Exponential input-to-state stability of recurrent neural networks with multiple time-varying delays.

abstract

• In this paper, input-to-state stability problems for a class of recurrent neural networks model with multiple time-varying delays are concerned with. By utilizing the Lyapunov-Krasovskii functional method and linear matrix inequalities techniques, some sufficient conditions ensuring the exponential input-to-state stability of delayed network systems are firstly obtained. Two numerical examples and its simulations are given to illustrate the efficiency of the derived results.

authors

• Huang, Tingwen

published proceedings

• Cogn Neurodyn

author list (cited authors)

• Yang, Z., Zhou, W., & Huang, T.

citation count

• 34

complete list of authors

• Yang, Zhichun||Zhou, Weisong||Huang, Tingwen

publication date

• February 2014

publisher

• Springer Nature  Publisher

published in

• Cognitive Neurodynamics  Journal

keywords

• Exponential Input-to-state Stability (exp-iss)
• Multiple Time-varying Delays
• Recurrent Neural Networks

PubMed Central ID

• 24465285

Digital Object Identifier (DOI)

• 10.1007/s11571-013-9258-9

start page

• 47

end page

• 54

volume

• 8

issue

• 1

URL

• http://dx.doi.org/10.1007/s11571-013-9258-9