IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308 _______________________________________________________________________________________ Volume: 02 Issue: 12 | Dec-2013, Available @ http://www.ijret.org 120 DESIGN OF C-SLOTTED MICROSTRIP ANTENNA USING ARTIFICIAL NEURAL NETWORK MODEL Pritam Singha Roy 1 , Samik Chakraborty 2 1 Assistant Professor, Electronics Engineering, DIET, West Bengal, India e-mail:prittam.pritam@gmail.com 2 Professor, Communication Engineering, Indian Maritime University, West Bengal, India e-mail:chakra_samik@yahoo.com Abstract In this paper, neural network model has been used to estimation of resonance frequency of a coaxial feed C-slotted Microstrip Antenna. The Multi-Layer Perceptron Feed forward back Propagation (MLPFFBP) and Radial basis function Artificial Neural Network (RBFANN) have been used to implement the neural network model. A relative performance analysis of the proposed neural network for different training algorithms. Number of neurons and number of hidden layer is also carried out for estimating the resonance frequency. The method of moment (MOM) based IE3D software was used to generate data dictionary for training and validation set of ANN. The results obtain using ANN are compared with simulation feeding and found quite satisfactory and also it is concluded that RBFANN network is more accurate and fast compared to MLPFFBP network algorithm. Index Terms: Artificial Neural Network, C slot, Microstrip Antenna, Multilayer Feed Forward Networks, Radial basis function Artificial Neural Network, Resonance frequency. ------------------------------------------------------------------------------------------------------------------------------------------ 1. INTRODUCTION Microstrip antennas due to their many attractive features have drawn attention of industries for an ultimate solution for wireless communication. The existing era of wireless communication has led to the design of an efficient, wide band, low cost and small volume antennas which can readily be incorporated into a broad spectrum of systems [1, 2].sufficient amount of work [3-10] indicates how ANN have been used efficiently to design rectangular Microstrip antenna for the determination of different patch dimensions i,e length,width,resonant frequency, radiation efficiency etc. In this paper, an attempt has been made to exploit the capability of artificial neural networks to calculate the resonating frequency of coaxial feed C-slotted Microstrip patch antenna. The trained ANN is used to determine different important antenna characteristics for various structural input variables.Neoro models are computationally much more efficient than EM models once they are trained with reliable learning data obtained from a “fine” model by either EM simulation or measurement [3, 4, 5, 6].The neuro models can be used for efficient and accurate optimization and design within the range of training. In this work, the authors extend the work on the use of the artificial neural network (ANN) technique taking into account different variants of back propagation training algorithm with MLPFFBP and RBF ANN model are stressed upon in place of conventional numerical techniques for the C-slot microstrip antenna design. Then, with the ANN results, simulated results from the IE3D software are compared. 2. DESIGN AND DATA GENERATION Designing of micro strip patch antenna depends on three parameters. In this paper The rectangular patch Microstrip antenna is designed to resonate at 6.8 GHz frequency with dielectric constant 2.2,Substrate thickness h = 1.5mm ,L=16.01mm and W=19.73mm. The width (W) and length (L) of antenna are calculated from conventional equations [11]. For generating data, we simulated the frequency domain response of the antenna for various patch dimensions, using method of moments based simulation software IE3D TM .For training and testing of the ANN, 100 data sets are generated