Add to ORKGIn this paper we introduce a new identification algorithm for MIMO bilinear systems driven by white noise inputs. The new algorithm is based on a convergent sequence of linear deterministic-stochastic state space approximations, thus considered a Picard based method. The key to the algorithm is the fact that the bilinear terms behave like white noise processes. Using a linear Kalman filter, the bilinear terms can be estimated and combined with the system inputs at each iteration, leading to a linear system which can be identified with a linear-deterministic subspace algorithm such as MOESP, N4SID, or CVA. Furthermore, the model parameters obtained with the new algorithm converge to those of a bilinear model. Finally, the dimensions of the d...
In this paper we introduce a bilinear repetitive process and present an iterative subspace algorithm...
input-output data can under the presence of process- and measurement noise be solved in a non-iterat...
Bilinear systems can be viewed as a bridge between linear and nonlinear systems, providing a promisi...
In this technical brief, a new subspace state space system identification algorithm for multi input ...
In this paper we introduce a recursive subspace system identification algorithm for MIMO linear para...
In this article, we introduce an iterative subspace system identification algorithm for MIMO linear ...
In this paper we introduce an identification algorithm for MIMO bilinear systems subject to determin...
We discuss the identification of multiple input, multiple output, discrete-time bilinear state space...
In this paper, we generalize a class of existing linear subspace identification techniques to subspa...
In this paper, a subspace identification method for bilinear systems is used . Wherein a " three-blo...
Several subspace algorithms for the identification of bilinear systems have been proposed recently. ...
This paper considers the combined parameter and state estimation problem of a bilinear state space s...
In this paper we derive a set of approximate but general bilinear Kalman filter equations for a mult...
A new subspace algorithm consistently identifies stochastic state space models directly from given o...
Abstract-We propose a mechanism which can improve the numerical robustness of a subspace based syste...
In this paper we introduce a bilinear repetitive process and present an iterative subspace algorithm...
input-output data can under the presence of process- and measurement noise be solved in a non-iterat...
Bilinear systems can be viewed as a bridge between linear and nonlinear systems, providing a promisi...
In this technical brief, a new subspace state space system identification algorithm for multi input ...
In this paper we introduce a recursive subspace system identification algorithm for MIMO linear para...
In this article, we introduce an iterative subspace system identification algorithm for MIMO linear ...
In this paper we introduce an identification algorithm for MIMO bilinear systems subject to determin...
We discuss the identification of multiple input, multiple output, discrete-time bilinear state space...
In this paper, we generalize a class of existing linear subspace identification techniques to subspa...
In this paper, a subspace identification method for bilinear systems is used . Wherein a " three-blo...
Several subspace algorithms for the identification of bilinear systems have been proposed recently. ...
This paper considers the combined parameter and state estimation problem of a bilinear state space s...
In this paper we derive a set of approximate but general bilinear Kalman filter equations for a mult...
A new subspace algorithm consistently identifies stochastic state space models directly from given o...
Abstract-We propose a mechanism which can improve the numerical robustness of a subspace based syste...
In this paper we introduce a bilinear repetitive process and present an iterative subspace algorithm...
input-output data can under the presence of process- and measurement noise be solved in a non-iterat...
Bilinear systems can be viewed as a bridge between linear and nonlinear systems, providing a promisi...