Bandwidth Analysis of Slotted Hairpin BandPass Filter Using Neural Network

Hairpin bandpass filter are compact structures they may theoretically be obtained by folding the resonator of parallel-coupled half wave length resonator which reduces the coupling between resonators. This type of U shape resonator is so called hair pin resonator. In the present paper a novel technique has been proposed for the estimation of bandwidth for variation of slot length on the bandpass characteristics of the filter has been presented using artificial neural networks (ANN). The different variants of training algorithms of MLPFFBP-ANN (Multilayer Perceptron feed forward back propagation Artificial Neural Network) have been used to implement the neural network model and compare with Radial basis fewer (RBF) network and concluded that RBF network is more accurate than MLPFFBP and performance comparison of the EM simulated result with ANN is evaluated in terms of maximum estimated error (MSE) KeywordArtificial neural network (ANN), Hairpin band pass filter (HPF), Mean square error (MSE), Multilayer feed forward network, Radial basis fewer (RBF,)Computer simulation technology (CST).