Artificial Neural Network (ANNs) techniques are recently indicating a lot of promises in the application of various micro-engineering fields. Such a use of ANNs for estimating the patch dimensions of a microstrip line...Artificial Neural Network (ANNs) techniques are recently indicating a lot of promises in the application of various micro-engineering fields. Such a use of ANNs for estimating the patch dimensions of a microstrip line feed rectangular microstrip patch antennas has been presented in this paper. An ANN model has been developed and tested for rectangular patch antenna design. The performance of the neural network has been compared with the simulated values obtained from IE3D EM Simulator. It transforms the data containing the dielectric constant (εr), thickness of the substrate (h), and antenna’s dominant-mode resonant frequency (fr) to the patch dimensions i.e length (L) and width (W) of the patch. The different variants of back propagation training algorithm of MLFFBP-ANN (Multilayer 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 results obtained from artificial neural network when compared with simulation results, found satisfactory and also it is concluded that RBF network is more accurate and fast as compared to different variants of back propagation training algorithms of MLPFFBP. The ANNs results are more in agreement with the simulation findings. Neural network based estimation has the usual advantage of very fast and simultaneous response of all the outputs.展开更多
文摘Artificial Neural Network (ANNs) techniques are recently indicating a lot of promises in the application of various micro-engineering fields. Such a use of ANNs for estimating the patch dimensions of a microstrip line feed rectangular microstrip patch antennas has been presented in this paper. An ANN model has been developed and tested for rectangular patch antenna design. The performance of the neural network has been compared with the simulated values obtained from IE3D EM Simulator. It transforms the data containing the dielectric constant (εr), thickness of the substrate (h), and antenna’s dominant-mode resonant frequency (fr) to the patch dimensions i.e length (L) and width (W) of the patch. The different variants of back propagation training algorithm of MLFFBP-ANN (Multilayer 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 results obtained from artificial neural network when compared with simulation results, found satisfactory and also it is concluded that RBF network is more accurate and fast as compared to different variants of back propagation training algorithms of MLPFFBP. The ANNs results are more in agreement with the simulation findings. Neural network based estimation has the usual advantage of very fast and simultaneous response of all the outputs.
