Nonlinear block-oriented systems, including the Hammerstein, Wiener and feedbacknonlinear systems have attracted considerable research interest both from the industrial and academic environments (Bai, 1998), (Greblicki, 1989), (Latawiec, 2004), (Latawiec et al., 2003), (Latawiec et al., 2004), (Pearson & Pottman, 2000). It is well known that orthonormal basis functions (OBF) (Bokor et al., 1999...

In this paper, non iterative algorithms for the identification of (multivariable) nonlinear systems consisting of the interconnection of LTI systems and static nonlinearities are presented. The proposed algorithms are numerically robust, since they are based only on least squares estimation and singular value decomposition. Three different block-oriented nonlinear models are considered in this ...

Nonlinearities provide a neverending challenge to systems and control. We savor linear systems since we have a good understanding of how they behave, and there is much we can do to analyze and predict their performance. As we move away from linearity, we need to scrutinize each nonlinearity that we encounter to assess its benefi t or detriment in terms of what we wish to achieve. The first issu...

Over the last two decades, there has been a growing interest in the use of orthonormal basis filters (OBF) for developing dynamic black box models. When compared with the conventional approach, a substantial dimensionality reduction can be achieved through OBF parameterization. Moreover, the orthonormal filters, because of their similarity to the Padé approximation, can model systems that exhib...

The existing results show the applicability of Over-Parameterized Model based Hammerstein-Wiener model identification methods. However, it requires to estimate extra parameters and performer a low rank approximation step. Therefore, may give rise unnecessarily high variance in parameter estimates for highly nonlinear systems, especially using small noisy data set. To overcome this corruptive ph...

Newly developed hypertext transfer protocol (HTTP)-based video streaming technologies enable flexible rate-adaptation under varying channel conditions. Accurately predicting the users' quality of experience (QoE) for rate-adaptive HTTP video streams is thus critical to achieve efficiency. An important aspect of understanding and modeling QoE is predicting the up-to-the-moment subjective quality...