Artificial Neural Network Model With Firefly Algorithm for Seljuk Star Shaped Microstrip Antenna

Abstract

In this study, Seljuk Star microstrip antenna (SSMA) design based on the hybrid Artificial Neural Network model for frequency values in the range of 0.5-3.5 GHz has been performed. In the present study, a novel model is developed for training neural network by combining a back propagation (BP) and a meta-heuristic algorithm. The major disadvantage of back propagation in finding solutions is that it stuck local minima rather than global one. In this new hybrid training algorithm, local and global search made simultaneously. Initially, Firefly Algorithm (FA) was utilized to obtain weights of neural network due to the lower probability of entrapment into local minima thanks to long jump. Subsequently, this algorithm was combined with the local search capability of the BP algorithm and used to train the artificial neural network. Levenberg-Marquardt algorithm was preferred due to providing fast convergence and stability in training process of Artificial Neural Networks. In this paper, Seljuk Star microstrip antenna has been designed on DE104, double faced with 1.55mm dielectric and 35um conductor thickness, which has an electrical conductivity of 4.37 and a loss tangent of 0.002. HFSS antenna simulation program was used to design for 272 microstrip antennas. 90% of the data set was used as training and 10% as test data. The ANN with Firefly Algorithm results are more in agreement with the simulating results.