Efficient Faulty Element Diagnostics of Large Antenna Arrays by Discrete Mean Field Neural Nets

A Survey on Applications of Neural Networks and Genetic Algorithms in Fault Diagnostics for Antenna Arrays

“…It has been found that the probability of finding the right solution increases with the number of samples. Authors in [22] have proposed use of a discrete asynchronous version of the Vidyasagar mean field neural network, to identify the faulty element in a large array and compared the performance of the method with the standard conjugate gradient method. A 25-element antenna array has been considered in the study.…”

“…By using ANNs it is possible to recalculate and produce the radiation pattern close to the original pattern [24][25] [26][29] [31][32]. The adaline [24], multilayer perceptron (MLP) [21][ [25][26][33]the radial basis function (RBF) neural networks [31][32]and the discrete mean field neural network based on Vidyasagar net [22] and have been found quite effective in finding faults in the antenna arrays. Genetic algorithms have also been very effectively used in fault diagnosis of arrays as mentioned ahead [20] [21][23] [30].…”

“…Information on the number and location of the faulty elements is required and these methods generally find it difficult to handle fault diagnosis. Hence new approaches are required [20][21][22][25] [26].Built in monitoring and calibration systems are complex and costly [33].The digital beamforming in smart antenna arrays provides possibility of failure correction and thus a cost-effective solution to hardware replacement [20].Artificial neural networks (ANNs) [10] [14] are non linear and use simple mathematical operators. They are able to locate faults by performing mapping between the damaged radiation pattern/s with the position of the fault element/s.…”

Large Phased Arrays Diagnostic via Distributional Approach

“…With these approaches one can determine the excitation coefficients of each radiating element and detects the failures by comparing the reconstructed ones with the nominal currents. Several deterministic and stochastic techniques have been developed [4][5][6][7][8][9] in the last years. Among the stochastic approaches, we point out the learning algorithms based on examples, such as neural networks [4,5], and the genetic algorithms based approaches [6,7].…”

“…Several deterministic and stochastic techniques have been developed [4][5][6][7][8][9] in the last years. Among the stochastic approaches, we point out the learning algorithms based on examples, such as neural networks [4,5], and the genetic algorithms based approaches [6,7]. These methods have the advantage to require small amount of samples of the radiated field and, in many cases, only amplitude data [7], but, due to the high size of search space, they can have poor performances.…”