Abstract
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.
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