This paper adresses the variance quantification problem for system identification based on the prediction error framework. The role of input and model class selection for the auto-...
In this paper we propose a new nonparametric approach to identification of linear time invariant systems using subspace methods. The nonparametric paradigm to prediction of station...
Alessandro Chiuso, Gianluigi Pillonetto, Giuseppe ...
We describe the use of the discrete wavelet transform (DWT) for non-parametric linear time-invariant system identification. Identification is achieved by using a test excitation t...
Identification of time-varying linear systems, which introduce both time-shifts (delays) and frequency-shifts (Doppler-shifts), is a central task in many engineering applications...
The Wiener model is a versatile nonlinear block oriented model structure for miscellaneous applications. In this paper a method for identifying the parameters of such a model usin...