The aim of the present study is to derive nonlinear instrument variable methods by using local linear models. Two algorithms to estimate consistent local ARX-models of the system order are presented. A local ARX-model with a regressor of higher order than the system is simulated to estimate an approximately noise-free data set. In the first algorithm this approximately noise-free data is used as estimation data to a local ARX-model of the system order. The second algorithm uses the simulated data as instrument in a local instrument variable method. The algorithms are demonstrated on both simulated and laboratory data
The local model network is a set of models, each describing the same dynamic system but at different...
System Identification is used to build mathematical models of a dynamic system based on measured da...
The present paper addresses the problem of characterising structural nonlinearities in view of syste...
Nonlinear systems might be estimated, using local linear models. If the estimation data is corrupted...
Nonlinear systems might be estimated, using local linear models. If the estimation data is corrupted...
We consider the local order estimation of nonlinear autoregressive systems with exogenous inputs (NA...
A common approach for dealing with non-linear systems is to describe the system by a model with para...
A common approach for dealing with non-linear systems is to describe the system by a model with para...
Abstract: A common approach for dealing with non-linear systems is to describe the system by a model...
The aim of this paper is to propose a new method to select the model order in continuous time system...
In this work, we propose a recursive local linear estimator (RLLE) for identification of nonlinear a...
The local model network is a set of models, each describing the same dynamic system but at different...
In this paper we continue to explore identification of nonlinear systems using the previously propos...
A nonlinear dynamic process can be described as a composition of several local affine models selecte...
In this paper, the problem of identifying linear discrete-time systems from noisy input and output d...
The local model network is a set of models, each describing the same dynamic system but at different...
System Identification is used to build mathematical models of a dynamic system based on measured da...
The present paper addresses the problem of characterising structural nonlinearities in view of syste...
Nonlinear systems might be estimated, using local linear models. If the estimation data is corrupted...
Nonlinear systems might be estimated, using local linear models. If the estimation data is corrupted...
We consider the local order estimation of nonlinear autoregressive systems with exogenous inputs (NA...
A common approach for dealing with non-linear systems is to describe the system by a model with para...
A common approach for dealing with non-linear systems is to describe the system by a model with para...
Abstract: A common approach for dealing with non-linear systems is to describe the system by a model...
The aim of this paper is to propose a new method to select the model order in continuous time system...
In this work, we propose a recursive local linear estimator (RLLE) for identification of nonlinear a...
The local model network is a set of models, each describing the same dynamic system but at different...
In this paper we continue to explore identification of nonlinear systems using the previously propos...
A nonlinear dynamic process can be described as a composition of several local affine models selecte...
In this paper, the problem of identifying linear discrete-time systems from noisy input and output d...
The local model network is a set of models, each describing the same dynamic system but at different...
System Identification is used to build mathematical models of a dynamic system based on measured da...
The present paper addresses the problem of characterising structural nonlinearities in view of syste...