The number of sources estimation is one of the vital key technologies in smart antenna. The current paper adopts a new system that employs a hybrid algorithm of artificial bee colony (ABC) and complex generalized Hebbian (CGHA) neural network to Bayesian information criterion (BIC) technique, aiming to enhance the accuracy of number of sources estimation. The advantage of the new system is that no need to compute the covariance matrix, since its principal eigenvalues are computed using the CGHA neural network for the received signals. Moreover, the proposed system can optimize the training condition of the CGHA neural network, therefore it can overcome the random selection of initial weights and learning rate, which evades network oscillation and trapping into local solution. Simulation results of the offered system show good responses through reducing the required time to train the CGHA neural network, fast converge speed, effectiveness, in addition to achieving the correct number of sources.
Collaboration network of the authors
4 authors ·
5 co-authorship links
· 4 across different organisations
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University of Technology - Iraq
University of Baghdad
Mustansiriyah University
Different organisations
Same organisation
Affiliation unknown
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