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\begin{document}

\title{\$H\_\textbackslash{}infty\$ State Estimation of Delayed Recurrent
Memristive Neural Networks: continuous-time case and discrete-time case}



\author[1]{fangyuan Ma}%
\author[1]{Xingbao Gao}%
\affil[1]{Shaanxi Normal University}%


\vspace{-1em}



  
  \date{May 26, 2024}


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\begin{abstract}
This paper investigates the problem of \$H\_\textbackslash{}infty\$
state estimation of delayed recurrent memristive neural networks
(DRMNNs) with both continuous-time and discrete-time cases. By utilizing
Lyapunov-Krasovskii functional (LKF) and linear matrix inequalities
(LMIs), two criterions are provided to guarantee the asymptotically
stable of the estimation error systems with a
\$H\_\textbackslash{}infty\$ performance. The connection weight
parameters of DRMNNs are dealed with logical switching signals, which
greatly reduces the computational complexity. The given conditions can
be easily checked by solving LMIs, the obtained theoretical results are
supported demonstrated by two numerical examples.%
\end{abstract}%



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