New compact microstrip stacked slotted resonators bandpass filter with transmission zeros using high‐permittivity ceramics substrate

2014 IEEE MTT-S International Microwave Symposium (IMS2014)

Cheng

2014

A simple technique for accurate and low-cost modeling of microwave structures is presented. Our approach utilizes response surface approximation at the level of suitably selected response features rather than direct approximation of frequency responses (e.g., S-parameters) of the structure of interest. By exploiting the fact that dependence of the features on designable parameters is much less nonlinear than similar dependence for original responses, accurate quasi-global surrogates can be constructed at a fraction of computational cost necessary by conventional modeling techniques. Numerical verification involving two micros trip filters is provided. Index Terms -Microwave component modeling, computer aided design, feature-based modeling, kriging interpolation.

Section: Verifica Non Examplesmentioning

confidence: 99%

“…Filter structures used for feature-based modeling verification: (a) stacked slotted resonators filter[7], (b) fourth-order ring resonator bandpass filter[8].…”

mentioning

confidence: 99%

Low-cost feature-based modeling of microwave structures

2014 IEEE MTT-S International Microwave Symposium (IMS2014)

Cheng

2014

“…Table 1 shows the average modelling error for the GSPRP model (1) -(6), as well as Kriging interpolation, with different number of training points from 20 to 400. For all considered problems, the accuracy of the GSPRP model is better than the accuracy of the Kriging a Stacked slotted resonators filter [31] b Third-order Chebyshev bandpass filter [32] c Fourth-order ring resonator bandpass filter [33] d Bandpass filter with open stub inverter [34] surrogate for the corresponding number of training points. Fig.…”

Section: Experimental Set-upmentioning

confidence: 99%

“…We consider three examples: the stacked slotted resonators bandpass filter [31] shown in Fig. 6a (filter 1), the thirdorder Chebyshev bandpass filter [32], Fig.…”

Section: Experimental Set-upmentioning

confidence: 99%

Generalised shape-preserving response prediction for accurate modelling of microwave structures

IET Microw. Antennas Propag.

Leifsson

2012

A generalised shape-preserving response prediction (generalised SPRP or GSPRP) methodology for low-cost and accurate modelling of microwave structures is discussed. The GSPRP method predicts the response of a high-fidelity electromagnetic-simulated model (e.g. S-parameters against frequency) using sets of characteristic points and translation vectors obtained from underlying low-fidelity model responses. Unlike the original SPRP technique, which uses a limited number of training points at restricted locations, GSPSP can utilise an arbitrary number of training points, which are handled through Kriging interpolation applied to model a set of characteristic points corresponding to the entire training set. This increases the flexibility of SPRP and allows us to improve the modelling accuracy if more training points are available. At the same time, the GSPRP inherits the main advantage of SPRP: good prediction accuracy without using a large amount of data, which is in contrast to conventional approximation techniques. This is ensured as the locations of the characteristic points are easy to model. The proposed technique is demonstrated through a comprehensive numerical study as well as comparison with other modelling techniques. Application examples are also given.

“…We consider two examples: the stacked slotted resonators bandpass filter [9] shown in Fig. 4 (Filter 1), and the third-order Chebyshev bandpass filter [10], Table I shows the average modeling error for the GSPRP model (1)-(6), as well as kriging interpolation, with different number of training points from 20 to 400.…”

Section: A Experimental Setupmentioning

confidence: 99%

Accurate modeling of microwave structures using generalized shape-preserving response prediction

2012 IEEE/MTT-S International Microwave Symposium Digest

2012

A generalized shape-preserving response prediction (GSPRP) methodology for low-cost and accurate modeling of microwave structures is presented. Our approach exploits kriging interpolation to model a set of characteristic point of the response of interest (e.g., S-parameters versus frequency), which are subsequently used to restore the entire response. As the locations of characteristic points are easy to model, GSPRP ensures good accuracy without using large amounts of training data, which is in contrast to conventional approximation techniques. The proposed technique is demonstrated through a comprehensive numerical study as well as comparison with other modeling techniques. Application examples are also given.Index Terms-Computer-aided design (CAD), surrogate modeling, microwave component modeling.