Delay-dependent H∞ and generalized H2 filtering for delayed neural networks

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Original languageEnglish
Pages (from-to)846-857
Journal / PublicationIEEE Transactions on Circuits and Systems I: Regular Papers
Issue number4
Online published31 Oct 2008
Publication statusPublished - Apr 2009


This paper focuses on studying the H and generalized H2 filtering problems for a class of delayed neural networks. The time-varying delay is only required to be continuous and bounded. Delay-dependent criteria are proposed such that the resulting filtering error system is globally exponentially stable with a guaranteed H or generalized H2 performance. It is also shown that the designs of the desired filters are achieved by solving a set of linear matrix inequalities, which can be facilitated efficiently by resorting to standard numerical algorithms. It should be noted that, based on a novel bounding technique, several slack variables are introduced to reduce the conservatism of the derived conditions. Three examples with simulation results are provided to illustrate the effectiveness and performance of the developed approaches. 

Research Area(s)

  • Delay-dependent criteria, Filter design, Global exponential stability, Linear matrix inequality (LMI), Neural networks, Time-varying delay