Fault Tolerant Neural Network for ECG Signal Classification Systems

Autor:	MERAH, M., OUAMRI, A., NAIT-ALI, A., KECHE, M.
Jazyk:	angličtina
Rok vydání:	2011
Předmět:	fault tolerant artificial neural networks hybrid backpropagation algorithms medical diagnosis Electrical engineering. Electronics. Nuclear engineering TK1-9971 Computer engineering. Computer hardware TK7885-7895
Zdroj:	Advances in Electrical and Computer Engineering, Vol 11, Iss 3, Pp 17-24 (2011)
Druh dokumentu:	article
ISSN:	1582-7445 1844-7600
DOI:	10.4316/AECE.2011.03003
Popis:	The aim of this paper is to apply a new robust hardware Artificial Neural Network (ANN) for ECG classification systems. This ANN includes a penalization criterion which makes the performances in terms of robustness. Specifically, in this method, the ANN weights are normalized using the auto-prune method. Simulations performed on the MIT ? BIH ECG signals, have shown that significant robustness improvements are obtained regarding potential hardware artificial neuron failures. Moreover, we show that the proposed design achieves better generalization performances, compared to the standard back-propagation algorithm.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/18555b6f47fc4da192d7601a0900f49f Zobrazit plný text záznamu View record in DOAJ