Multirate architectures have been used for realizing linear FIR digital filters with reduced computational complexity. The Volterra kernel can be represented as a generalized convolution. It would thus be expected that multirate architectures could be used to advantage in realizing Volterra kernels as well. The quadratic Volterra kernel may be realized in the form of an “LDL structure.” The LDL...

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...

In this paper, a Bernoulli pseudo-spectral method for solving nonlinear fractional Volterra integro-differential equations is considered. First existence of a unique solution for the problem under study is proved. Then the Caputo fractional derivative and Riemman-Liouville fractional integral properties are employed to derive the new approximate formula for unknown function of the problem....

In this paper, two methods of distortion analysis are described. The Volterra series is usually used to determine nonlinear behaviour. The direct method is an extension of the Volterra series method to circuits with multiple inputs. The Volterra series method of distortion analysis is presented in the analysis of a common emitter circuit. Next the direct method is used to analyze a mixer circui...

This work builds on the Volterra series formalism presented in [D. W. Dreisigmeyer and P. M. Young, J. Phys. A 36, 8297, (2003)] to model nonconservative systems. Here we treat Lagrangians and actions as ‘time dependent’ Volterra series. We present a new family of kernels to be used in these Volterra series that allow us to derive a single retarded equation of motion using a variational princip...

This article presents a survey of various methods for nonparametric identification of nonlinear systems. Nonparametric identification methods are those that measure Wiener kernels or Volterra kernels, since an output of a nonlinear system can be described by the convolution integral of Wiener or Volterra kernels and the system input. Section 1 highlights the representation methods of nonlinear ...

In this paper the hybrid block-pulse function and Bernstein polynomials are introduced to approximate the solution of linear Volterra integral equations. Both second and first kind integral equations, with regular, as well as weakly singular kernels, have been considered. Numerical examples are given to demonstrate the applicability of the proposed method. The obtained results show that the hyb...

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.