Error-minimizing dead zone for basis function networks

Abstract

The incorporation of dead zones in the error signal of basis function networks avoids the networks' overtraining and guarantees the convergence of the normalized least mean square (LMS) algorithm and related algorithms. A new so-called error-minimizing dead zone is presented providing the least a posteriori error out of the set of all convergence assuring dead zones. A general convergence proof is developed for LMS algorithms with dead zones, and the error-minimizing dead zone is derived from the resulting convergence condition. The performance is compared with the performance of classical dead zones.