Modified Hebbian learning for curve and surface fitting
Neural Networks
A modified MCA EXIN algorithm and its convergence analysis
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
Total least mean squares algorithm
IEEE Transactions on Signal Processing
Development and analysis of a neural network approach toPisarenko's harmonic retrieval method
IEEE Transactions on Signal Processing
Convergence analysis of a deterministic discrete time system of feng's MCA learning algorithm
IEEE Transactions on Signal Processing
Algorithms for accelerated convergence of adaptive PCA
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
Convergence analysis of a deterministic discrete time system of Oja's PCA learning algorithm
IEEE Transactions on Neural Networks
A stable MCA learning algorithm
Computers & Mathematics with Applications
A unified learning algorithm to extract principal and minor components
Digital Signal Processing
Hi-index | 5.23 |
Minor component analysis (MCA) is a statistical method of extracting the eigenvector associated with the smallest eigenvalue of the covariance matrix of input signals. Convergence is essential for MCA algorithms towards practical applications. Traditionally, the convergence of MCA algorithms is indirectly analyzed via their corresponding deterministic continuous time (DCT) systems. However, the DCT method requires the learning rate to approach zero, which is not reasonable in many applications due to the round-off limitation and tracking requirements. This paper studies the convergence of the deterministic discrete time (DDT) system associated with the OJAn MCA learning algorithm. Unlike the DCT method, the DDT method does not require the learning rate to approach zero. In this paper, some important convergence results are obtained for the OJAn MCA learning algorithm via the DDT method. Simulations are carried out to illustrate the theoretical results achieved.