Robust exponential stability of uncertain delayed neural networks with stochastic perturbation and impulse effects.

abstract

• This paper focuses on the hybrid effects of parameter uncertainty, stochastic perturbation, and impulses on global stability of delayed neural networks. By using the Ito formula, Lyapunov function, and Halanay inequality, we established several mean-square stability criteria from which we can estimate the feasible bounds of impulses, provided that parameter uncertainty and stochastic perturbations are well-constrained. Moreover, the present method can also be applied to general differential systems with stochastic perturbation and impulses.

authors

• Huang, Tingwen

published proceedings

• IEEE Trans Neural Netw Learn Syst

author list (cited authors)

• Huang, T., Li, C., Duan, S., & Starzyk, J. A.

citation count

• 305

complete list of authors

• Huang, Tingwen||Li, Chuandong||Duan, Shukai||Starzyk, Janusz A

publication date

• June 2012

publisher

• Institute of Electrical and Electronics Engineers (IEEE)  Publisher

published in

• IEEE Transactions on Neural Networks and Learning Systems  Journal

keywords

• Algorithms
• Computer Simulation
• Models, Statistical
• Neural Networks, Computer
• Nonlinear Dynamics
• Pattern Recognition, Automated
• Stochastic Processes

Digital Object Identifier (DOI)

• 10.1109/TNNLS.2012.2192135

start page

• 866

end page

• 875

volume

• 23

issue

• 6

URL

• http://dx.doi.org/10.1109/tnnls.2012.2192135