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

Journals >Laser & Optoelectronics Progress >Volume 59 >Issue 12 >Page 1220001 > Article

Laser & Optoelectronics Progress
Vol. 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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DOI: 10.3788/LOP202259.1220001 Cite this Article Set citation alerts

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 Copy Citation Text

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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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Reverse modeling method	Mean square error			Running time /s
Reverse modeling method	L₅ /mm	W₅ /mm	f /GHz	Running time /s
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

Table 1. Performance comparison of four modeling methods

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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