\(H_{\infty }\) state estimation for discrete-time stochastic memristive BAM neural networks with mixed time-delays

  • Zidong Wang
  • Hongjian Liu
  • Bo Shen
  • Fuad E. Alsaadi
  • Abdullah M. Dobaie
Original Article


In this paper, the \(H_\infty\) state estimation problem is investigated for a class of discrete-time stochastic memristive bidirectional associative memory (DSMBAM) neural networks with mixed time delays. The mixed time delays comprise both discrete and distributed time-delays. A series of novel switching functions are proposed to reflect the state-dependent characteristics of the memristive connection weights in the discrete-time setting, which facilitates the dynamics analysis of the addressed memristive neural networks (MNNs). By means of the introduced series of switching functions, an \(H_\infty\) state estimator is designed such that the estimation error is exponentially mean-square stable and the prescribed \(H_\infty\) performance requirement is achieved. The gain matrices of the desired estimator are parameterized by utilizing the semi-definite programming method. Finally, a simulation example is employed to demonstrate the usefulness and effectiveness of the proposed theoretical results.


Discrete-time memristive neural networks BAM neural networks Mixed time delays \(H_\infty\) state estimation 


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2017

Authors and Affiliations

  1. 1.College of Electrical Engineering and AutomationShandong University of Science and TechnologyQingdaoChina
  2. 2.Department of Computer ScienceBrunel University LondonUxbridgeUK
  3. 3.School of Information Science and TechnologyDonghua UniversityShanghaiChina
  4. 4.Department of Electrical and Computer Engineering, Faculty of EngineeringKing Abdulaziz UniversityJeddahSaudi Arabia

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