Mean square delay dependent-probability-distribution stability analysis of neutral type stochastic neural networks.
ISA Trans
; 58: 11-9, 2015 Sep.
Article
em En
| MEDLINE
| ID: mdl-25862099
ABSTRACT
The aim of this manuscript is to investigate the mean square delay dependent-probability-distribution stability analysis of neutral type stochastic neural networks with time-delays. The time-delays are assumed to be interval time-varying and randomly occurring. Based on the new Lyapunov-Krasovskii functional and stochastic analysis approach, a novel sufficient condition is obtained in the form of linear matrix inequality such that the delayed stochastic neural networks are globally robustly asymptotically stable in the mean-square sense for all admissible uncertainties. Finally, the derived theoretical results are validated through numerical examples in which maximum allowable upper bounds are calculated for different lower bounds of time-delay.
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1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Redes Neurais de Computação
Idioma:
En
Revista:
ISA Trans
Ano de publicação:
2015
Tipo de documento:
Article