The aim of the given paper is the development of an approach for parametric identification of Wiener systems with static non-invertible function, i.e., when the linear part with unknown parameters is followed by piecewise linear nonlinearity with negative slopes. It is shown here that the problem of identification of a nonlinear Wiener system could be reduced to a linear parametric estimation problem by a simple input-output data reordering and by a following data partition into three data sets. A technique based on ordinary least squares (LS) is proposed here for the separate estimation of parameters of linear and nonlinear parts of the Wiener system, including the unknown threshold of piecewise nonlinearity, by processing respective particles of input-output observations. The simulation results are given.
International Frontier Science Letters (Volume 6)
R. Pupeikis "Parametric Identification of Linear Systems Followed by Non-Invertible Piecewise Nonlinearities", International Frontier Science Letters, Vol. 6, pp. 16-27, 2015