Parameter Identification of Hammerstein Systems with Asymmetric Dead–zones
نویسنده
چکیده
The paper deals with the parameter identification of Hammerstein systems having piecewise-linear nonlinearities with asymmetric dead-zones. A new form of nonlinearity representation provides a special form of Hammerstein model. Parameter estimation is carried out iteratively with measured input and output data records and estimated internal variables. To demonstrate the feasibility of the identification method, illustrative examples are included.
منابع مشابه
Modeling and Identification of Hammerstein System by using Triangular Basis Functions
This paper deals with modeling and parameter identification of nonlinear systems described by Hammerstein model having Piecewise nonlinear characteristics such as Dead-zone nonlinearity characteristic. The simultaneous use of both an easy decomposition technique and the triangular basis functions leads to a particular form of Hammerstein model. The approximation by using Triangular basis functi...
متن کاملIdentification of Hammerstein Systems using Triangular basis Functions
A new identification method is proposed for Hammerstein systems in presence of dead zone input nonlinearities. To describe and identify the nonlinear system, a new decomposition technique using the triangular basis functions is employed. Then a parameterized model is derived to represent the entire system. The approximation by Triangular basis functions for the description of the static nonline...
متن کاملModelling and Estimation of Hammerstein System with Preload Nonlinearity
This paper deals with modelling and parameter identification of nonlinear systems described by Hammerstein model having asymmetric static nonlinearities known as preload nonlinearity characteristic. The simultaneous use of both an easy decomposition technique and the generalized orthonormal bases leads to a particular form of Hammerstein model containing a minimal parameters number. The employ ...
متن کاملNonlinear System Identification Using Hammerstein-Wiener Neural Network and subspace algorithms
Neural networks are applicable in identification systems from input-output data. In this report, we analyze theHammerstein-Wiener models and identify them. TheHammerstein-Wiener systems are the simplest type of block orientednonlinear systems where the linear dynamic block issandwiched in between two static nonlinear blocks, whichappear in many engineering applications; the aim of nonlinearsyst...
متن کاملAn Iterative Method for Hammerstein–wiener Systems Parameter Identification
The paper deals with parameter identification of nonlinear dynamic systems using Hammerstein-Wiener models. Multiple application of a decomposition technique provides special expressions for the model description that are linear in parameters. This allows iterative estimation of all model parameters based on the measured input/output data and estimates of internal variables. The proposed algori...
متن کامل