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

88 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

Bibliographical note

Funding Information:
This work was supported in part by the National Research Foundation of Korea (NRF) through the Ministry of Science, ICT & Future Planning under Grant NRF-2014R1A1A1006101, in part by the Brain Korea 21 Plus Project in 2017, and in part by the Basic Science Research Program through the NRF funded by the Ministry of Education under Grant NRF-2016R1D1A1B01016071.

Publisher Copyright:
© 2013 IEEE.


  • 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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