Designing rectangular patch antenna using the neurospectral method

Mishra, Rabindra Kishore; Patnaik, Amalendu

doi:10.1109/tap.2003.814748

Cited by 64 publications

(44 citation statements)

References 31 publications

(23 reference statements)

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“…The network is trained using the gradient descent method and the error calculated is sent back to the hidden layer as well as the output layer for weight adjustments [13]. In multilayer perceptron network there are two sets of weights, namely from input layer to hidden layer and from hidden to output layer.…”

Section: Back-propagation Algorithmmentioning

confidence: 99%

Design and Analysis of Microstrip Patch Antennas Using Artificial Neural Network

Kushwah¹,

Tomar²

2017

Trends in Research on Microstrip Antennas

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The microstrip patch antenna can also be designed using an artificial neural network (ANN) modeling technique where size of the antenna is major limitation especially in mobile and wireless applications. In this chapter, analysis and synthesis problems for designing of microstrip patch antennas were discussed using the artificial neural network technique. An analysis problem refers to calculation of resonant frequency of microstrip patch antenna whereas a synthesis problem refers to calculation of dimensions of patch antenna. Both problems are reciprocal of each other. Results are implemented using graphical user interface (GUI) tools of MATLAB programming language. Backpropagation training algorithm of artificial neural network is used to train the network for minimization of error and computation time. Therefore, the geometric dimensions of patch are obtained with high accuracy in less computation time as compared to simulation software.

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Section: Back-propagation Algorithmmentioning

confidence: 99%

Design and Analysis of Microstrip Patch Antennas Using Artificial Neural Network

Kushwah¹,

Tomar²

2017

Trends in Research on Microstrip Antennas

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“…The overall formulated and simulated models are very easy for application purpose and also less complex for the engineers to predict & find out different values of resonant frequency & patch dimensions. [8][9][10][11].…”

Section: International Journal Of Computer Applications (0975 -8887) mentioning

confidence: 99%

Design and Analysis of Printed Rectangular Micro Strip Antenna on Ferrite Material

kour¹

2015

IJCA

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The study of Micro strip patch antenna is proposed by using artificial neural network. The geometry modelled for the study is rectangular patch type which will be mounted on ferrite substrate. The artificial neural network is used to study the modelled micro strip antenna by using synthesis and analytical approach. The relation between different parameter and dimensions of micro strip antenna are obtained. The patch dimension which is denoted by (W,L) are dependent of various input variable that can be further divided as resonant frequency(fr), height of dielectric substrate(h) and the dielectric constant of dielectric material(εx, εy). The results which are obtained using artificial neural network for the designed antenna are comparable with the result available in the patch antennas literature, theoretically as well as experimentally. The designed procedure is divided into synthesis and analysis which are forward and reverse side respectively. The designed procedure is used and ANN can be applied to the other geometry of antenna in general approach.Index Terms-Artificial neural networks, Micro strip antenna, reverse modelling, radial basis function, Ferrite material.

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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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Designing rectangular patch antenna using the neurospectral method

Cited by 64 publications

References 31 publications

Design and Analysis of Microstrip Patch Antennas Using Artificial Neural Network

Design and Analysis of Microstrip Patch Antennas Using Artificial Neural Network

Design and Analysis of Printed Rectangular Micro Strip Antenna on Ferrite Material

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

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