System identification
Structure identification of nonlinear dynamic systems—a survey on input/output approaches
Automatica (Journal of IFAC)
Nonlinear black-box modeling in system identification: a unified overview
Automatica (Journal of IFAC) - Special issue on trends in system identification
Identifying MIMO Wiener systems using subspace model identification methods
Signal Processing - Special issue: subspace methods, part II: system identification
An optimal two-stage identification algorithm for Hammerstein-Wiener nonlinear systems
Automatica (Journal of IFAC)
System identification (2nd ed.): theory for the user
System identification (2nd ed.): theory for the user
Extended stochastic gradient identification algorithms for Hammerstein-Wiener ARMAX systems
Computers & Mathematics with Applications
Gradient based and least-squares based iterative identification methods for OE and OEMA systems
Digital Signal Processing
Auxiliary model-based RELS and MI-ELS algorithm for Hammerstein OEMA systems
Computers & Mathematics with Applications
Gradient-based iterative parameter estimation for Box-Jenkins systems
Computers & Mathematics with Applications
Computers & Mathematics with Applications
Identification methods for Hammerstein nonlinear systems
Digital Signal Processing
Brief Estimation of an N-L-N Hammerstein-Wiener model
Automatica (Journal of IFAC)
Identification of Hammerstein nonlinear ARMAX systems
Automatica (Journal of IFAC)
Hierarchical gradient-based identification of multivariable discrete-time systems
Automatica (Journal of IFAC)
Mathematical and Computer Modelling: An International Journal
Auxiliary model based multi-innovation algorithms for multivariable nonlinear systems
Mathematical and Computer Modelling: An International Journal
Mathematical and Computer Modelling: An International Journal
Mathematical and Computer Modelling: An International Journal
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Nonlinear Multi-Input Multi-Output (MIMO) models seem quite suitable to represent most industrial systems and many control problems. Furthermore, the outputs of the real systems are usually corrupted with noises which might not satisfy the assumption of white noises. This paper presents simple and efficient identification methods for nonlinear MIMO systems in the presence of colored noises. In the proposed approaches, three classes of MIMO block-oriented structures including Hammerstein, Wiener, and Hammerstein-Wiener models are studied. Appropriate and flexible representations of these models lead to a pseudo-linear-in-the-parameter problem that contains some terms related to the colored noises which are not known a priori. In this paper, gradient based and least squares based iterative learning algorithms are invoked which can successfully estimate the matrix of unknown parameters as well as the colored noises. The efficiency of the proposed identification schemes is investigated through two simulated and a real process as case studies. As the results show, these approaches are quite efficient for identification of nonlinear colored MIMO systems.