Learning in a partially hard-wired recurrent network / 91-0114
Chi-Bang Kuan, Kurt Hornik · Illinois Digital Environment for Access to Learning and Scholarship (University of Illinois at Urbana-Champaign) · 1991
In this paper we propose a partially hard-wired Elman network.A distinct feature of our approach is that only minor modifications of existing on-line and off-line learning algorithms are necessary in order to implement the proposed network.This allows researchers to adapt easily to trainable recurrent networks.Given this network architecture, we show that in a general dynamic environment the standard back-propagation estimates for the learnable connection weights can converge to a mean square error minimizer with probability one and are asymptotically normally distributed.• \