Ahmet Kayabasi and Ali Akdagli
A Comparative Study on ANN, ANFIS and SVM Methods for Computing Resonant Frequency of AShaped Compact Microstrip Antennas
757 - 763
2015
9
8
International Journal of Electronics and Communication Engineering
https://publications.waset.org/pdf/10001803
https://publications.waset.org/vol/104
World Academy of Science, Engineering and Technology
In this study, three robust predicting methods, namely artificial neural network (ANN), adaptive neuro fuzzy inference system (ANFIS) and support vector machine (SVM) were used for computing the resonant frequency of Ashaped compact microstrip antennas (ACMAs) operating at UHF band. Firstly, the resonant frequencies of 144 ACMAs with various dimensions and electrical parameters were simulated with the help of IE3D™ based on method of moment (MoM). The ANN, ANFIS and SVM models for computing the resonant frequency were then built by considering the simulation data. 124 simulated ACMAs were utilized for training and the remaining 20 ACMAs were used for testing the ANN, ANFIS and SVM models. The performance of the ANN, ANFIS and SVM models are compared in the training and test process. The average percentage errors (APE) regarding the computed resonant frequencies for training of the ANN, ANFIS and SVM were obtained as 0.457, 0.399 and 0.600, respectively. The constructed models were then tested and APE values as 0.601 for ANN, 0.744 for ANFIS and 0.623 for SVM were achieved. The results obtained here show that ANN, ANFIS and SVM methods can be successfully applied to compute the resonant frequency of ACMAs, since they are useful and versatile methods that yield accurate results.
Open Science Index 104, 2015