A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Artificial Neural Network based Dimension Prediction of Rectangular Microstrip Antenna
https://orcid.org/http://orcid.org/0000-0003-3847-7675
A computational method for the prediction of dimensions of a microstrip antenna has been proposed in this work. The model uses artificial neural network (ANN) as the chief designing tool for the development of the predictor model. The ANN structure has been designed to take three major output parameters as input, viz. resonant frequency, fractional bandwidth and return loss. The outputs of the model are assigned as the three major design parameters of the antenna model: length, width and position of feed point. Equal number of input and output parameters of the model enhances the importance of the model. Backpropagation topology has been adopted while designing the ANN structure. Validation of the proposed ANN model is carried out by designing and simulating the prototype antenna models using the predicted dimensions in IE3D software. The proposed model yields an average error of 1.014% in predicting resonant frequency using the designed antenna with the model-predicted design parameters. Similarly, the average error is found to be 2.38% in case of bandwidth, both of which validates the effectiveness of the predictor model.
Artificial Neural Network based Dimension Prediction of Rectangular Microstrip Antenna
https://orcid.org/http://orcid.org/0000-0003-3847-7675
A computational method for the prediction of dimensions of a microstrip antenna has been proposed in this work. The model uses artificial neural network (ANN) as the chief designing tool for the development of the predictor model. The ANN structure has been designed to take three major output parameters as input, viz. resonant frequency, fractional bandwidth and return loss. The outputs of the model are assigned as the three major design parameters of the antenna model: length, width and position of feed point. Equal number of input and output parameters of the model enhances the importance of the model. Backpropagation topology has been adopted while designing the ANN structure. Validation of the proposed ANN model is carried out by designing and simulating the prototype antenna models using the predicted dimensions in IE3D software. The proposed model yields an average error of 1.014% in predicting resonant frequency using the designed antenna with the model-predicted design parameters. Similarly, the average error is found to be 2.38% in case of bandwidth, both of which validates the effectiveness of the predictor model.
Artificial Neural Network based Dimension Prediction of Rectangular Microstrip Antenna
https://orcid.org/http://orcid.org/0000-0003-3847-7675
A computational method for the prediction of dimensions of a microstrip antenna has been proposed in this work. The model uses artificial neural network (ANN) as the chief designing tool for the development of the predictor model. The ANN structure has been designed to take three major output parameters as input, viz. resonant frequency, fractional bandwidth and return loss. The outputs of the model are assigned as the three major design parameters of the antenna model: length, width and position of feed point. Equal number of input and output parameters of the model enhances the importance of the model. Backpropagation topology has been adopted while designing the ANN structure. Validation of the proposed ANN model is carried out by designing and simulating the prototype antenna models using the predicted dimensions in IE3D software. The proposed model yields an average error of 1.014% in predicting resonant frequency using the designed antenna with the model-predicted design parameters. Similarly, the average error is found to be 2.38% in case of bandwidth, both of which validates the effectiveness of the predictor model.
Artificial Neural Network based Dimension Prediction of Rectangular Microstrip Antenna
J. Inst. Eng. India Ser. B
Mukherjee, Pinaki (author) / Mukherjee, Alok (author) / Chatterjee, Kingshuk (author)
Journal of The Institution of Engineers (India): Series B ; 103 ; 1033-1039
2022-08-01
7 pages
Article (Journal)
Electronic Resource
English
Design of Minkowski Curve-Based Slotted Microstrip Patch Antenna Using Artificial Neural Network
| Springer Verlag | 2023
Rectangular Microstrip Antenna with Slot Embedded Geometry
| Springer Verlag | 2014
Estimation of Resonant Frequency of a Circular Microstrip Antenna Using Artificial Neural Network
| Springer Verlag | 2012
Discharge prediction of circular and rectangular side orifices using artificial neural networks
| Online Contents | 2016