Inverse Modeling Approach for Ultra-Wideband Filters Based on IALO-HBP Neural Networks

Jingchang Nan; Jingjing Du; Mingming Gao; huan Xie

doi:10.3788/LOP202259.1220001

Laser & Optoelectronics Progress, Volume. 59, Issue 12, 1220001(2022)

Inverse Modeling Approach for Ultra-Wideband Filters Based on IALO-HBP Neural Networks

Jingchang Nan¹, Jingjing Du^1、*, Mingming Gao^1,2, and huan Xie¹

Author Affiliations

¹School of Electronics and Information Engineering, Liaoning Technical University, Huludao 125105, Liaoning , China

²Information Science and Technology College, Dalian Maritime University, Dalian 116026, Liaoning , China

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Figures & Tables(9)

Fig. 1. HBP neural network structure

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Fig. 2. Structure of dual notched bands UWB filter

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Fig. 3. Correspondence between $S_{21}$ with $L_{5}$ and $W_{5}$

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Fig. 4. Reverse modeling process of double notch ultra-wideband filter based on IALO-HBP

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Fig. 5. Comparison of the output values $L_{5}$ of the four inverse models with the actual values of HFSS

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Fig. 6. Comparison of the output values $W_{5}$ of the four inverse models with the actual values of HFSS

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Fig. 7. Comparison of the output values $f$ of the four inverse models with the actual values of HFSS

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Fig. 8. Comparison diagram of algorithm convergence curve

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Table 1. Performance comparison of four modeling methods
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Table 1. Performance comparison of four modeling methods
Reverse modeling method Mean square error Running time /s
L₅ /mm W₅ /mm f /GHz
IALO-HBP 0.0016 6.0507×10^-4
0.0014
0.0085
0.0071
0.0364
3.977
ALO-HBP 0.0094 0.0103 9.711
GA-HBP 0.0080 0.0062 10.038
BP 0.0625 0.1071 11.700

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Jingchang Nan, Jingjing Du, Mingming Gao, huan Xie. Inverse Modeling Approach for Ultra-Wideband Filters Based on IALO-HBP Neural Networks[J]. Laser & Optoelectronics Progress, 2022, 59(12): 1220001

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