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$H_\infty$ State Estimation of Delayed Recurrent Memristive Neural Networks: continuous-time case and discrete-time case
  • fangyuan Ma,
  • Xingbao Gao
fangyuan Ma
Shaanxi Normal University
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Xingbao Gao
Shaanxi Normal University
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Abstract

This paper investigates the problem of $H_\infty$ 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 $H_\infty$ 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.

Peer review status:UNDER REVIEW

07 Mar 2021Submitted to Mathematical Methods in the Applied Sciences
08 Mar 2021Assigned to Editor
08 Mar 2021Submission Checks Completed
14 Mar 2021Reviewer(s) Assigned
05 Apr 2021Review(s) Completed, Editorial Evaluation Pending