11/4/2022 0 Comments Microstrip antenna theory![]() Devi, "Radiation resistance of coax-fed rectangular microstrip antenna using artificial neural networks," Microwave and Optical Technology Lett., Vol. Sarikaya, "Resonant frequency calculation for circular microstrip antennas with a dielectric cover using adaptive network-based fuzzy inference system optimized by various algorithms," Progress In Electromagnetic Research, Vol. Erler, "Neural computation of resonant frequency of electrically thin and thick rectangular microstrip antennas," IEEE Proceedings - Microwaves, Antennas and Propagation, Vol. Dash, "An artificial neural network model for effective dielectric constant of microstrip line," IEEE Trans. Patnaik, "Neural network-based CAD model for the design of square-patch antennas," IEEE Trans. Pattnaik, "A novel method of using artificial neural networks to calculate input impedance of circular microstrip antenna," Antennas and Propagation Society International Symposium, Vol. Mansour, "Application of neural networks in microwave circuit modelling," Electrical and computer Engineering, 1998, IEEE Canadian Conference, Vol. Yildirim, "Artificial neural design of microstrip antennas," Turk. Naghsh, "Heuristic artificial neural network for analysing and synthesizing rectangular microstrip anteena," IJCSNS International Journal of Computer Science and Network Security, Vol. Nakhla, "Analysis and optimization of microwave circuits & devices using neural network models," IEEE MTT-S Digest, 393-396, 1994.Ĥ. Gupta, "Design and optimization of CPW circuits using EM ANN models for CPW components," IEEE Transactions on Microwave Theory and Techniques, Vol. Gupta, Neural Networks for RF and Microwave Design, Artech House Publishers, 2000.Ģ. The results obtained from artificial neural network when compared with experimental and simulation results, found satisfactory and also it is concluded that RBF network is more accurate and fast as compared to different variants of backpropagation training algorithms of MLPFFBP.ġ. The different variants of training algorithm of MLPFFB-ANN (Multilayer Perceptron feed forward back propagation Artificial Neural Network) and RBF -ANN (Radial basis function Artificial Neural Network) has been used to implement the network model. ![]() ![]() The example antenna is also designed physically with glass epoxy substrate with ε r = 4.7 for few results for testing the artificial neural network model. MICROSTRIP ANTENNA THEORY SOFTWAREThe Method of Moments (MOM) based IE3D software has been used to generate training and test data for the ANN. In the present work an Artificial Neural Network (ANN) model is developed to analyse the bandwidth of the example antenna. MICROSTRIP ANTENNA THEORY PATCHThe illustrated patch antenna gives enhanced bandwidth as compared to antenna with out slots of the same physical dimensions. This paper deals with the design of slotted microsrip antenna on a substrate of thickness 1.588 mm that gives wideband characteristics using ANN. In this paper a new approach is proposed to design inset feed microstrip antenna with slots in it to improve the antenna bandwidth. Many applications of microstrip antenna are rendered by their inherent narrow bandwidth. ![]()
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