Nowadays the use of surrogate models (SMs) is becoming a common practice to accelerate the optimization phase of the design of microwave and millimeter wave devices. In order to further enhance the performances of the optimization process, the accuracy of the response provided by a SM can be improved employing a suitable output correction block, obtaining in this way a so-called enhanced surrogate model (ESM). In this paper a comparative study of three different techniques for building ESMs, i.e. Kriging, Support Vector Regression Machines (SVRMs) and Artificial Neural Networks (ANNs), applied to the modelling of substrate integrated waveguide (SIW) devices, is presented and discussed.

Appraisal of enhanced surrogate models for substrate integrate waveguide devices characterization

CALCAGNO, SALVATORE
2019

Abstract

Nowadays the use of surrogate models (SMs) is becoming a common practice to accelerate the optimization phase of the design of microwave and millimeter wave devices. In order to further enhance the performances of the optimization process, the accuracy of the response provided by a SM can be improved employing a suitable output correction block, obtaining in this way a so-called enhanced surrogate model (ESM). In this paper a comparative study of three different techniques for building ESMs, i.e. Kriging, Support Vector Regression Machines (SVRMs) and Artificial Neural Networks (ANNs), applied to the modelling of substrate integrated waveguide (SIW) devices, is presented and discussed.
978-3-319-95097-6
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/20.500.12318/11854
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