AI Summary & Key Takeaways
AI InsightThe estimated resonant frequencies using the trained model are compared with the studies in the literature, and an average percentage error (APE) of 0.933% is obtained.
📌 3 Key Takeaways
Abstract
In recent years, microstrip antennas have become a popular research subject with the increasing use of mobile technologies. With the development of neural networks, the design and analysis of microstrip antennas are carried out quickly with high accuracy. However, optimizing the weight matrices and bias vectors of deep neural learning models is an important challenge for engineering problems. This study presents a deep neural network-based (DNN-based) neural model to estimate the gain and scattering parameter (S11) of C-shaped compact microstrip antennas (CCMAs). For this purpose, the S11 and gain values of 324 CCMAs with different physical and electrical properties were obtained using full-wave electromagnetic simulation software based on the finite integration technique (FIT). The data related to 324 CCMAs were used for the training and testing process. The improved manta ray foraging optimization (MRFO) algorithm based on the Lévy-flight (LF) mechanism was used to optimize the connection weights matrices and bias vectors. The MRFO-optimized model has estimation success for training and testing data as 0.925 and 0.922, in terms of R2 score, respectively. The estimated resonant frequencies using the trained model are compared with the studies in the literature, and an average percentage error (APE) of 0.933% is obtained.