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Delay-Dependent Criteria for Global Exponential Stability of Time-Varying Delayed Fuzzy Inertial Neural Networks

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Abstract

This paper is mainly concerned with global exponential stability of time-varying delayed fuzzy inertial neural networks. Different from previous approaches of variable transformation, we use non-reduced order method. Different from previous non-reduced order method used to investigate the inertial neural networks without time-varying delays, we take the time-varying delayed effects into account. By constructing a modified delay-dependent Lyapunov functional and inequality technique, delay-dependent criteria stated with simple algebraic inequalities are given in order to ensure the global exponential stability for the addressed delayed fuzzy inertial neural network model. The approach applied can provide a new method to study the fuzzy inertial neural networks with time delays via non-reduced order method. Some previous works in the literature are extend and complement. Finally, numerical examples with simulations are presented to make comparisons between the system with delays and without delays, and further demonstrate the validity and originality of the proposed approach.

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Acknowledgements

The authors thank the anonymous reviewers for their insightful suggestions which improved this work significantly. This work is jointly supported by the National Natural Science Foundation of China (No. 12001011), Anhui Provincial Natural Science Foundation (No. 2008085QA14) and the Talent Foundation of Anhui Normal University (No. 751965).

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Authors and Affiliations

  1. School of Mathematics and Statistics, Anhui Normal University, Wuhu, 241000, Anhui, China

    Dengdi Chen & Fanchao Kong

  2. MOE-LCSM, School of Mathematics and Statistics, Hunan Normal University, Changsha, 410081, Hunan, China

    Fanchao Kong

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  1. Dengdi Chen
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  2. Fanchao Kong
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Correspondence to Fanchao Kong.

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Chen, D., Kong, F. Delay-Dependent Criteria for Global Exponential Stability of Time-Varying Delayed Fuzzy Inertial Neural Networks. Neural Process Lett 53, 49–68 (2021). https://doi.org/10.1007/s11063-020-10382-w

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