<abstract xmlns="http://www.w3.org/1999/xhtml"><p> An application of artificial neural network (ANN) based on multilayer perceptrons (MLP) to compute the resonant frequency of E-shaped compact microstrip antennas (ECMAs) is presented in this paper. The resonant frequencies of 144 ECMAs with different dimensions and electrical parameters were firstly determined by using IE3D(<sup>tm) </sup>software based on the method of moments (MoM), then the ANN model for computing the resonant frequency was built by considering the simulation data. The parameters and respective resonant frequency values of 130 simulated ECMAs were employed for training and the remaining 14 ECMAs were used for testing the model. The computed resonant frequencies for training and testing by ANN were obtained with the average percentage errors (APE) of 0.257% and 0.523%, respectively. The validity and accuracy of the present approach was verified on the measurement results of an ECMA fabricated in this study. Furthermore, the effects of the slots loading method over the resonant frequency were investigated to explain the relationship between the slots and resonant frequency.</p></abstract>

An application of artificial neural network (ANN) based on multilayer perceptrons (MLP) to compute the resonant frequency of E-shaped compact microstrip antennas (ECMAs) is presented in this paper. The resonant frequencies of 144 ECMAs with different dimensions and electrical parameters were firstly determined by using IE3D(tm) software based on the method of moments (MoM), then the ANN model for computing the resonant frequency was built by considering the simulation data. The parameters and respective resonant frequency values of 130 simulated ECMAs were employed for training and the remaining 14 ECMAs were used for testing the model. The computed resonant frequencies for training and testing by ANN were obtained with the average percentage errors (APE) of 0.257% and 0.523%, respectively. The validity and accuracy of the present approach was verified on the measurement results of an ECMA fabricated in this study. Furthermore, the effects of the slots loading method over the resonant frequency were investigated to explain the relationship between the slots and resonant frequency.

An Application of Artificial Neural Network to Compute the Resonant Frequency of E–Shaped Compact Microstrip Antennas

Department of Electrical & Electronics Engineering, Faculty of Engineering, Mersin University, 33343, Ciftlikkoy, Mersin, Turkey

Department of Information Technologies, Mersin University, 33343, Ciftlikkoy, Mersin, Turkey

Department of Electronic & Automation, Silifke-Tasucu Vocational School, Selcuk University, 33900, Silifke, Mersin, Turkey

Department of Electrical & Electronics Engineering, Faculty of Engineering, Erciyes University, 38039, Kayseri, Turkey

Journal of Electrical Engineering

An application of artificial neural network (ANN) based on multilayer perceptrons (MLP) to compute the resonant frequency of E-shaped compact microstrip...

An Application of Artificial Neural Network to Compute the Resonant Frequency of E–Shaped Compact Microstrip Antennas

Online veröffentlicht: 15. Okt. 2013

Seitenbereich: 317 - 322

DOI: https://doi.org/10.2478/jee-2013-0046

Schlüsselwörter
compact microstrip antenna, E-shaped antenna, resonant frequency, artificial neural network (ANN)

This content is open access.

An Application of Artificial Neural Network to Compute the Resonant Frequency of E–Shaped Compact Microstrip Antennas

Online veröffentlicht: 15. Okt. 2013

Seitenbereich: 317 - 322

DOI: https://doi.org/10.2478/jee-2013-0046

Schlüsselwörtercompact microstrip antenna, E-shaped antenna, resonant frequency, artificial neural network (ANN)

This content is open access.

Schlüsselwörter
compact microstrip antenna, E-shaped antenna, resonant frequency, artificial neural network (ANN)