Prediction of Slot-Size and Inserted Air-Gap for Improving the Performance of Rectangular Microstrip Antennas Using Artificial Neural Networks

2013 ◽  
Vol 12 ◽  
pp. 1367-1371 ◽  
Author(s):  
Taimoor Khan ◽  
Asok De ◽  
Moin Uddin
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Microstrip Antennas ◽  
Air Gap ◽  
Slot Size ◽  
Artificial Neural

scholarly journals Prediction of Slot Shape and Slot Size for Improving the Performance of Microstrip Antennas Using Knowledge-Based Neural Networks

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Taimoor Khan ◽  
Asok De
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Prior Knowledge ◽  
Neural Model ◽  
Microstrip Antennas ◽  
Performance Parameters ◽  
Dual Frequency ◽  
Knowledge Based ◽  
Slot Size ◽  
Good Agreement

In the last decade, artificial neural networks have become very popular techniques for computing different performance parameters of microstrip antennas. The proposed work illustrates a knowledge-based neural networks model for predicting the appropriate shape and accurate size of the slot introduced on the radiating patch for achieving desired level of resonance, gain, directivity, antenna efficiency, and radiation efficiency for dual-frequency operation. By incorporating prior knowledge in neural model, the number of required training patterns is drastically reduced. Further, the neural model incorporated with prior knowledge can be used for predicting response in extrapolation region beyond the training patterns region. For validation, a prototype is also fabricated and its performance parameters are measured. A very good agreement is attained between measured, simulated, and predicted results.

scholarly journals Adaptive Forming of the Beam Pattern of Microstrip Antenna with the Use of an Artificial Neural Network

2012 ◽  
Vol 2012 ◽  
pp. 1-13 ◽  
Author(s):  
Janusz Dudczyk ◽  
Adam Kawalec
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Microstrip Antenna ◽  
Microstrip Antennas ◽  
Emission Source ◽  
Application Systems ◽  
Artificial Neural ◽  
Selection Of ◽  
Made In

Microstrip antenna has been recently one of the most innovative fields of antenna techniques. The main advantage of such an antenna is the simplicity of its production, little weight, a narrow profile, and easiness of integration of the radiating elements with the net of generators power systems. As a result of using arrays consisting of microstrip antennas; it is possible to decrease the size and weight and also to reduce the costs of components production as well as whole application systems. This paper presents possibilities of using artificial neural networks (ANNs) in the process of forming a beam from radiating complex microstrip antenna. Algorithms which base on artificial neural networks use high parallelism of actions which results in considerable acceleration of the process of forming the antenna pattern. The appropriate selection of learning constants makes it possible to get theoretically a solution which will be close to the real time. This paper presents the training neural network algorithm with the selection of optimal network structure. The analysis above was made in case of following the emission source, setting to zero the pattern of direction of expecting interference, and following emission source compared with two constant interferences. Computer simulation was made in MATLAB environment on the basis of Flex Tool, a programme which creates artificial neural networks.

Determination of Resonant Frequencies of Triangular and Retangular Microstrip Antennas, Using Artificial Neural Networks

PIERS Online ◽  
2005 ◽  
Vol 1 (5) ◽  
pp. 579-582 ◽  
Author(s):  
Everaldo R. Brinhole ◽  
Jancer F. Z. Destro ◽  
Antonio A. C. de Freitas ◽  
Naasson Alcantara Jr.
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Microstrip Antennas ◽  
Resonant Frequencies ◽  
Artificial Neural
Sign in / Sign up

Export Citation Format

Share Document