Resonant frequency calculation for circular microstrip antennas using artificial neural networks

1998 ◽  
Vol 8 (3) ◽  
pp. 270-277 ◽  
Author(s):  
?eref Sa?iro?lu ◽  
Kerim G�ney ◽  
Mehmet Erler
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Resonant Frequency ◽  
Microstrip Antennas ◽  
Frequency Calculation ◽  
Artificial Neural

scholarly journals Calculation of Resonant Frequency for a Microstrip Antenna with Vertical Slots Using Applying Adaptive Network-Based Fuzzy Inference System

2017 ◽  
Author(s):  
Mahmood Abbasi Layegh ◽  
Changiz Ghobadi ◽  
Javad Nourinia
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Resonant Frequency ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
High Approximation ◽  
Adaptive Network ◽  
Resonant Frequencies ◽  
Inference System ◽  
Artificial Neural

This paper attempts at applying adaptive network-based fuzzy inference system (ANFIS) for analysis of the resonant frequency of a microstrip rectangular patch antenna with two equal size slots which are placed on the patch vertically. The resonant frequency is calculated as the position of slots is shifted to the right and left sides on the patch. As a result , the antenna resonates at more than one frequency . Commonly, machine algorithms based on artificial neural networks are employed to recognize the whole resonant frequencies. However ,they fail to estimate the resonant frequencies correctly as in some cases variations are not very sensible and the resonant frequencies overlap each other . It can be concluded that artificial neural networks could be replaced in such designs by the adaptive network-based fuzzy Inference system due to its high approximation capability and much faster convergence rate.

scholarly journals Comparison of the Resonant Frequency Determination of a Microstrip Patch Antenna using ANN and Analytical Methods

2017 ◽  
Vol 6 (1) ◽  
pp. 1
Author(s):  
Lahcen Aguni ◽  
Samira Chabaa ◽  
Saida Ibnyaich ◽  
Abdelouhab Zeroual
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Dielectric Permittivity ◽  
Resonant Frequency ◽  
Analytical Methods ◽  
Patch Antenna ◽  
Good Argument ◽  
Microstrip Patch ◽  
Rectangular Patch ◽  
Artificial Neural

In this paper we are interested to calculate the resonant frequency of rectangular patch antenna using artificial neural networks based on the multilayered perceptrons. The artificial neural networks built, transforms the inputs which are, the width of the patch W, the length of the patch L, the thickness of the substrate h and the dielectric permittivity to the resonant frequency fr which is an important parameter to design a microstrip patch antenna.The proposed method based on artificial neural networks is compared to some analytical methods using some statistical criteria. The obtained results demonstrate that artificial neural networks are more adequate to achieve the purpose than the other methods and present a good argument with the experimental results available in the literature. Hence, the artificial neural networks can be used by researchers to predict the resonant frequency of a rectangular patch antenna knowing length (L), width (W), thickness (h) and dielectric permittivity with a good accuracy.

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.

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