The paper presents a new method for the identification of frequency-domain Volterra kernels. Based on the assumption that frequency-domain kernels are locally smooth, the kernel surface can be approximated by interpolation techniques, thus reducing the complexity of the model. Similarly to the unreduced (Volterra) model, this smaller model is also (i) linear in the unknowns, (ii) only locally s...

this paper investigates the dynamics and stability properties of a discrete-time lotka-volterra type system. we first analyze stability of the fixed points and the existence of local bifurcations. our analysis shows the presence of rich variety of local bifurcations, namely, stable fixed points; in which population numbers remain constant, periodic cycles; in which population numbers oscillate amo...

Discrete-time Volterra models play an important role in many application areas. The main drawback of these models is their parametric complexity due to the huge number of their parameters, the kernel coefficients. Using the symmetry property of the Volterra kernels, these ones can be viewed as symmetric tensors. In this paper, we apply tensor decompositions (PARAFAC and HOSVD) for reducing the ...

In the present paper we introduce a notion of homotopy of two Volterra operators which is related to fixed points of such operators. It is establish a criterion when two Volterra operators are homotopic, as a consequence we obtain that the corresponding tournaments of that operators are the same. This, due to [4], gives us a possibility to know some information about the trajectory of homotopic...

A 30 W LDMOS is modeled using a 5th order polynomial model. The polynomial model is compared to the largesignal MET model using harmonic balance, and as the results agreed very well, the polynomial model was imported to a numerical Volterra simulator to find out the dominant cause of distortion for a class A biased amplifier. The characterization technique is briefly discussed.

This paper presents a general methodological framework for the practical modeling of neural systems with point-process inputs (sequences of action potentials or, more broadly, identical events) based on the Volterra and Wiener theories of functional expansions and system identification. The paper clarifies the distinctions between Volterra and Wiener kernels obtained from Poisson point-process ...

We propose a new method for discretizing the time variable in integrable lattice systems while maintaining the locality of the equations of motion. The method is based on the zero-curvature (Lax pair) representation and the lowest-order “conservation laws”. In contrast to the pioneering work of Ablowitz and Ladik, our method allows the auxiliary dependent variables appearing in the stage of tim...

We prove the equivalence between the recent matrix model formulation of 2Dgravity and lattice integrable models. For even potentials this system is theVolterra hierarchy.

We give a construction of a cubic stochastic operator (CSO) on a finite dimensional simplex. This construction depends on a probability measure μ which is given on a fixed finite graph G. Using the construction of CSO for μ defined as product of measures given on components of G a wide class of non-Volterra CSOs is described. It is shown that the non-Volterra operators can be reduced to N numbe...

In this paper we first prove the equivalence between the system of coupled Volterra gyrostats and a special class of energy-conserving low-order models. We then extend the definition of the classical Volterra gyrostat to include nonlinear feedback, resulting in a class of generalized Volterra gyrostats. Using this new class of gyrostats as a basic building block, we present an algorithm for con...