Title :
Learning laws with exponential error convergence for recurrent neural networks
Author :
Kosmatopoulos, Elias B. ; Christdoulou, M.A. ; Ioannou, Peters A.
Author_Institution :
Dept. of Electron. & Comput. Eng., Tech. Univ. of Crete, Chania, Greece
Abstract :
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
Keywords :
convergence of numerical methods; error analysis; identification; learning (artificial intelligence); nonlinear systems; recurrent neural nets; exponential error convergence; learning laws; nonlinear systems; recurrent neural networks; system identification; Adaptive algorithm; Adaptive control; Convergence; Lyapunov method; Neural networks; Neurons; Parameter estimation; Programmable control; Recurrent neural networks; Robustness;
Conference_Titel :
Decision and Control, 1993., Proceedings of the 32nd IEEE Conference on
Conference_Location :
San Antonio, TX
Print_ISBN :
0-7803-1298-8
DOI :
10.1109/CDC.1993.325707
