System identification using a linear combination of cumulant slices

System identification using a linear combination of cumulant slices

In this paper we develop a new linear approach to identify the parameters of a moving average (MA) model from the statistics of the output. First, we show that, under some constraints, the impulse response of the system can be expressed as a linear combination of cumulant slices. Then, this result is used to obtain a new well-conditioned linear method to estimate the MA parameters of a non-Gaus...

FIR System Identification using a Linear Combination of Cumulants

In this paper we develop a general linear approach to identify the parameters of a moving average (MA) model from the statistics of the output. First, we show that, under some constraints, the impulse response of the system can be expressed as a linear combination of cumulant slices. Then, this result is used to obtain a new well-conditioned linear method to estimate the MA parameters of a non-...

Adaptive blind equalization using weighted cumulant slices

Impulse response recovery of linear systems through weighted cumulant slices

Identifiability of the so-called w-slice algorithm is proven for ARMA linear systems. Although proofs were developed in the past for the simpler cases of MA and AR models [2][6], they were not extendible to general exponential linear systems. The results presented in this paper demonstrate a unique feature of the w-slice method which is unbiasedness and consistency when order is overdetermined,...

Non-linear system identification using neural networks

International Journal of Control Publication details, including instructions for authors and subscription information: http://www.informaworld.com/smpp/title~content=t713393989 Non-linear system identification using neural networks S. Chen a; S. A. Billings b; P. M. Grant a a Department of Electrical Engineering, University of Edinburgh, Edinburgh, Scotland, U.K b Department of Control Engineer...