Add to ORKGThe identification task consists of making a model of a system from measured input and output signals. Wiener models consist of a linear dynamic system, followed by a static nonlinearity. We derive an algorithm to calculate the maximum likelihood estimate of the model for this class of systems. We describe an implementation in some detail and show simulation results where a test system is successfully identified from data
The identification of nonlinear systems by the minimization of a predictionerror criterion suffers f...
© 2016 IEEE. Wiener systems represent a linear time invariant (LTI) system followed by a static nonl...
We present a technique for kernel-based identification of Wiener systems. We model the impulse respo...
The identification task consists of making a model of a system from measured input and output signal...
The Wiener model is a block oriented model, having a linear dynamic system followed by a static nonl...
Wiener-type systems are widely used model structures, consisting of a series connection of a dynamic...
Within the class of nonlinear system models, the so-called block-oriented models have gained wide re...
The Wiener model is a block oriented model, having a linear dynamic system followed by a static nonl...
A Wiener model consists of a linear dynamic system followed by a static nonlinearity. The input and ...
We propose a new methodology for identifying Wiener systems using the data acquired from two separat...
Wiener model is a nonlinear representation of systems composed by the coupling of a linear system L ...
\u3cp\u3eThis paper proposes a parametric identification method for parallel Wiener systems, startin...
reconstruction. The nonlinear system identification based on the Volterra model is applicable only f...
The ability to construct accurate mathematical models of real systems is an important part of contro...
A Wiener system is composed of a linear dynamic subsystem followed by a static nonlinearity. It is w...
The identification of nonlinear systems by the minimization of a predictionerror criterion suffers f...
© 2016 IEEE. Wiener systems represent a linear time invariant (LTI) system followed by a static nonl...
We present a technique for kernel-based identification of Wiener systems. We model the impulse respo...
The identification task consists of making a model of a system from measured input and output signal...
The Wiener model is a block oriented model, having a linear dynamic system followed by a static nonl...
Wiener-type systems are widely used model structures, consisting of a series connection of a dynamic...
Within the class of nonlinear system models, the so-called block-oriented models have gained wide re...
The Wiener model is a block oriented model, having a linear dynamic system followed by a static nonl...
A Wiener model consists of a linear dynamic system followed by a static nonlinearity. The input and ...
We propose a new methodology for identifying Wiener systems using the data acquired from two separat...
Wiener model is a nonlinear representation of systems composed by the coupling of a linear system L ...
\u3cp\u3eThis paper proposes a parametric identification method for parallel Wiener systems, startin...
reconstruction. The nonlinear system identification based on the Volterra model is applicable only f...
The ability to construct accurate mathematical models of real systems is an important part of contro...
A Wiener system is composed of a linear dynamic subsystem followed by a static nonlinearity. It is w...
The identification of nonlinear systems by the minimization of a predictionerror criterion suffers f...
© 2016 IEEE. Wiener systems represent a linear time invariant (LTI) system followed by a static nonl...
We present a technique for kernel-based identification of Wiener systems. We model the impulse respo...