynamical model is nonlinear in the unknown parameters, but it can be transformed into an overparametrized linear regression model, where the components of the overparametrization vector are nonlinear functions of the smaller number of unknown parameters. We present an algorithm that directly identifies the unknown parameters, we characterize the convergence domains under two different sets of assumptions on the excitation of the signals, and we compute the corresponding convergence rates.