Modified Hebbian learning for curve and surface fitting
Neural Networks
Neural Networks for Optimization and Signal Processing
Neural Networks for Optimization and Signal Processing
Applied Neural Networks for Signal Processing
Applied Neural Networks for Signal Processing
A stable MCA learning algorithm
Computers & Mathematics with Applications
Total least mean squares algorithm
IEEE Transactions on Signal Processing
On the discrete time dynamics of a self-stabilizing MCA learning algorithm
Mathematical and Computer Modelling: An International Journal
Against the convergence of the minor component analysis neurons
IEEE Transactions on Neural Networks
A class of learning algorithms for principal component analysis and minor component analysis
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
A Class of Self-Stabilizing MCA Learning Algorithms
IEEE Transactions on Neural Networks
Dynamics of Generalized PCA and MCA Learning Algorithms
IEEE Transactions on Neural Networks
| Hi-index | 0.00 |
A neural approach for solving the total least square (TLS) problem is presented in the paper. It is based on a linear neuron with a self-stabilizing neural algorithm, capable of resolving the TLS problem present in the parameter estimation of an adaptive FIR filters for system identification, where noisy errors affect not only the observation vector but also the data matrix. The learning rule is analyzed mathematically and the condition to guarantee the stability of algorithm is educed. The computer simulations are given to illustrate that the neural approach is self-stabilizing and considerably outperforms the existing TLS methods when a larger learning factor is used or the signal-noise-rate is lower.