Modified Hebbian learning for curve and surface fitting
Neural Networks
Convergence analysis of the OJAn MCA learning algorithm by the deterministic discrete time method
Theoretical Computer Science
Development and analysis of a neural network approach toPisarenko's harmonic retrieval method
IEEE Transactions on Signal Processing
A minor subspace analysis algorithm
IEEE Transactions on Neural Networks
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
Neural network learning algorithms for tracking minor subspace in high-dimensional data stream
IEEE Transactions on Neural Networks
Convergence analysis of a deterministic discrete time system of Oja's PCA learning algorithm
IEEE Transactions on Neural Networks
Dynamics of Generalized PCA and MCA Learning Algorithms
IEEE Transactions on Neural Networks
A unified learning algorithm to extract principal and minor components
Digital Signal Processing
A Self-Stabilizing Neural Algorithm for Total Least Squares Filtering
Neural Processing Letters
On the discrete-time dynamics of a class of self-stabilizing MCA extraction algorithms
IEEE Transactions on Neural Networks
A self-stabilizing MSA algorithm in high-dimension data stream
Neural Networks
Hi-index | 0.09 |
Minor component analysis (MCA) is an important statistical tool for signal processing and data analysis. Neural networks can be used to extract online minor component from input data. Compared with traditional algebraic approaches, a neural network method has a lower computational complexity. Stability of neural networks learning algorithms is crucial to practical applications. In this paper, we propose a stable MCA neural networks learning algorithm, which has a more satisfactory numerical stability than some existing MCA algorithms. Dynamical behaviors of the proposed algorithm are analyzed via deterministic discrete time (DDT) method and the conditions are obtained to guarantee convergence. Simulations are carried out to illustrate the theoretical results achieved.