Finite-time robust stochastic stability of uncertain stochastic delayed reaction-diffusion genetic regulatory networks

Authors:
Jianping Zhou;Shengyuan Xu;Hao Shen
Affiliations:
School of Automation, Nanjing University of Science and Technology, Nanjing 210094, PR China;School of Automation, Nanjing University of Science and Technology, Nanjing 210094, PR China;School of Automation, Nanjing University of Science and Technology, Nanjing 210094, PR China
Venue:
Neurocomputing
Year:
2011

Citing 9
Cited 1

Robust control of a class of uncertain nonlinear systems

Systems & Control Letters
Noise-induced cooperative behavior in a multicell system

Bioinformatics
Asymptotic and robust stability of genetic regulatory networks with time-varying delays

Neurocomputing
Technical communique: Finite-time stability of linear time-varying systems with jumps

Automatica (Journal of IFAC)
Delay-dependent stability of neutral type neural networks with distributed delays

Neurocomputing
Stochastic stability for uncertain genetic regulatory networks with interval time-varying delays

Neurocomputing
Exponential stability of genetic regulatory networks with random delays

Neurocomputing
Robust state estimation for stochastic genetic regulatory networks

International Journal of Systems Science - Dynamics Analysis of Gene Regulatory Networks
On the transient and steady-state estimates of interval genetic regulatory networks

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

Letters: Finite-time boundedness of uncertain time-delayed neural network with Markovian jumping parameters

Neurocomputing

Quantified Score

Hi-index	0.01

Visualization

Abstract

This paper is concerned with the problem of finite-time stability analysis for uncertain stochastic delayed reaction-diffusion genetic regulatory networks. The parameter uncertainties are assumed to be norm-bounded, and the time delays are assumed to be time-varying. Based on the Lyapunov functional method, sufficient conditions ensuring the networks to be finite-time robustly stochastically stable are established. When there are no norm-bounded parameter uncertainties in the networks, a finite-time stochastic stability condition is also established. All the conditions are diffusion-dependent as well as delay-dependent. Numerical examples are given to illustrate the effectiveness of the proposed results.