Robust beamforming by a globally convergent MCA neural network

Authors:
Mao Ye
Affiliations:
CI Lab, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
Venue:
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
Year:
2005

Citing 5
Cited 1

Original Contribution: Principal components, minor components, and linear neural networks

Neural Networks
Modified Hebbian learning for curve and surface fitting

Neural Networks
Letter: Global convergence analysis of a self-stabilizing MCA learning algorithm

Neurocomputing
Against the convergence of the minor component analysis neurons

IEEE Transactions on Neural Networks
The MCA EXIN neuron for the minor component analysis

IEEE Transactions on Neural Networks

A robust and globally convergent PCA learning algorithm

Control and Intelligent Systems

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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.