Biomedical subjects

Debin Huang

Publications and source records attributed to Debin Huang.

6 recordsLinked to original sources

PubMed2006

Synchronization in adaptive weighted networks.

In this paper, global synchronization in coupled oscillator networks is investigated. We propose an adaptive weighted network and show that such a simple and quite general scheme is able to tip oscillator networks towards collective synchronization. In comparison with the results based on linear stability analysis of unweighted networks, the proposed scheme improves the synchronizability of network dynamics, and is beneficial to analyze the effect of network structure on synchronizability.

Journal Article↗

PubMed2006

Adaptive-feedback control algorithm.

This paper is motivated by giving the detailed proofs and some interesting remarks on the results the author obtained in a series of papers [Phys. Rev. Lett. 93, 214101 (2004); Phys. Rev. E 71, 037203 (2005); 69, 067201 (2004)], where an adaptive-feedback algorithm was proposed to effectively stabilize and synchronize chaotic systems. This note proves in detail the strictness of this algorithm from the viewpoint of mathematics, and gives some interesting remarks for its potential applications to chaos control & synchronization. In addition, a significant comment on synchronization-based parameter estimation is given, which shows some techniques proposed in literature less strict and ineffective in some cases.

Journal Article↗

PubMed2005

Simple adaptive-feedback controller for identical chaos synchronization.

Based on the invariance principle of differential equations, a simple adaptive-feedback scheme is proposed to strictly synchronize almost all chaotic systems. Unlike the usual linear feedback, the variable feedback strength is automatically adapted to completely synchronize two almost arbitrary identical chaotic systems, so this scheme is analytical, and simple to implement in practice. Moreover, it is quite robust against the effect of noise. The famous Lorenz and Rössler hyperchaos systems are used as illustrative examples.

Journal Article↗

PubMed2004

Stabilizing near-nonhyperbolic chaotic systems with applications.

Based on the invariance principle of differential equations a simple, systematic, and rigorous feedback scheme with the variable feedback strength is proposed to stabilize nonlinearly finite-dimensional chaotic systems without any prior analytical knowledge of the systems. Especially the method may be used to control near-nonhyperbolic chaotic systems, which, although arising naturally from models in astrophysics to those for neurobiology, all Ott-Grebogi-York type methods will fail to stabilize. The technique is successfully used for the famous Hindmarsh-Rose neuron model, the FitzHugh-Rinzel neuron model, and the Rössler hyperchaos system, respectively.

Journal Article↗

PubMed2004

Synchronization-based estimation of all parameters of chaotic systems from time series.

By a simple combination of adaptive scheme and linear feedback with the updated feedback strength, for a large class of chaotic systems it is proved rigorously by using the invariance principle of differential equations that all unknown model parameters can be estimated dynamically. This approach supplies a systematic and analytical procedure for estimating parameters from time series, and it is simple to implement in practice. In addition, this method is quite robust against the effect of noise and able to respond rapidly to changes in operating parameters of the experimental system. Lorenz and Rössler hyperchaos systems are used to illustrate the validity of this technique.

Journal Article↗

PubMed2004

Identifying parameter by identical synchronization between different systems.

In this paper, parameters of a given (chaotic) dynamical system are estimated from time series by using identical synchronization between two different systems. This technique is based on the invariance principle of differential equations, i.e., a dynamical Lyapunov function involving synchronization error and the estimation error of parameters. The control used in this synchronization consists of feedback and adaptive control loop associated with the update law of estimation parameters. Our estimation process indicates that one may identify dynamically all unknown parameters of a given (chaotic) system as long as time series of the system are available. Lorenz and Rossler systems are used to illustrate the validity of this technique. The corresponding numerical results and analysis on the effect of noise are also given.

Algorithms↗