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Proportional–integral-type estimator design for delayed recurrent neural networks under encoding–decoding mechanism

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  • Fan Yang
  • Jiahui Li
  • Hongli Dong
  • Yuxuan Shen

Abstract

In this paper, the proportional–integral-type estimator design problem is studied for recurrent neural networks under the encoding–decoding communication mechanism. In the process of the measurement data transmission, an encoding–decoding mechanism is introduced to improve the security of the network by encrypting the measurement data. The purpose of this paper is to design a proportional–integral-type estimation algorithm such that the estimation error dynamics is exponentially ultimately bounded in mean square. First, a sufficient condition is obtained for the existence of the desired estimator. Then, the parameters of the estimator are obtained by solving certain matrix inequality. Finally, a simulation example is given to verify the effectiveness of the designed estimation algorithm.

Suggested Citation

  • Fan Yang & Jiahui Li & Hongli Dong & Yuxuan Shen, 2022. "Proportional–integral-type estimator design for delayed recurrent neural networks under encoding–decoding mechanism," International Journal of Systems Science, Taylor & Francis Journals, vol. 53(13), pages 2729-2741, October.
    • Handle: RePEc:taf:tsysxx:v:53:y:2022:i:13:p:2729-2741
    DOI: 10.1080/00207721.2022.2063968
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