MLP and KNN Algorithm Model Applications for Determining the Operating Frequency of A-Shaped Patch Antennas

Ahmet Kayabasi


In this study, two machine learning methods, namely multilayer perceptron (MLP) and K-nearest neighbors (KNN) algorithm models are used for determining the operating frequency of A-shaped patch antennas (APAs) at UHF band applications. Firstly, data set is obtained from the 144 antenna simulations using IE3D™ software based on method of moment (MoM). Weka (Waikato Environment for Knowledge Analysis) program was then used to build models by considering 144 simulation data. The models input with the various dimensions and electrical parameters of 124 APAs are trained and their accuracies are tested via 20 APAs. The mean absolute error (MAE) values are calculated for different number of hidden layer neurons and different neighbourhood values in MLP and KNN models, respectively.  The performance of the MLP and KNN models are compared in the training and testing process. The lowest MAEs are obtained with 6 hidden layer neurons for MLP and 2 neighbourhood values for KNN. These results point out that this models can be successfully applied to computation operating frequencies of APAs.


A-shaped patch antenna, operating frequency, multilayer perceptron, K-nearest neighbors algorithm

Full Text:

Submitted: 2017-05-03 11:53:40
Published: 2017-09-29 16:13:25
Search for citations in Google Scholar
Related articles: Google Scholar


K. Wong (2002). Compact and broadband microstrip antennas. John Wiley & Sons, Inc.

G. Kumar and K. P. Ray (2003). Broadband microstrip antennas, Norwood: Artech House.

J. Singh, G. Singh, S. Kaur and B.S. Sohi (2015). Performance analysis of different neural network models for parameters estimation of coaxial fed 2.4 GHz E-shaped Microstrip patch antenna. In Recent Advances in Engineering & Computational Sciences (RAECS), 2nd International Conference on. IEEE. 1─5.

N. Gunavathi and D. Sriramkumar (2015). Analysis and synthesis of coplanar waveguide-feed using Multilayer Perceptron Feed Forward Neural Networks. Signal Processing, Communication and Networking (ICSCN), 3rd International Conference on. IEEE.

A. Ashrf, M. Simsek and Z. Aydin (2015). Development of knowledge based response correction for a reconfigurable N-shaped microstrip antenna design. Numerical Electromagnetic and Multiphysics Modeling and Optimization (NEMO), 2015 IEEE MTT-S International Conference on. IEEE.

J. Jingon (2016). Machine Learning-Based Antenna Selection in Wireless Communications. IEEE Communications Letters. 20 (11) 2241─2244.

IE3D™, version 14, Menthor graphics corporation, Boeckman Road Wilsonville, OR 97070.

R. F. Harrington (1993). Field computation by moment methods. Piscataway, NJ, IEEE Press.

M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann and I. H. Witten (2009). The WEKA Data Mining Software: An Update. SIGKDD Explorations. 11 (1) 10─18.

V. Mai, I. Khalil and C. Meli (2011). ECG biometric using multilayer perceptron and radial basis function neural networks. Engineering in Medicine and Biology Society, EMBC, Annual International Conference of the IEEE.

M. Kantardzic (2003). Data Mining: Concepts, Models, Methods, and Algorithms. John Wiley & Sons Publishing.

J. Wang, P. Neskovic and L. N. Cooper (2007). Improving nearest neighbor rule with a simple adaptive distance measure. Pattern Recognition Letters. 28 (2) 207─213.

Y. Zhou, Y. Li and S. Xia (2009). An improved KNN text classification algorithm based on clustering. Journal of computers. 4 (3) 230─237.

R. Arora (2012). Comparative analysis of classification algorithms on different datasets using WEKA. International Journal of Computer Applications. 54 (13).

Abstract views:


Copyright (c) 2017 International Journal of Intelligent Systems and Applications in Engineering

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
© AtScience 2013-2018     -     AtScience is a registered trademark property of AtScience.