Treffer: Global exponential stability of a class of impulsive neural networks with unstable continuous and discrete dynamics

Title:
Global exponential stability of a class of impulsive neural networks with unstable continuous and discrete dynamics
Authors:
LINNA WEI, CHEN, Wu-Hua
Source:
Neurocomputing (Amsterdam). 147:225-234
Publisher Information:
Amsterdam: Elsevier, 2015.
Publication Year:
2015
Physical Description:
print, 35 ref
Original Material:
INIST-CNRS
Subject Terms:
Cognition, Computer science, Informatique, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Logiciel, Software, Systèmes informatiques et systèmes répartis. Interface utilisateur, Computer systems and distributed systems. User interface, Automatique théorique. Systèmes, Control theory. Systems, Modélisation et identification, Modelling and identification, Analyse des systèmes de commande, Control system analysis, Analyse séquentielle, Sequential analysis, Análisis secuencial, Commande impulsionnelle, Impulse control, Control impulsional, Critère stabilité, Stability criterion, Criterio estabilidad, Fonction Lyapunov, Lyapunov function, Función Lyapunov, Fonction poids, Weight function, Función peso, Inégalité matricielle linéaire, Linear matrix inequality, Desigualdad matricial lineal, Modèle ordre réduit, Reduced order model, Modelo orden reducido, Réponse impulsion, Pulse response, Respuesta impulsión, Réseau neuronal, Neural network, Red neuronal, Stabilisation globale, Global stabilization, Estabilización global, Stabilité exponentielle, Exponential stability, Estabilidad exponencial, Système paramètre variable, Time varying system, Sistema parámetro variable, Impulsive neural networks, Reduced-order impulsive control, Time-varying Lyapunov function, Unstable dynamics
Document Type:
Fachzeitschrift Article
File Description:
text
Language:
English
Author Affiliations:
School of Mathematics, South China University of Technology, Guangzhou 510641, Guangdong, China
College of Mathematics and Information Science, Guangxi University, Nanning 530004, Guangxi, China
ISSN:
0925-2312
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=28836745
Rights:
Copyright 2015 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems
Accession Number:
edscal.28836745
Database:
PASCAL Archive

Weitere Informationen

This paper deals with global exponential stability of a class of impulsive neural networks whose continuous and discrete dynamics are unstable. Assuming that the impulsive neural network under consideration can be decomposed into two lower order impulsive systems, a time-varying weighted Lyapunov function associated with the impulse time sequence is introduced for stability analysis. A novel global exponential stability criterion is derived in terms of linear matrix inequalities (LMIs). By employing the newly obtained stability criterion, a sufficient condition on the existence of a reduced-order impulsive controller is derived. Unlike the previous results concerning impulsive control, the proposed reduced-order impulsive controller only exerts the impulses on a partial set of the state vector. Moreover, the controller gain matrices can be achieved by solving a set of LMIs. Finally, four illustrative examples are given to show the effectiveness of the developed techniques and results.