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Improved Summation Inequality Based State Estimation for Stochastic Semi-Markovian Jumping Discrete-Time Neural Networks with Mixed Delays and Quantization

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Abstract

The problem of estimator design for stochastic discrete-time semi-Markov jump neural networks (NNs) with both quantization and mixed time delays is addressed. The asymptotic stability criteria are acquired by setting up an appropriate Lyapunov functional using the summation inequalities in both single and double forms for the semi-Markov jump networks. Making use of Lyapunov functional technique, the explicit expressions for the gain are proposed. Eventually, two examples are exploited numerically to exemplify the usefulness of the new methodology.

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Authors and Affiliations

  1. School of Cyber Science and Engineering, Southeast University, Nanjing, 211189, China

    Yang Cao

  2. Department of Mathematics, Kumaraguru College of Technology, Coimbatore, Tamilnadu, 641 049, India

    K. Maheswari & S. Dharani

Authors
  1. Yang Cao
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  2. K. Maheswari
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  3. S. Dharani
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Correspondence to Yang Cao.

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This work was supported by the National Natural Science Foundation of China under Grant No. 62103103, and the Natural Science Foundation of Jiangsu Province of China under Grant No. BK20210223.

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Cao, Y., Maheswari, K. & Dharani, S. Improved Summation Inequality Based State Estimation for Stochastic Semi-Markovian Jumping Discrete-Time Neural Networks with Mixed Delays and Quantization. Neural Process Lett 55, 1919–1935 (2023). https://doi.org/10.1007/s11063-022-10969-5

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