An Integrated Kernel PCA Neural Network and EGM for Number of Sources Estimation in Wireless Communication

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Mohammed Hussein Miry, Ali  Hussien Miry


The present work argues estimating number of sources in communication system using an integrated model of Principal Component Analysis (PCA) neural network and kernel method to produce Eigenvalue Grads Method (EGM). The essential advantage of this new suggested model is that, PCA neural is used to determine the covariance matrix   instead of the traditional computation process which is time consuming. Simulation outcomes of this adopted model demonstrate wonderful responses through effectiveness, fast converge speed for (PCA) neural network, as well as achieving correct number of sources.

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How to Cite
Mohammed Hussein Miry, et al. (2023). An Integrated Kernel PCA Neural Network and EGM for Number of Sources Estimation in Wireless Communication. International Journal on Recent and Innovation Trends in Computing and Communication, 11(10), 144–148.
Author Biography

Mohammed Hussein Miry, Ali  Hussien Miry

Mohammed Hussein Miry1, Ali  Hussien Miry2

1Assistant Professor, Department of Communication Engineering

University of Technology- Iraq

Bagdad, Iraq

2Professor, Department of Mechatronics Engineering

University of Bagdad- Iraq

Bagdad, Iraq


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