Low-cost feature-based modeling of microwave structures

Koziel, Slawomir; Cheng, Qingsha S.

doi:10.1109/mwsym.2014.6848276

Cited by 5 publications

(1 citation statement)

References 8 publications

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“…An initial version was introduced in [26]. In this paper, we use an operator notation [27] to simplify the description of the process of extracting features and restoring responses.…”

Section: Introductionmentioning

confidence: 99%

Feature‐based surrogates for low‐cost microwave modelling and optimisation

Koziel

Cheng²

2015

IET Microwaves, Antennas & Propagation

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A simple, reliable, and accurate surrogate modelling technique for microwave structures is presented. A hierarchical surrogate model is constructed using response surface approximations (RSAs) of suitable response features with embedded 'visible' knowledge. The authors describe and discuss a novel scanning algorithm for capturing these desired response features. They demonstrate that the dependence of the selected features on the designable parameters is much less non-linear than that of the original responses taken as functions of the design parameters. They illustrate the steps of their algorithm using novel diagrams. They simplify the description of their modelling process by exploiting an operator notation. They discuss relationships between their method and other feature-based approaches such as shape-preserving response prediction (SPRP). They provide a wideband microstrip bandstop filter, a fourth-order ring resonator bandpass filter and a microstrip bandpass filter with open stub inverter examples to demonstrate their approach. Using these examples, they compare their approach with two very different direct RSA methods (kriging and radial basis function) and an SPRP method. The models of all the filter examples are validated using design optimisation.

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“…An initial version was introduced in [26]. In this paper, we use an operator notation [27] to simplify the description of the process of extracting features and restoring responses.…”

Section: Introductionmentioning

confidence: 99%