In this paper, based on the invariant principle of functional differential equations, a simple, analytical, and rigorous adaptive feedback scheme is proposed for the synchronization of almost all kinds of coupled identical neural networks with time-varying delay, which can be chaotic, periodic, etc. We do not assume that the concrete values of the connection weight matrix and the delayed connection weight matrix are known. We show that two coupled identical neural networks with or without time-varying delay can achieve synchronization by enhancing the coupling strength dynamically. The update gain of coupling strength can be properly chosen to adjust the speed of achieving synchronization. Also, it is quite robust against the effect of noise and simple to implement in practice. In addition, numerical simulations are given to show the effectiveness of the proposed synchronization method.
Skip Nav Destination
,
Article navigation
March 2006
Research Article|
March 30 2006
Adaptive synchronization of neural networks with or without time-varying delay Available to Purchase
Jinde Cao;
Jinde Cao
a)
Department of Mathematics,
Southeast University
, Nanjing 210096, China
Search for other works by this author on:
Jianquan Lu
Jianquan Lu
Department of Mathematics,
Southeast University
, Nanjing 210096, China
Search for other works by this author on:
Jinde Cao
a)
Jianquan Lu
Department of Mathematics,
Southeast University
, Nanjing 210096, Chinaa)
Electronic mail: [email protected]
Chaos 16, 013133 (2006)
Article history
Received:
October 04 2005
Accepted:
January 30 2006
Citation
Jinde Cao, Jianquan Lu; Adaptive synchronization of neural networks with or without time-varying delay. Chaos 1 March 2006; 16 (1): 013133. https://doi.org/10.1063/1.2178448
Download citation file:
Pay-Per-View Access
$40.00
Sign In
You could not be signed in. Please check your credentials and make sure you have an active account and try again.
Citing articles via
Discovery of interpretable structural model errors by combining Bayesian sparse regression and data assimilation: A chaotic Kuramoto–Sivashinsky test case
Rambod Mojgani, Ashesh Chattopadhyay, et al.
Recent achievements in nonlinear dynamics, synchronization, and networks
Dibakar Ghosh, Norbert Marwan, et al.
Reservoir computing with the minimum description length principle
Antony Mizzi, Michael Small, et al.
Related Content
Adaptive complete synchronization of two identical or different chaotic (hyperchaotic) systems with fully unknown parameters
Chaos (November 2005)
Adaptive Q-S (lag, anticipated, and complete) time-varying synchronization and parameters identification of uncertain delayed neural networks
Chaos (June 2006)
Delay-induced stochastic bifurcations in a bistable system under white noise
Chaos (August 2015)