Synchronization of memristor-based recurrent neural networks with two delay components based on second-order reciprocally convex approach.
Neural Netw
; 57: 79-93, 2014 Sep.
Article
in En
| MEDLINE
| ID: mdl-24953308
ABSTRACT
We extend the notion of Synchronization of memristor-based recurrent neural networks with two delay components based on second-order reciprocally convex approach. Some sufficient conditions are obtained to guarantee the synchronization of the memristor-based recurrent neural networks via delay-dependent output feedback controller in terms of linear matrix inequalities (LMIs). The activation functions are assumed to be of further common descriptions, which take a broad view and recover many of those existing methods. A Lyapunov-Krasovskii functional (LKF) with triple-integral terms is addressed in this paper to condense conservatism in the synchronization of systems with additive time-varying delays. Jensen's inequality is applied in partitioning the double integral terms in the derivation of LMIs and then a new kind of linear combination of positive functions weighted by the inverses of squared convex parameters has emerged. Meanwhile, this paper puts forward a well-organized method to manipulate such a combination by extending the lower bound lemma. The obtained conditions not only have less conservatism but also less decision variables than existing results. Finally, numerical results and its simulations are given to show the effectiveness of the proposed memristor-based synchronization control scheme.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Neural Networks, Computer
Type of study:
Prognostic_studies
Language:
En
Journal:
Neural Netw
Journal subject:
NEUROLOGIA
Year:
2014
Document type:
Article