We consider causal time-invariant nonlinear inputoutput maps that take a set of bounded functions into a set of real-valued functions, and we give criteria under which these maps can be uniformly approximated arbitrarily well using a certain structure consisting of a notnecessarily linear dynamic part followed by a nonlinear memoryless section that may contain sigmoids or radial basis functions...

With this paper we want to present a black-box model, that can be applied to a vast number of RF electron devices (e.g. FET). We will show that an analytical Volterra series approximation of the nonlinear behavior time-dependent model of an electron device can be built using a neural network and its parameters, once the proper training data are given. Key-Words: black-box model, nonlinearity, V...

the volterra model is widely used for nonlinearity identification in practical applications. in this paper, we employed volterra model to find the nonlinearity relation between electroencephalogram (eeg) signal and the noise that is a novel approach to estimate noise in eeg signal. we show that by employing this method. we can considerably improve the signal to noise ratio by the ratio of at le...

The main purpose of this article is to demonstrate the use of the two Dimensional Walsh and Haar functions with Operational Matrix for solving nonlinear Volterra-Fredholm integral equations. The approximate solution is represented in the form of series. The approximate solution is obtained by two Dimensional Walsh and Haar series. The operational matrix and direct method for solving the linear ...

A great many of processes in a nature are nonlinear, so their modeling requires an embedding of nonlinear parts into the model structure. One of the popular approaches to the nonlinear system modeling are Volterra series. Unfortunately, already the second order Volterra kernel requires high amount of coefficients for its identification and therefore a large number of computations for its realiz...

A polynomial approximation to the likelihood function allows for marginalised estimates of model parameters to be obtained in the form of a Volterra series. The series can be applied directly to the observed data vector in an iterative fashion, to converge upon a set of parameter MAP estimates with low computational cost. A sample application towards OCR is used as an illustration.

In this paper, we focus on model reduction of large-scale bilinear systems. The main contributions are threefold. First, we introduce a new framework for interpolatory model reduction of bilinear systems. In contrast to the existing methods where interpolation is forced on some of the leading subsystem transfer functions, the new framework shows how to enforce multipoint interpolation of the un...

A broad class of nonlinear systems can be modeled by the Volterra series representation. However, the practical use of such a representation is often limited due to the large number of parameters associated with the Volterra filter structure. This paper is concerned with the problem of identification of third-order Volterra systems. The SVD technique is used to represent the quadratic Volterra ...