We demonstrate the capabilities of nonlinear Volterra models to simulate the behavior of an audio system and compare them to linear filters. In this paper a nonlinear model of an audio system based on Volterra series is presented and Normalized Least Mean Square algorithm is used to determine the Volterra series to third order. Training data for the models were collected measuring a physical sp...

In this work we want to present a novel application of Neural Networks as a Black-Box model, which allows representing the nonlinear behavior of a vast number of RF electronic devices with the Volterra series approximation. We propose a simple approach for the generation of the Volterra model for a device, even in the case of a nonlinearity that depends on more than one variable, which allows o...

The baseband Volterra–Parafac model is a useful tool to represent a nonlinear communication channel with a parametric complexity reduced with respect to the full Volterra model. In this paper we include additional symmetry properties of real power amplifier kernels in the equivalent baseband Volterra-Parafac approach in order to gain a further reduction in the number of parameters. To illustrat...

Nonlinear intersymbol interferences (ISI) often arise in voiceThe major drawback of the Volterra approach is the enormous band communication channels at high transmission rates or in complexity. Even when the symmetry in the 3rd order Volterra satellite channels due to nonlinearities in the power amplifiers. Proposed equalizers for the cancellation of these nonlinear interferences are mainly ba...

This dissertation is primarily concerned with the estimation of nonlinear communication systems that are modeled by Volterra series. The major methods used for estimating the unknown channel parameters can be classified into two main categories: training-based and blind. First, orthobasis representation and training-based identification through the respective Fourier series are investigated for...

A broad class of nonlinear systems and filters can be modeled by the Volterra series representation. However, its practical use in nonlinear system identification is sometimes limited due to the large number of parameters associated with the Volterra filter’s structure. The parametric complexity also complicates design procedures based upon such a model. This limitation for system identificatio...

The accurate traffic flow time series prediction is the prerequisite for achieving traffic flow inducible system. Aiming at the issue about multi-step prediction traffic flow chaotic time series, the traffic flow Volterra Neural Network (VNN) rapid learning algorithm is proposed. Combing with the chaos theory and the Volterra functional analysis, method of the truncation order and the truncatio...

In this paper, we present and demonstrate an innovative numerical method, which makes use of fuzzy numbers parameters that is effective in the solution type Volterra integro-differential equations, was previously thought to be impossible using conventional methods. The first application a technique for solving equations type, devised tested shown here. This time approach has been used. system’s...

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The semidiscretization in space of Volterra-Fredholm integral equations (arising, for example, as mathematical models of the spreading of epidemics) leads to large systems of Volterra integral equations. Here, we study inexpensive timestepping methods using certain DQ (= direct quadrature) methods which are employed in a way that exploits the local superconvergence properties of spatial colloca...