Design of a wideband microstrip antenna and the use of artificial neural networks in parameter calculation

Neog, Dipak Kr.; Pattnaik, Shyam S.; Panda, Dhruba C.; Devi, Swapna; Khuntia, Bonomali; Dutta, Maitreyee

doi:10.1109/map.2005.1532541

Cited by 67 publications

(36 citation statements)

References 15 publications

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“…Hence, the individual neural models [9][10][11][12][13][14][15][16] and generalized neural approaches [17][18][19][20] have been used only for resonance frequency and/or geometric dimensions of microstrip patch antennas. Few neural networks models [21][22][23] have also been proposed for designing the slotted microstrip antennas. But unfortunately, simultaneous computations of different performance parameters (i.e., resonance frequency, gain, directivity, antenna efficiency, and radiation efficiency) using neural networks model have been rarely attempted in the available literature [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23] as these parameters are essentially required for antenna designers for synthesizing the MSAs.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Different Performance Parameters of Slotted Microstrip Antennas with Air-Gap Using Neural Networks

Khan

Де

2014

ISRN Electronics

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Over the past decade, artificial neural networks have emerged as fast computational medium for predicting different performance parameters of microstrip antennas due to their learning and generalization features. This paper illustrates a neural network model for instantly predicting the resonance frequencies, gains, directivities, antenna efficiencies, and radiation efficiencies for dual-frequency operation of slotted microstrip antennas with air-gap. The proposed neural model is valid for any arbitrary slotdimensions and inserted air-gap within their specified ranges. A prototype is fabricated using Roger's substrate and its performance is measured for validation. A very good agreement is achieved in simulated, predicted, and measured results.

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Section: Introductionmentioning

confidence: 99%

Estimation of Different Performance Parameters of Slotted Microstrip Antennas with Air-Gap Using Neural Networks

Khan

Де

2014

ISRN Electronics

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“…Having reduced the training data set significantly, we can subsequently utilize a supervised learning NN architecture. In many applications of NN in microstrip antenna design, the training vectors include the parameters of the geometry of the antenna and those of the substrate and the required results at the output of the NN are the indices of operation, that is the gain, the resonant frequency, the bandwidth, the polarization etc, [89], or the radiation pattern [90]. In [40], however, the inverse problem is solved.…”

Section: Artificial Neural Networkmentioning

confidence: 99%