Artificial neural network based design of a microstrip patch antenna for RADAR applications

Neebha, T. Mary; Nesasudha, M.

doi:10.1109/icraae.2017.8297224

Cited by 10 publications

(9 citation statements)

References 8 publications

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“…As seen in Figure 5 and Figure 6, while the harmony is high at low frequencies, the discrepancy between the estimated data and the simulation data increases as the frequency increases. As mentioned in the literature review, most of the studies related to CCMAs have focused on optimizing the antenna designs for specific applications and obtaining mathematical models that usually provide the resonant frequency [3,5,6,[17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34]. Although this study is not directly related to the estimation of resonant frequencies of CCMAs, the resonant frequencies in dominant mode (TM010) were indirectly estimated by the interpretation of the S11 curves, and the comparison between the results obtained in this study and the results in the literature [7,23] is shown in Table 5.…”

Section: Numerical Results and Discussionmentioning

confidence: 99%

“…In recent years, researchers have vigorously challenged the accurate calculation of the resonant frequency of CMAs. While most studies focus on designing and optimizing CMA structures [3,[17][18][19][20] and obtaining mathematical models for determining the resonant frequencies of the antennas [5,7,[21][22][23][24], there are also studies on computing the resonant frequencies or other characteristic parameters using neural network models [25][26][27][28][29][30][31][32][33][34]. In most recent studies, resonant frequency estimation was made in two ways: the closed-form expression approach and the neural model approach.…”

Section: Introductionmentioning

confidence: 99%

“…Abbassi et al [38] have designed a CMA operating at a frequency of 2.4 GHz and used a NN model to predict the S11 and gain parameters. Neebha and Nesasudha [34] have designed a CMA for C-band applications using an ANN model. Four antenna-related parameters were used as the network input, and two physical parameters were estimated [34].…”

Section: Introductionmentioning

confidence: 99%

“…Neebha and Nesasudha [34] have designed a CMA for C-band applications using an ANN model. Four antenna-related parameters were used as the network input, and two physical parameters were estimated [34]. Singh et al [33] studied on a coaxial-fed E-shaped CMA (ECMA) operating at the 2.4 GHz frequency.…”

Section: Introductionmentioning

confidence: 99%

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Analysis of C-shaped Compact Microstrip Antennas Using Deep Neural Networks Optimized by Manta Ray Foraging Optimization with Lévy-Flight Mechanism

Biçer

2021

Sakarya University Journal of Computer and Information Sciences

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In recent years, microstrip antennas have become a popular research subject with the increasing use of mobile technologies. With the development of neural networks, the design and analysis of microstrip antennas are carried out quickly with high accuracy. However, optimizing the weight matrices and bias vectors of deep neural learning models is an important challenge for engineering problems. This study presents a deep neural network-based (DNN-based) neural model to estimate the gain and scattering parameter (S11) of C-shaped compact microstrip antennas (CCMAs). For this purpose, the S11 and gain values of 324 CCMAs with different physical and electrical properties were obtained using full-wave electromagnetic simulation software based on the finite integration technique (FIT). The data related to 324 CCMAs were used for the training and testing process. The improved manta ray foraging optimization (MRFO) algorithm based on the Lévy-flight (LF) mechanism was used to optimize the connection weights matrices and bias vectors. The MRFO-optimized model has estimation success for training and testing data as 0.925 and 0.922, in terms of R 2 score, respectively. The estimated resonant frequencies using the trained model are compared with the studies in the literature, and an average percentage error (APE) of 0.933% is obtained.

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Section: Numerical Results and Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Analysis of C-shaped Compact Microstrip Antennas Using Deep Neural Networks Optimized by Manta Ray Foraging Optimization with Lévy-Flight Mechanism

Biçer

2021

Sakarya University Journal of Computer and Information Sciences

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“…and Nesasudha proposed an ANN based system for microstrip patch antenna design for 5 to 8 GHz radar applications by using a multilayer perceptron and Levenberg-Marquardt algorithm for training processes. 11 Sabanci et al proposed a frequency estimator for notch antenna by ANN with multilayer perceptron algorithm. 12 Multi parameter estimation of patch antenna was proposed with enhanced accuracy by ANN model in a recently conducted paper.…”

mentioning

confidence: 99%

Ground plane design configuration estimation of 4.9 GHz reconfigurable monopole antenna for desired radiation features using artificial neural network

Alkurt

Ozdemir

Akgöl

et al. 2021

Int J RF Mic Comp-Aid Eng

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This paper presents a system based on artificial neural network (ANN) that predicts ground plane design for desired radiation properties of a monopole antenna with operation band of 4.8 to 5 GHz. The operating frequency can be adapted to any other frequency regimes. Initially, a 180 Â 180 mm2 ground plane, which is composed of a copper layer, is designed and integrated to a radiative pole that creates monopole antenna configuration. The ground plane is divided into 18 rows and 18 columns as 18 Â 18 matrix so that each unit cell has a square shape having 10 mm side length. Moreover, 152 different ground plane configurations are created by using logic 1 s and 0 s. Multi-layered feed forward ANN is used along with Scale Conjugate Gradient learning algorithm to design ground plane of the monopole antenna. Simulated 152 random ground plane arrays and obtained radiation patterns are used to train ANN for the ground plane design. If a user wants to manipulate radiation, artificial neural network gives the optimum ground plane design for the desired radiation direction and gain with 91.03% accuracy. Finally, one test antenna is fabricated and experimentally measured to support the results of the simulated one. The proposed ANN model approach can be easily used for antenna applications in the antenna industry. K E Y W O R D Santenna design, artificial neural network, monopole antenna, optimized ground plane | INTRODUCTIONRecently, artificial neural networks (ANN) have become very interesting in all areas of engineering applications [1][2][3][4] especially in antenna engineering. [5][6][7][8][9][10] Applications towards to the design parameter estimations of circular patch antennas with the help of ANN algorithms were presented by researchers, 5,6 and also modeling and designs of slotted fractal antenna and Wi-Fi antenna were investigated by using ANNs. 7,8 Moreover, Neebha

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Automated optimization for broadband flat‐gain antenna designs with artificial neural network

Mir

Kouhalvandi

Matekovits

et al. 2021

IET Microwaves Antenna & Prop

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An automated optimization process for designing and optimising high-performance single microstrip antennas is presented. It consists of the successive use of two optimization methods, bottom-up optimization (BUO) and Bayesian optimization (BO), which are applied sequentially, resulting in electromagnetic (EM)-based artificial neural network modelling. The BUO method is applied for the initial design of the structure of the antennas whereas the BO approach is successively implemented to predict suitable dimensional parameters, leading to broadband, high flat-gain antennas. The optimization process is performed automatically with the combination of an electronic design automation tool and a numerical analyser. The proposed method is easy to use; it allows one to perform the design with little experience, because both structure modelling and sizing are performed automatically. To verify the power of the proposed EM-based method experimentally, two single microstrip antennas have been designed, optimised, fabricated, and measured. The first antenna has flat-gain performance (6.9-7.2 dB) in a frequency band of 8.8-10 GHz. The second has been designed to perform in the 8.7-to 10-GHz band, where it exhibits flat-gain performance with reduced fluctuation in the range of 6.7-7 dB. The experimental results are in good agreement with the numerical data.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Artificial neural network based design of a microstrip patch antenna for RADAR applications

Cited by 10 publications

References 8 publications

Analysis of C-shaped Compact Microstrip Antennas Using Deep Neural Networks Optimized by Manta Ray Foraging Optimization with Lévy-Flight Mechanism

Analysis of C-shaped Compact Microstrip Antennas Using Deep Neural Networks Optimized by Manta Ray Foraging Optimization with Lévy-Flight Mechanism

Ground plane design configuration estimation of 4.9 GHz reconfigurable monopole antenna for desired radiation features using artificial neural network

Automated optimization for broadband flat‐gain antenna designs with artificial neural network

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