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...

Volterra integral equations arise in many problems pertaining to mathematical physics like heat conduction problems. Several numerical methods for approximating the solution of Volterra integral equations are known [1-10]. This paper is focused on the solution of Volterra integral equations of the second kind with weakly singular kernel via Haar function by taking advantage of the nice properti...

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 ...

Differential equations involving causal operators have gained much attention of late and some results are assembled in a recent monograph [1]. The term causal is adopted from the engineering literature. Basically, a causal operator is a nonanticipative operator. The theory of these operators has the powerful quality of unifying ordinary differential equations, integrodifferential equations, dif...

This paper provides an overview of system identification using orthonormal basis function models, such as those based on Laguerre, Kautz, and generalized orthonormal basis functions. The paper is separated in two parts. The first part of the paper approached issues related with linear models and models with uncertain parameters. Now, the mathematical foundations as well as their advantages and ...

One of the main problems in chaotic time series prediction is that the underlying nonlinear dynamics is usually unknown. Using a nonlinear predictor to predict a chaotic time series usually puts a limit on the accuracy since the nonlinear predictor is basically an approximation of the unknown nonlinear mapping. In this paper, we propose using fusion of predictors as a method to improve the perf...

Ahmed A. Hamoud, Nedal M. Mohammed, Homan Emadifar;, Foroud Parvaneh, Faraidum K. Hamasalh, Soubhagya Kumar Sahoo, and Masoumeh Khademi. International Journal of Fuzzy Logic Intelligent Systems 2022;22:391-400. https://doi.org/10.5391/IJFIS.2022.22.4.391