Prediction of Slot-Shape, Slot-Size and Inserted Air-Gap of a Microstrip Antenna Using Knowledge-Based Neural Network

Khan, Taimoor; Де, Асок

doi:10.2528/pierc16011602

Cited by 2 publications

(5 citation statements)

References 23 publications

(58 reference statements)

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“…The antenna substrate is selected as Rogers RT/duroid 5870 with relative permittivity of 2.33, thickness of 0.787 mm, and loss tangent of 0.0012. By using (20)(21)(22)(23), antenna patch dimensions L and W are computed as 38.6 mm and 47.5 mm, respectively. Then, parametric analysis in a High-Frequency Structural Simulator (HFSS) [37] is carried out to find the best reflection coefficient and input impedance and tune the resonance frequency to be close to 2.4 GHz for obtaining the feed location of the microstrip patch antenna without slots.…”

Section: Design Of Triple-band Antennamentioning

confidence: 99%

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Soft Computing Approach to Design a Triple-Band Slotted Microstrip Patch Antenna

Yigit

Günel

Aydemir³

et al. 2022

Applied Sciences

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The design process of antenna structures that meet up-to-date requirements takes a long time and brings a high computational load. In this paper, an approach based on Soft Computing (SC) techniques was used to shorten the design time and to achieve an antenna structure that yields performance characteristics as close as possible to the desired values. In order to obtain a microstrip patch antenna with the targeted characteristics and the best accuracy in a faster way, a Support Vector Machine (SVM)-based regression model was employed. A triple-band microstrip antenna with desired resonance frequencies and gain values was designed by using the Support Vector Regression (SVR) model by introducing multiple slots and arc-truncation to the patch antenna. Simulation results of the High-Frequency Structural Simulator (HFSS) and measurements of implementation of the designed antenna are given. Performance characteristics of the obtained antenna are also compared with those given in the literature, which have triple-band properties. In addition, the antenna was redesigned using the optimization tool in HFSS for comparison. The accuracy of the results and required time for design were compared for both the SVR model approach and the HFSS optimization tool.

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Section: Design Of Triple-band Antennamentioning

confidence: 99%

“…SC is the aggregation of various computational techniques. Some of the SC techniques and their applications include fuzzy logic [1,2], evolutionary algorithms [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18], artificial neural networks (ANN) [19][20][21][22][23][24][25][26][27], and support vector machines (SVM) [19,20,28].…”

Section: Introductionmentioning

confidence: 99%

Soft Computing Approach to Design a Triple-Band Slotted Microstrip Patch Antenna

Yigit

Günel

Aydemir³

et al. 2022

Applied Sciences

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“…Thus, for a complicated geometry, sometime it becomes a time‐consuming process. For fulfilling such obligations, several artificial neural networks (ANN) based models have been proposed . A technique based on genetic algorithm (GA) is implied in an ANN for determining the operating frequency of a single‐shorting‐post MSA .…”

Section: Introductionmentioning

confidence: 99%

“…For fulfilling such obligations, several artificial neural networks (ANN) based models have been proposed. [8][9][10][11][12][13][14] A technique based on genetic algorithm (GA) is implied in an ANN for determining the operating frequency of a single-shorting-post MSA. 8 Various MSA designs are analyzed where different ANN structures are implied rigorously.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of slot‐position and slot‐size of a microstrip antenna using support vector regression

Khan

Roy

2018

Int J RF Microw Comput Aided Eng

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This article presents a synthesis modeling scheme of rectangular microstrip antenna with support vector regression (SVR) scheme. Here, radiating patch and ground surface is loaded with two asymmetrical slots and two symmetrical slots, respectively. The position of the slots on the radiating patch as well as the size of the slots on the ground surface are predicted using SVR model and artificial neural network (ANN) model. A good convergence rate has been addressed in synthesis model by employing the adaptive step‐size. A comparison between SVR model and ANN model is presented where SVR is more accurate and faster than ANN. The suggested SVR approach is also validated by fabricating and characterizing a prototype of microstrip antenna. A very good agreement is observed in measured, simulated, and predicted results. The predicted microstrip antenna has displayed quite good agreement between measured and simulated performance parameters.

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Prediction of Slot-Shape, Slot-Size and Inserted Air-Gap of a Microstrip Antenna Using Knowledge-Based Neural Network

Cited by 2 publications

References 23 publications

Soft Computing Approach to Design a Triple-Band Slotted Microstrip Patch Antenna

Soft Computing Approach to Design a Triple-Band Slotted Microstrip Patch Antenna

Prediction of slot‐position and slot‐size of a microstrip antenna using support vector regression

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