Abstract—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 A-shaped 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. Keywords—A-shaped compact microstrip antenna, Artificial Neural Network (ANN), adaptive Neuro-Fuzzy Inference System (ANFIS), Support Vector Machine (SVM). I. INTRODUCTION RESENT portable communication and handheld devices inherently need miniaturized microstrip antennas (MAs). By using the substrate materials with high dielectric constant, the smaller antennas can be achieved but this gives rise to decrease the bandwidth and efficiency performances [1]. Thus, it is difficult to carry out the requirements of mobile communication devices by using the traditional MAs. The compact geometry has been proved as an alternate methodology to design miniature microstrip antennas. The compact microstrip antennas (CMAs) are obtained by applying some modification such as slot-loading and shorting- pin/wall on traditional MA structures [1]. Several slot loaded CMA configurations such as C [2]-[5], E [6]-[10], H [2], [3], [11]-[13], L [14], [15], annular ring [16]-[19] and rectangular ring [2], [20] shapes have been presented in the literature as an Ahmet Kayabasi is with the Department of Electronic & Automation, Silifke-Tasucu Vocational School, Selcuk University, 33900, Silifke, Mersin, Turkey (e-mail: ahmetkayabasi@selcuk.edu.tr). Ali Akdagli is with the Department of Electrical & Electronics Engineering, Faculty of Engineering, Mersin University, 33343, Ciftlikkoy, Mersin, Turkey (email: akdagli@mersin.edu.tr). alternative and effectively method to physically reduce the antenna size by increasing the effective resonant length. A- shaped CMA (ACMA) is also one of the configurations obtained by using the method of slots loading on the patch. It is observed that the ACMAs show similar features with C, E, H, L, annular ring and rectangular ring CMAs. The antenna designers can make selection among these designs according to the devices to be placed inside. In analysis of the conventional MA, techniques such as cavity model [21] and transmission line model [22] are used. However, because of irregular shapes, CMAs may not be analyzed with use of these techniques. Simulation and experimental studies are therefore, carried out in analysis and design of CMAs, in general. Powerful simulation tools, which employ electromagnetic methods involving rigorous mathematical formulation and extensive numerical procedures such as finite difference time domain (FDTD) method [23] and method of moment (MoM) [24] are widely utilized; however, the design procedure may be highly time consuming using these tools. It is shown that the results of simulation tools are consistent with the experimental results in the literature [3]-[5], [8]-[10], [12], [13], [15], [17]-[20]. It is well known that current advancements in wireless communication technology have led to increase the use of CMAs; hence, simple models should be utilized to analyze their performances such as bandwidth and resonant frequency. On the other hand, the resonant frequency is of crucial importance in the CMA design process because these antennas inherently suffer from the narrow bandwidth. Alternative simple ways should therefore be investigated by taking into consideration that the analysis of the microstrip patch is a complex problem because of the fringing fields at the edges. There exist several approaches which vary in accuracy and computational efforts have been proposed to analyze and design CMAs. The most widely used can be listed as formulation methods [2]-[4], [9], [12], [17] and artificial intelligent systems (AIs) [5], [10], [13], [15], [18]-[20]. Formulation methods are commonly derived with the aid of the optimization algorithm such as genetic, particle swarm, differential evolution etc. The most well-known artificial intelligent systems are the artificial neural network (ANN) [25]-[27] and the adaptive neuro-fuzzy interference system (ANFIS) [28]-[30] and the support vector machine (SVM) [31], [32]. This paper deals with the computing the resonant frequency of the ACMAs operate in UHF band suitable for miniaturized A Comparative Study on ANN, ANFIS and SVM Methods for Computing Resonant Frequency of A-Shaped Compact Microstrip Antennas Ahmet Kayabasi, Ali Akdagli P World Academy of Science, Engineering and Technology International Journal of Electronics and Communication Engineering Vol:9, No:8, 2015 757 International Scholarly and Scientific Research & Innovation 9(8) 2015 scholar.waset.org/1307-6892/10001803 International Science Index, Electronics and Communication Engineering Vol:9, No:8, 2015 waset.org/Publication/10001803