Robust beamforming by a globally convergent MCA neural network
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
Conjugate gradient eigenstructure tracking for adaptive spectralestimation
IEEE Transactions on Signal Processing
Against the convergence of the minor component analysis neurons
IEEE Transactions on Neural Networks
Optimal linear compression under unreliable representation and robust PCA neural models
IEEE Transactions on Neural Networks
Robust recursive least squares learning algorithm for principal component analysis
IEEE Transactions on Neural Networks
Algorithms for accelerated convergence of adaptive PCA
IEEE Transactions on Neural Networks
A class of learning algorithms for principal component analysis and minor component analysis
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
The MCA EXIN neuron for the minor component analysis
IEEE Transactions on Neural Networks
On the discrete-time dynamics of the basic Hebbian neural network node
IEEE Transactions on Neural Networks
A new simple ∞OH neuron model as a biologically plausible principal component analyzer
IEEE Transactions on Neural Networks
Principal component extraction using recursive least squares learning
IEEE Transactions on Neural Networks
Energy function for the one-unit Oja algorithm
IEEE Transactions on Neural Networks
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Principal component analysis (PCA) using neural networks is an active research field with many applications to signal processing and data analysis. This paper presents a PCA neural network endowed with a novel learning algorithm, and an analysis of its features. As the basic discrete-time Oja's PCA neural network does not converge globally, it is important to derive a robust and globally convergent PCA learning algorithm. Based on previous works on the globally convergent PCA learning algorithm, a robust and globally convergent PCA learning algorithm is proposed in this paper. The behavior of this discrete-time learning algorithm is directly studied in this paper. We show that the algorithm is robust and globally convergent. The selection of the parameters of this algorithm will also be discussed in details. Finally, simulation results are provided to verify the theoretical results presented. Compared to other PCA learning algorithms, the proposed algorithm performs favorably in terms of robust stability, global convergence, speed and accuracy.