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The Sampled-data Exponential Stability of BAM with Distributed Leakage Delays

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Abstract

This paper investigates the exponential stability of bidirectional associative memory (BAM) neural networks with distributed leakage delays and sampled-data state feedback input. Based on the input delay approach, the considered BAM neural networks is transformed into a system with mixed distributed leakage delays and time-varying discrete delays. Some sufficient conditions are given to ensure that the system is exponentially stable. Finally, a numerical example is provided to demonstrate the effectiveness of the theoretical analysis.

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Authors and Affiliations

  1. Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), School of Science, Jiangnan University, Wuxi, 214122, People’s Republic of China

    Li Li, Yongqing Yang & Fei Wang

Authors
  1. Li Li
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  2. Yongqing Yang
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  3. Fei Wang
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Corresponding author

Correspondence to Li Li.

Additional information

This work was jointly supported by the Foundation of Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, People’s Republic of China and the Fundamental Research Funds for the Central Universities (JUSRP51317B, JUDCF13042).

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Li, L., Yang, Y. & Wang, F. The Sampled-data Exponential Stability of BAM with Distributed Leakage Delays. Neural Process Lett 46, 537–547 (2017). https://doi.org/10.1007/s11063-016-9576-0

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