An Efficient Approach Combining Genetic Algorithm and Neural Networks for Eigen Value Grads Method (EGM) In Wireless Mobile Communications

The 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

A Comparison of Three PCA Neural Techniques

Abstract. We present a comparison of three neural PCA techniques: the GHA by Sanger, the APEX by Kung and Diamataras, and the { APEX rst proposed by the present authors. Through numerical simulations and computational complexity evaluations we show the {APEX algorithms exhibit superior capability and interesting features. 1