Volume 43 Issue 8
Aug.  2022
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LIU Feiyang, LI Bing. Event-Based State Estimation of Complex-Valued Neural Networks With Mixed Delays[J]. Applied Mathematics and Mechanics, 2022, 43(8): 911-919. doi: 10.21656/1000-0887.420359
Citation: LIU Feiyang, LI Bing. Event-Based State Estimation of Complex-Valued Neural Networks With Mixed Delays[J]. Applied Mathematics and Mechanics, 2022, 43(8): 911-919. doi: 10.21656/1000-0887.420359

Event-Based State Estimation of Complex-Valued Neural Networks With Mixed Delays

doi: 10.21656/1000-0887.420359
  • Received Date: 2021-11-25
  • Rev Recd Date: 2022-01-06
  • Available Online: 2022-07-06
  • Publish Date: 2022-08-01
  • The event-based state estimation problem was investigated for a class of complex-valued neural networks with mixed delays. Based on the measurement output, a novel event-triggering scheme was introduced to reduce the frequency of updating while ensuring the estimation performance. A waiting time was first employed to avoid the Zeno phenomenon. By means of the Lyapunov direct method and some properties of complex-valued matrices, a sufficient criterion was established to guarantee the globally asymptotic stability for the error system. The weighted parameters and gain matrices were designed with resort to the feasible solution of matrix inequalities. A numerical simulation example illustrates the effectiveness of the proposed method.

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