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

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Abstract

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.

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Author information

Authors and Affiliations

  1. College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao, 266590, China

    Zidong Wang

  2. Department of Computer Science, Brunel University London, Uxbridge, Middlesex, UB8 3PH, UK

    Zidong Wang

  3. School of Information Science and Technology, Donghua University, Shanghai, 200051, China

    Hongjian Liu & Bo Shen

  4. Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah, Saudi Arabia

    Fuad E. Alsaadi & Abdullah M. Dobaie

Authors
  1. Zidong Wang
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  2. Hongjian Liu
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  3. Bo Shen
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  4. Fuad E. Alsaadi
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  5. Abdullah M. Dobaie
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Corresponding author

Correspondence to Zidong Wang.

Additional information

This project was funded by the Deanship of Scientific Research (DSR) at King Abdulaziz University, Jeddah, under Grant No. (RG-1-135-38). The authors, therefore, acknowledge with thanks DSR technical and financial support.

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Wang, Z., Liu, H., Shen, B. et al. \(H_{\infty }\) state estimation for discrete-time stochastic memristive BAM neural networks with mixed time-delays. Int. J. Mach. Learn. & Cyber. 10, 771–785 (2019). https://doi.org/10.1007/s13042-017-0769-2

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  • DOI: https://doi.org/10.1007/s13042-017-0769-2

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