Abstract—In electromagnetic directivity is a figure of merit for an antenna. It measures the power density of an actual antenna in the direction of its strongest emission, relative to the power density radiated by an ideal isotropic radiator antenna radiating the same amount of total power. It is closely related to the phase difference and distance between array elements. A neural network-based solution can exploit the prior knowledge of the radiating system to relate a given directivity distribution with the applied phase and distance between element that must be applied to each radiating element without increase of complexity. In this approach the computation of the directivity is accomplished using three layer radial basis neural network and could be useful to predict directivity for given data set. The result obtained by this network is in excellent agreement with simulation results.
Index Terms—Neural network (NN), radial basis function neural network (RBFNN), array antenna, dipole antenna.
R. N. Yadav is working as associate professor with the Electronics and Communication Engineering Department, Maulana Azad National Institute of Technology, Bhopal, India. (e-mail: yadavrn@manit.ac.in).
S. C. Shrivastava was with Electronics and Communication Engineering Department, Maulana Azad National Institute of Technology, Bhopal, India. He is now with the T.I.T. Institute of Technology, Bhopal, India. (e-mail: scshrivastava@manit.ac.in).
Cite: Abhishek Rawat, R. N. Yadav, and S. C. Shrivastava, "Neural Modeling of Antenna Array Using Radial Basis Neural Network for Directivity prediction," International Journal of Modeling and Optimization vol. 3, no. 1, pp. 95-97, 2013.
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