Paper Info
Title
Convergence and attractivity of memristor-based cellular neural networks with time delays.
Abstract
This paper presents theoretical results on the convergence and attractivity of memristor-based cellular neural networks (MCNNs) with time delays. Based on a realistic memristor model, an MCNN is modeled using a differential inclusion. The essential boundedness of its global solutions is proven. The state of MCNNs is further proven to be convergent to a critical-point set located in saturated region of the activation function, when the initial state locates in a saturated region. It is shown that the state convergence time period is finite and can be quantitatively estimated using given parameters. Furthermore, the positive invariance and attractivity of state in non-saturated regions are also proven. The simulation results of several numerical examples are provided to substantiate the results.
Year
DOI
Venue
2015
10.1016/j.neunet.2014.12.002
Neural Networks
Keywords
Field
DocType
Memristor,Cellular neural networks,Finite-time convergence,Positive invariance,Attractivity
Differential inclusion,Convergence (routing),Mathematical optimization,Memristor,Invariant (physics),Activation function,Cellular neural network,Mathematics
Journal
Volume
Issue
ISSN
63
1
0893-6080
Citations 
PageRank 
References 
15
0.55
28
Authors
3
Name
Order
Citations
PageRank
Sitian Qin124423.00
Jun Wang29228736.82
Xiaoping Xue318617.00