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Sampled-data state estimation for delayed neural networks with discontinuous activations

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Abstract

This paper firstly treats of the sampled-data state estimation issue for the dynamic neural networks with mixed time-delays and discontinuous activation. Based on the theory of differential inclusion and non-smooth analysis, several criteria are presented to guarantee the existence of the desired state sampled-data estimator for the discontinuous neural networks by solving some linear matrix equalities. The obtained results are also applicable to neural networks with continuous activations since they are a special cases of neural networks with discontinuous activations. Results of this paper improve a few previous known results. Finally, two numerical simulations are given to illustrate the effectiveness of the proposed methods.

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Authors and Affiliations

  1. School of Science, Hebei University of Engineering, Handan, 056038, China

    Haitao Wang

  2. School of Science, Beijing Jiaotong University, Beijing, 100044, China

    Qian Wang

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  1. Haitao Wang
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  2. Qian Wang
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Correspondence to Qian Wang.

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Wang, H., Wang, Q. Sampled-data state estimation for delayed neural networks with discontinuous activations. Int. J. Mach. Learn. & Cyber. 7, 805–817 (2016). https://doi.org/10.1007/s13042-014-0301-x

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  • DOI: https://doi.org/10.1007/s13042-014-0301-x

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