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Finite-Time Stability of Neural Networks with Impulse Effects and Time-Varying Delay

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Abstract

In this paper, the impulsive effects on the finite-time stability of neural networks with time-varying delay are considered. Several novel criteria which govern the systems considered are finite-time stable are obtained by the idea of Lyapunov–Krasovskii functional and the average impulsive interval method. Moreover, the proposed sufficient conditions can be simplified into the form of linear matrix equalities which can be easily checked by Matlab LMI toolbox. The results proposed show that the model can achieve stable in finite time with stabilizing impulsive effects on one hand, and it can preserve the finite-time stability property in presence of destabilizing impulses on the other hand. Numerical examples are presented to demonstrate the effectiveness of the obtained results.

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Acknowledgements

This work was supported by Natural Science Foundation of China (Grant Nos. 61374078, 61633011), Chongqing Research Program of Basic Research and Frontier Technology (Nos. cstc2015jcyjBX0052, cstc2014jcyja40012), and the Science and Technology Foundation of the Education Department of Chongqing (KJ1501317, KJ1601310).

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Authors and Affiliations

  1. Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, College of Electronic and Information Engineering, Southwest University, Chongqing, 400715, China

    Jie Tan & Chuandong Li

  2. College of Mathematics and Physics, Chongqing University of Science and Technology, Chongqing, 401331, China

    Jie Tan

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  1. Jie Tan
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  2. Chuandong Li
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Correspondence to Chuandong Li.

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Tan, J., Li, C. Finite-Time Stability of Neural Networks with Impulse Effects and Time-Varying Delay. Neural Process Lett 46, 29–39 (2017). https://doi.org/10.1007/s11063-016-9570-6

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  • DOI: https://doi.org/10.1007/s11063-016-9570-6

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