The identification of nonlinear biological systems: Wiener and Hammerstein cascade models
Biological Cybernetics
Recursive prediction error identification using the nonlinear Wiener model
Automatica (Journal of IFAC) - Special section on fault detection, supervision and safety for technical processes
System identification (2nd ed.): theory for the user
System identification (2nd ed.): theory for the user
An Optimization Based Robust Identification Algorithm in the Presence of Outliers
Journal of Global Optimization
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The aim of the given paper is the development of an approach for parametric identification of Wiener systems with piecewise linear nonlinearities, i.e., when the linear part with unknown parameters is followed by a saturation-like function with unknown slopes. It is shown here that by a simple data reordering and by a following data partition the problem of identification of a nonlinear Wiener system could be reduced to a linear parametric estimation problem. Afterwards, estimates of the unknown parameters of linear regression models are calculated by processing respective particles of input-output data. A technique based on ordinary least squares (LS) is proposed here for the estimation of parameters of linear and nonlinear parts of the Wiener system, including the unknown threshold of piecewise nonlinearity, too. The results of numerical simulation and identification obtained by processing observations of input-output signals of a discrete-time Wiener system with a piecewise nonlinearity by computer are given.