Learning laws with exponential error convergence for recurrent neural networks
Elias B. Kosmatopoulos, M.A. Christdoulou, Pétros Ioannou · 2002
In this paper, we propose new learning laws for adjusting the weights of recurrent high order neural networks (RHONN) when they are used to system identification problems. The main advantages of these learning laws over the classical robust adaptive ones, is that the identification error converges to zero exponentially fast, and that such a convergence is independent of the number of high order connections of the RHONN.>