An artificial neural network (ANN) model has been developed for the design of a circular micro-strip antenna. The main advantage of the proposed approach is that, after adequate training, the proposed neural model completely eludes the repeated use of complex iterative processes for the design of such types of antennas, resulting in an extremely fast solution with high precision. The difficulty in the design of antennas patch micro-strip is due to the participation of a large number of physical parameters and, therefore, their associated optimum values. Indeed, it is very difficult to formulate an exact numerical solution through empirical studies based on practical observations. In order to circumvent this problem, an alternative solution is achieved using artificial neural networks. The proposed technique used the feed-forward artificial neural network (FFBP-ANN) with a hidden layer and formed by Levenberg-Marquardt's posterior propagation learning algorithm as an approximate model for the design of micro-strip patch antennas Circular with reasonable accuracy. IE3D software has been used to generate data dictionary for training and validation of ANN set. We also compare the results of the proposed RNA models with simulated and theoretical values, these results are in agreement with them.
Micro-strip antennas are increasing in popularity for use in wireless applications because of their low profile structure. The circular micro-strip antennas are formed by a circular patch on a ground plane with a substrate thickness h and a dielectric constant ε. Modes supported by a circular patch antenna can be found by treating the patch, ground plane, and materials between two circular cavities. The modes that are supported by circular antennas are TMz where z is taken perpendicular to the patch. Micro-strip antennas are used in high performance applications such as spacecraft, aircraft, missiles and satellites, where size, weight, cost, performance, ease of installation and aerodynamic profile are limitations and low antennas may be required profile. To meet these requirements, micro-strip antennas offer an optimal solution. Micro-strip antennas are also referred to as patch antennas due to radiant elements (patches) photographed on the dielectric substrate. This radiant patch can be square, rectangular, circular, elliptical, triangular and any other desired configuration. Neural networks have recently gained attention as a fast and flexible vehicle for EM / microwave modelling, simulations and optimisation. ANN can be used efficiently for the design of various types of micro strip antenna. In the study a comparative evaluation of different variants of the posterior propagation training algorithm has made for the design of the rectangular micro-strip antenna. ANN can also be used to calculate different parameters of the circular micro-strip antenna, such as resonance frequency, input impedance, etc. Sufficient amount of work indicates how ANN can be efficiently used to design circular and rectangular micro-strip antennas. The ANN can also be used to calculate different parameters of the rectangular micro-strip antenna, such as radiation efficiency, resonance frequency, directivity, feed position, triangular and rectangular micro-strip antennas, calculation of resonant resistance of rectangular micro-strip antennas Electrically thin and thick. Micro-strip antennas.