Filtering of Discrete-Time Switched Neural Networks Ensuring Exponential Dissipative and l2 - L Performances

Hyun Duck Choi, Choon Ki Ahn, Hamid Reza Karimi, Myo Taeg Lim

Research output: Contribution to journalArticlepeer-review

65 Citations (Scopus)


This paper studies delay-dependent exponential dissipative and l2 - l filtering problems for discrete-time switched neural networks (DSNNs) including time-delayed states. By introducing a novel discrete-time inequality, which is a discrete-time version of the continuous-time Wirtinger-type inequality, we establish new sets of linear matrix inequality (LMI) criteria such that discrete-time filtering error systems are exponentially stable with guaranteed performances in the exponential dissipative and l2 - l senses. The design of the desired exponential dissipative and l2 - l filters for DSNNs can be achieved by solving the proposed sets of LMI conditions. Via numerical simulation results, we show the validity of the desired discrete-time filter design approach.

Original languageEnglish
Article number7837591
Pages (from-to)3195-3207
Number of pages13
JournalIEEE Transactions on Cybernetics
Issue number10
Publication statusPublished - 2017 Oct


  • discrete Wirtinger-type inequality
  • discrete-time switched neural networks (DSNNs)
  • dissipative filtering
  • exponential stability
  • l-l filtering

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Information Systems
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering


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