A survey of nonlinear system identification algorithms and related topics is presented by
extracting significant results from the literature and presenting these in an organised and …

We propose a robust learning algorithm and apply it to recurrent neural networks. This
algorithm is based on filtering outliers from the data and then estimating parameters from the …

“One can FT anything—often meaningfully.”—John W. Tukey John Tukey made this remark
after my book had been published, but it is surely the motif of the work of the book. In fact the …

It is the purpose of this tutorial paper to place bispectrum estimation in a digital signal
processing framework in order to aid engineers in grasping the utility of the available …

Emotion recognition through computational modeling and analysis of physiological signals
has been widely investigated in the last decade. Most of the proposed emotion recognition …

Adaptive nonlinear filters equipped with polynomial models of nonlinearity are explained.
The polynomial systems considered are those nonlinear systems whose output signals can …

The most general form of a nonlinear strictly stationary process is that referred to as a
Volterra expansion; this is to a linear process what a polynomial is to a linear function …

A review of blind channel estimation algorithms is presented. From the (second-order)
moment-based methods to the maximum likelihood approaches, under both statistical and …

Prognostics is an emerging science of predicting the health condition of a system (or its
components) based upon current and previous system states. A reliable predictor is very …

Some recent results on the design and implementation of second-order Volterra filters are
presented. The (second-order) Volterra filter is a nonlinear filter with the filter structure of …