New Results on Robust Finite-Time Passivity for Fractional-Order Neural Networks with Uncertainties

Abstract

In this paper, the robust finite-time passivity for a class of fractional-order neural networks with uncertainties is considered. Firstly, the definition of finite-time passivity of fractional-order neural networks is introduced. Then, by using finite-time stability theory and linear matrix inequality approach, new sufficient conditions that ensure the finite-time passivity of the fractional-order neural network systems are derived via linear matrix inequalities which can be effectively solved by various computational tools. Finally, three numerical examples with simulation results are given to illustrate the effectiveness of the proposed method.

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Acknowledgements

The authors sincerely thank the anonymous reviewers for their constructive comments that helped improve the quality and presentation of this paper. This paper was revised when the authors were working as researchers at Vietnam Institute for Advance Study in Mathematics (VIASM). Authors would like to thank the VIASM for providing a fruitful research environment and extending support and hospitality during their visit.

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Correspondence to Mai Viet Thuan.

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Thuan, M.V., Huong, D.C. & Hong, D.T. New Results on Robust Finite-Time Passivity for Fractional-Order Neural Networks with Uncertainties. Neural Process Lett 50, 1065–1078 (2019). https://doi.org/10.1007/s11063-018-9902-9

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Keywords

  • Fractional order neural networks
  • Finite-time stability
  • Finite-time passivity
  • Linear matrix inequalities