Abstract
Minor component analysis (MCA) by neural networks approach has attracted many attentions in the field of neural networks. Convergent learning algorithms of MCA neural networks are very important and useful for applications. In this paper, a globally convergent learning algorithm for MCA neural network is reviewed. Rigorous mathematical proof of global convergence is given and exponential convergence rate is obtained. Comparison experiments illustrate that this algorithm has good performance on beamforming problem.
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