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Global Exponential Dissipativity of Impulsive Recurrent Neural Networks with Multi-proportional Delays

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Abstract

This paper addresses the global exponential dissipativity (GED) of impulsive recurrent neural networks (IRNNs) with proportional delays. By introducing some adjustable parameters, skillfully designing several Lyapunov functionals and utilizing matrix norm properties, serval delay-dependent GED criteria are developed, and global attractive sets (GAS) and global exponential attractive sets (GEAS) of the proposed system are given. These adjustable parameters are related to the exponential decay rate and contribute greatly to expand the attractive sets of this paper. Here the criteria proposed improve and extend the earlier global dissipativity criteria. Several numerical examples are used to verify the obtained results and show that the obtained results are independent of each other.

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Acknowledgements

This work is supported by the National Science Foundation of Tianjin (No. 18JCYBJC85800) and the Innovative Talents Cultivation of Young Middle Aged Backbone Teachers of Tianjin (No. 135205GC38).

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  1. School of Mathematics Science, Tianjin Normal University, Tianjin, 300387, China

    Liqun Zhou

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  1. Liqun Zhou
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Correspondence to Liqun Zhou.

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Zhou, L. Global Exponential Dissipativity of Impulsive Recurrent Neural Networks with Multi-proportional Delays. Neural Process Lett 53, 1435–1452 (2021). https://doi.org/10.1007/s11063-021-10451-8

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