Neuro-transfer functions (neuro-TF) modeling method has been developed as one of the popular methods for parametric modeling of electromagnetic (EM) filter responses. The discontinuity issue of zero and pole data caused by extraction using vector fitting w.r.t. geometrical parameters change affects the neuro-TF training process and limits its modeling accuracy. This issue is addressed by this paper which proposes a novel systematic pole-zero sorting method for neuro-TF parametric modeling. The proposed method can obtain continuous pole-zero data which change much more smooth w.r.t. geometrical parameters change than the existing neuro-TF method, especially solves the difficulty of disorder of positive and negative values due to small values. The proposed systematic sorting method can substantially improve the modeling accuracy during the establishment and training of neuro-TF model over the existing neuro-TF method without systematic sorting.
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| http://dx.doi.org/10.1364/OE.514525 | DOI Listing |
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