Memory in Backpropagation-Decorrelation O(N) Efficient Online Recurrent Learning

摘要

We consider regularization methods to improve the recently introduced backpropagation-decorrelation (BPDC) online algorithm for O(N) training of fully recurrent networks. While BPDC combines one-step error backpropa-gation and the usage of temporal memory of a network dynamics by means of decorrelation of activations, it is an online algorithm using only instantaneous states and errors. As enhancement we propose several ways to introduce memory in the algorithm for regularization. Simulation results of standard tasks show that different such strategies cause different effects either improving training performance at the cost of overfitting or degrading training errors.