Radiation resistance of coax‐fed rectangular microstrip patch antenna with the use of artificial neural networks

Pattnaik, Shyam S.; Panda, Dhruba C.; Devi, Swapna

doi:10.1002/mop.10370

Cited by 18 publications

(7 citation statements)

References 4 publications

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“…Figure 3 shows the comparison between the number of cycles and the error for both cases. As shown in Table I, the results are closer to the experimental results, as compared to the numerical and analytical results presented in [8,9].…”

Section: Resultssupporting

confidence: 87%

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Application of a genetic algorithm in an artificial neural network to calculate the resonant frequency of a tunable single-shorting-post rectangular-patch antenna

Pattnaik

Khuntia

Panda

et al. 2004

Int J RF and Microwave Comp Aid Eng

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In this article, an efficient application of a genetic algorithm (GA) in an artificial neural network (ANN) to calculate the resonant frequency of a coaxially-fed tunable rectangular microstrip-patch antenna is presented. For a normal feed-forward back-propagation algorithm, with a compromise between time and accuracy, it is difficult to train the network to achieve an acceptable error tolerance. The selection of suitable parameters of ANNs in a feed-forward network leads to a high number of man-hours necessary to train a network efficiently. However, in the present method, the GA is used to reduce the man-hours while training a neural network using the feed forward-back-propagation algorithm. It is seen that the training time has also been reduced to a great extent while giving high accuracy. The results are in very good agreement with the experimental results.

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Section: Resultssupporting

confidence: 87%

“…In [7], the GA was used to assign/find out the initial weight set, which is subsequently processed using a back-propagation algorithm. The algorithm presented in [8] takes a long time to select an optimized model. Although there are some numerical approximations to initialize the various ANN parameters, this is not true in all cases.…”

Section: Introductionmentioning

confidence: 99%

Application of a genetic algorithm in an artificial neural network to calculate the resonant frequency of a tunable single-shorting-post rectangular-patch antenna

Pattnaik

Khuntia

Panda

et al. 2004

Int J RF and Microwave Comp Aid Eng

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“…Moreover, the microstrip patch antennas can provide frequency agility, broad band-width and feed line flexibility [5][6]. Microstrip antennas are used in numerous applications, ranging from biomedical diagnosis to wireless communications [7]. But in spite of many advantages, one major problem of gain and bandwidth is concerned.…”

Section: Introductionmentioning

confidence: 99%

Gain and Bandwidth Enhancement Techniques in Microstrip Patch Antennas - A Review

Kumar¹,

Gupta²,

Gautam³

2016

IJCA

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“…The application of ANN on the field of microstrip patch antenna is very recent. A number of papers [28][29][30][31][32][33][34][35][36][37][38][39][40][41][42][43][44] are present in the literature that signifies the importance of ANN in the field of microstrip patch antenna.…”

Section: Introductionmentioning

confidence: 99%

Design and Analysis of Microstrip Line Feed Toppled T Shaped Microstrip Patch Antenna using Radial Basis Function Neural Network

Aneesh¹,

Kumar²,

Singh³

et al. 2015

Journal of Electrical Engineering and Technology

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-This paper deals with the design of a microstrip line feed toppled T shaped microstrip patch antenna that gives dualband characteristics at 4 GHz and 6.73 GHz respectively. The simulation of proposed antenna geometry has been performed using method of moment based IE3D simulation software. A radial basis function neural network (RBFNN) is used for the estimation of bandwidth for dualband at 4 GHz and 6.73 GHz respectively. In RBFNN model, antenna parameters such as dielectric constant, height of substrate, and width are used as input and bandwidth of first and second band is considered as output of the network. To validate the RBFNN output, an antenna has been physically fabricated on glass epoxy substrate. The fabricated antenna can be utilized in S and C bands applications. RBFNN results are found in close agreement with simulated and experimental results.

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Radiation resistance of coax‐fed rectangular microstrip patch antenna with the use of artificial neural networks

Cited by 18 publications

References 4 publications

Application of a genetic algorithm in an artificial neural network to calculate the resonant frequency of a tunable single-shorting-post rectangular-patch antenna

Application of a genetic algorithm in an artificial neural network to calculate the resonant frequency of a tunable single-shorting-post rectangular-patch antenna

Gain and Bandwidth Enhancement Techniques in Microstrip Patch Antennas - A Review

Design and Analysis of Microstrip Line Feed Toppled T Shaped Microstrip Patch Antenna using Radial Basis Function Neural Network

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