University of Technology-IraqEngineering and Technology Journal1681-6900291320110928An Efficient Approach Combining Genetic Algorithm and Neural Networks for Eigen Value Grads Method (EGM) In Wireless Mobile Communications2590260032957ENMohammed Hussein MiryJournal Article20110901The objective of this paper is combining Genetic Algorithm and Principal
Component Analysis (PCA) neural network for Eigenvalue Grads Method (EGM)
to estimate the number of sources in wireless mobile communications. The
Eigenvalue Grads Method (EGM) is a popular method for estimation the number
of sources impinging on an array of sensors, which is a problem of great interest in
wireless mobile communications. This paper proposed a new system to estimate
the number of sources by applying the output of genetic algorithm and PCA neural
network with Complex Generalized Hebbian algorithm (CGHA) to EGM
technique. In the proposed model, the initial weight and learning rate values for
CGHA neural network can be selected automatically by using Genetic algorithm.
The result of computer simulation for proposed system showed good response by
fast converge speed for neural network , efficiency and yield the correct number of
the sources. The important feature of new system is that, the PCA of covariance
matrix are calculated based on CGHA neural network instead of determining the
covariance matrix because computation of covariance matrix is time consuming.https://etj.uotechnology.edu.iq/article_32957_098a6f44aef30fc4dd1cc07a07e2c19c.pdf