Adaptive algorithms for first principal eigenvector computation
Neural Networks
| Hi-index | 35.68 |
We discuss two novel adaptive algorithms for generalized eigendecomposition that are derived from a two-layer linear feedforward hetero-associative neural network. In addition, we provide a rigorous convergence analysis of the adaptive algorithms by using stochastic approximation theory. Finally, we use these algorithms for on-line multiuser access interference cancellation in code-division-multiple-access-based cellular communications. Numerical simulations are reported to demonstrate their rapid convergence