Open AccessComplete Periodic Synchronization of Memristor-Based Neural Networks with Time-Varying Delays
Author(s) -
Huaiqin Wu,
Luying Zhang,
Sanbo Ding,
Xueqing Guo,
Lingling Wang
Publication year - 2013
Publication title -
discrete dynamics in nature and society
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.264
H-Index - 39
eISSN - 1607-887X
pISSN - 1026-0226
DOI - 10.1155/2013/140153
Subject(s) - memristor , synchronization (alternating current) , control theory (sociology) , artificial neural network , controller (irrigation) , coincidence , set (abstract data type) , computer science , state (computer science) , mathematics , topology (electrical circuits) , control (management) , algorithm , artificial intelligence , medicine , agronomy , alternative medicine , pathology , combinatorics , electrical engineering , biology , programming language , engineering
This paper investigates the complete periodic synchronization of memristor-based neural networks with time-varying delays. Firstly, under the framework of Filippov solutions, by using M-matrix theory and the Mawhin-like coincidence theorem in set-valued analysis, the existence of the periodic solution for the network system is proved. Secondly, complete periodic synchronization is considered for memristor-based neural networks. According to the state-dependent switching feature of the memristor, the error system is divided into four cases. Adaptive controller is designed such that the considered model can realize global asymptotical synchronization. Finally, an illustrative example is given to demonstrate the validity of the theoretical results
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