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Robust \(H_\infty\) filtering for uncertain discrete-time stochastic neural networks with Markovian jump and mixed time-delays

  • Yajun Li
  • Feiqi Deng
  • Gai Li
  • Like Jiao
Original Article
  • 97 Downloads

Abstract

In this paper, the robust \(H_\infty\) filtering problem is discussed for a class of uncertain discrete-time stochastic neural networks with Markovian jumping parameters and mixed time-delays. Norm-bounded parameter uncertainties exist in both the state and measurement equation. The neuron activation function satisfies sector-bounded condition. The aim is to design a full-order filter with a prescribed \(H_\infty\) performance level. Delay-segment-dependent conditions are developed in terms of linear matrix inequalities (LMIs) such that the resulted filtering error systems robustly stochastically stable. Finally, example is provided to demonstrate the effectiveness and applicability of the related results are obtained in this paper.

Keywords

Filter design Parameter uncertainty Discrete-time stochastic neural networks Markovian jumping parameter Mixed time-delay Linear matrix inequality (LMI) 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  1. 1.College of Electronics and Information EngineeringShunde PolytechnicFoshanChina
  2. 2.College of Automation Science and EngineeringSouth China University of TechnologyGuangzhouChina

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