System identification: theory for the user
System identification: theory for the user
Recursive prediction error identification using the nonlinear Wiener model
Automatica (Journal of IFAC) - Special section on fault detection, supervision and safety for technical processes
Identification of Nonlinear Systems Using Neural Networks and Polynomial Models: A Block-Oriented Approach (Lecture Notes in Control and Information Sciences)
Parameter bounds evaluation of Wiener models with noninvertible polynomial nonlinearities
Automatica (Journal of IFAC)
On Nonparametric Identification of Wiener Systems
IEEE Transactions on Signal Processing
Stochastic gradient identification of polynomial Wiener systems: analysis and application
IEEE Transactions on Signal Processing
Analysis of stochastic gradient tracking of time-varying polynomialWiener systems
IEEE Transactions on Signal Processing
Identification of certain time-varying nonlinear Wiener andHammerstein systems
IEEE Transactions on Signal Processing
Frequency domain identification of Wiener models
Automatica (Journal of IFAC)
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The paper deals with the recursive identification of dynamic systems having noninvertible output characteristics, which can be represented by the Wiener model. A special form of the model is considered where the linear dynamic block is given by its transfer function and the nonlinear static block is characterized by such a description of the piecewise-linear characteristic, which is appropriate for noninvertible nonlinearities. The proposed algorithm is a direct application of the known recursive least squares method extended with the estimation of internal variables and enables the on-line estimation of both the linear block parameters and the parameters of some noninvertible nonlinearities and their changes. The feasibility of the proposed method is illustrated on examples of time-varying systems.