Dynamic Effects of Signal Normalizing on Adaptive Algorithms

A method is shown for suppressing the adaptive switching transient that arises in complex adaptive (LMS and stochastic approximation) algorithms due to signal normalization with controlled switched gains. The method involves rescaling of the adaptive weights with the normalizing gains just prior to the first adaptation with the new gains. In addition, an effective method of rescaling the adaptive gain factor based on the maximum eigenvalues of the respective covariance matrices is shown.