1991
DOI: 10.1002/mmce.4570010303
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The relationship between bivariate volterra analysis and power series analysis with application to the behavioral modeling of microwave circuits

Abstract: Volterra nonlinear transfer functions can be used in the behavioral modeling of many nonlinear microwave circuits. They can be developed experimentally, numerically, and, to a limited extent, analytically. This article presents an enhanced analytic method for developing bivariate Volterra nonlinear transfer functions based on their relationship to power series. The technique is applied to the Volterra-series-based behavioral modeling of a MESFET amplifier using experimental characterization of the MESFET.

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Cited by 4 publications
(1 citation statement)
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“…The most general, rigorous modeling approach for systems characterized by nonlinear dynamic phenomena is the well-known Volterra series [34]- [38]. This approach is based on the description of the nonlinear dynamic effects by means of multi-dimensional convolution integrals which can be considered to be a generalization of the onedimensional convolution integral for dynamic linear systems; the nonlinear effects, in particular, are described by means of a multi-dimensional integral polynomial representation which is analogous to the Taylor series expansion for purely algebraic nonlinear systems.…”
Section: The Nonlinear Integral Modelmentioning
confidence: 99%
“…The most general, rigorous modeling approach for systems characterized by nonlinear dynamic phenomena is the well-known Volterra series [34]- [38]. This approach is based on the description of the nonlinear dynamic effects by means of multi-dimensional convolution integrals which can be considered to be a generalization of the onedimensional convolution integral for dynamic linear systems; the nonlinear effects, in particular, are described by means of a multi-dimensional integral polynomial representation which is analogous to the Taylor series expansion for purely algebraic nonlinear systems.…”
Section: The Nonlinear Integral Modelmentioning
confidence: 99%