Open AccessSingle Perceptron Model for Smart Beam forming in Array Antennas
Author(s) -
K.S. Senthilkumar,
K. Pirapaharan,
P.R.P. Hoole,
R.R.H Hoole
Publication year - 2016
Publication title -
international journal of electrical and computer engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.277
H-Index - 22
ISSN - 2088-8708
DOI - 10.11591/ijece.v6i5.pp2300-2309
Subject(s) - artificial neural network , computer science , beamforming , smart antenna , activation function , least mean squares filter , antenna (radio) , multilayer perceptron , perceptron , radiation pattern , antenna array , matlab , algorithm , dipole antenna , artificial intelligence , adaptive filter , telecommunications , operating system
In this paper, a single neuron neural network beamformer is proposed. A perceptron model is designed to optimize the complex weights of a dipole array antenna to steer the beam to desired directions. The objective is to reduce the complexity by using a single neuron neural network and utilize it for adaptive beamforming in array antennas. The selection of nonlinear activation function plays the pivotal role in optimization depends on whether the weights are real or complex. We have appropriately proposed two types of activation functions for respective real and complex weight values. The optimized radiation patterns obtained from the single neuron neural network are compared with the respective optimized radiation patterns from the traditional Least Mean Square (LMS) method. Matlab is used to optimize the weights in neural network and LMS method as well as display the radiation patterns.