Support-vector modeling of electromechanical coupling for microwave filter tuning

Zhou, Jinzhu; Duan, Baoyan; Huang, Jin

doi:10.1002/mmce.20683

Cited by 12 publications

(9 citation statements)

References 29 publications

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“…The amount of experimental data is so little that a satisfactory model cannot be obtained. On the other hand, a simulation model built from some physical knowledge is available [27,28]. Using a calibrated simulator, one can obtain enough prior data, but which may be biased from the measured results.…”

Section: B Mkplpsvrmentioning

confidence: 99%

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Multi-kernel linear programming support vector regression with prior knowledge

Zhou

Song

2014

2014 International Joint Conference on Neural Networks (IJCNN)

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This paper proposes a multi-kernel linear programming support vector regression with prior knowledge in order to obtain an accurate regression model in the case of the scarcity of measured data available. In the algorithm, multi-kernel and prior knowledge which may be exact or biased from a calibrated simulator have been incorporated into the framework of linear programming support vector regression by utilizing multiple feature spaces and modifying optimization formulation. Some experiments from a synthetic example have been carried out, and the results show that the proposed algorithm is effective, and that the obtained model is sparse and accurate. The proposed algorithm shows great potential in some practical applications where the experimental data is few and the prior knowledge from a simulator is available.

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Section: B Mkplpsvrmentioning

confidence: 99%

“…However, this will lead to an exact fit to the data points, which may not be advised if the prior data is biased from the measured results. Moreover, all the equality constraints may lead to an unfeasible problem if they cannot be satisfied simultaneously [24,[27][28][29].Therefore, some soft constraints have been utilized in Eq. (14) …”

Section: B Mkplpsvrmentioning

confidence: 99%

Multi-kernel linear programming support vector regression with prior knowledge

Zhou

Song

2014

2014 International Joint Conference on Neural Networks (IJCNN)

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“…Different from reference and references utilizes the least squares (LSs) SVR to model the relationship between length variable of tuning screws and the filter response. Reference is similar to reference, the coupling matrix elements are used as outputs of the modeling. In order to improve the modeling accuracy, a linear programming SVR modeling method for microwave filter tuning is proposed .…”

Section: Introductionmentioning

confidence: 99%

“…Reference is similar to reference, the coupling matrix elements are used as outputs of the modeling. In order to improve the modeling accuracy, a linear programming SVR modeling method for microwave filter tuning is proposed . Whereas the extraction of the coupling matrix is difficult when the detuning is high.…”

Section: Introductionmentioning

confidence: 99%

“…In order to improve the modeling accuracy, a linear programming SVR modeling method for microwave filter tuning is proposed. 15 Whereas the extraction of the coupling matrix is difficult when the detuning is high. In addition, Bayesian SVR have been applied to the modeling of plannar antennas and microwave filters, which aims to reduce training sets and establishes a high-fidelity BSVR model.…”

Section: Introductionmentioning

confidence: 99%

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Parametric modeling of microwave filter using combined MLS-SVR and pole-residue-based transfer functions

Cao

Liu

Yuan

et al. 2018

Int J RF Microw Comput Aided Eng

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In this article, an advanced technique is developed to combine multi‐output least‐squares support vector regression (MLS‐SVR) and pole‐residue‐based transfer function models for microwave filter parametric modeling. MLS‐SVR is trained to learn the relationship between the length of tuning screws and the pole/residues of the transfer function, where MLS‐SVR is an effective method to cope with the multi‐output case unlike the traditional approach. Traditional approach treats the different outputs separately in the multi‐output case and it cannot model the relation between different outputs. Another important element for modeling is feature parameters. Extracted feature parameters have an important influence on the accuracy of modeling. For the purpose of establishing a more accurate model, the complex system poles and residues from Y‐parameters are chosen as the outputs of modeling, which are obtained by vector fitting (VF). Then we give a solution to obtaining pole/residues extracted by VF when the filter is in high detuned. After the proposed modeling process, trained model can be used to provide an accurate and fast prediction of the behavior of microwave filter with the length of tuning screws as variables, and model the electromagnetic simulation (or actual) microwave filter tuning. The methodology is applied to a narrow band coaxial‐resonator filter modeling, and more accurate results are achieved compared with the other methods. An example is presented to illustrate the efficiency of the proposed method.

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Dual-band differential bandpass filter using stepped impedance resonators

Cui

Sun

Wang

et al. 2015

Int J RF and Microwave Comp Aid Eng

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In this article, a novel dual-band differential bandpass filter using (SIRs) is designed. To demonstrate the design ideas, the differential and common mode equivalent half circuits are built and studied. Two resistors are connected between the two ends of the SIRs to consume the power in common mode. A capacitor is connected between the Ground and Center of the SIR to adjust the spurious frequencies, also strength the coupling of the two SIRs. The theoretical analysis shows the second band can be obtained by the proper impedance ratios of the resonances and the capacitor connected to the resonator. Two through ground vias (TGVs) connecting the top and bottom sides of the SIR filter, are used to realize the common mode rejection. To investigate the proposed filter in detail, a set of design equations are derived based on the circuit theory and transmission line theory. A phototype dual-band differential filter operating at 1.5 and 2.75 GHz has been realized to validate the proposed concept and theory. V C 2015 Wiley Periodicals, Inc. Int J RF and Microwave CAE 25:468-473, 2015.

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Support-vector modeling of electromechanical coupling for microwave filter tuning

Cited by 12 publications

References 29 publications

Multi-kernel linear programming support vector regression with prior knowledge

Multi-kernel linear programming support vector regression with prior knowledge

Parametric modeling of microwave filter using combined MLS-SVR and pole-residue-based transfer functions

Dual-band differential bandpass filter using stepped impedance resonators

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