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An LMI Based State Estimation for Fractional-Order Memristive Neural Networks with Leakage and Time Delays

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Abstract

This paper investigates the state estimation problem for a class of fractional-order memristive neural networks (FOMNNs) with leakage and time delay. The main objective of this study is to construct an efficient estimator such that the state of the corresponding estimation error is globally stable. Distinct to the previous studies, the state estimation problem of FOMNNs is investigated through fractional-order Lyapunov direct method. The sufficient conditions that ensure the global stability of the error system has been derived as a set of solvable linear matrix inequalities. In order to validate the effectiveness of the proposed theoretical results, two numerical examples have been illustrated.

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Authors and Affiliations

  1. Department of Mathematics, The Gandhigram Rural Institute (Deemed to be University), Dindigul, Tamil Nadu, India

    G. Nagamani, M. Shafiya & G. Soundararajan

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  1. G. Nagamani
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  2. M. Shafiya
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  3. G. Soundararajan
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Correspondence to G. Nagamani.

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Nagamani, G., Shafiya, M. & Soundararajan, G. An LMI Based State Estimation for Fractional-Order Memristive Neural Networks with Leakage and Time Delays. Neural Process Lett 52, 2089–2108 (2020). https://doi.org/10.1007/s11063-020-10338-0

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  • DOI: https://doi.org/10.1007/s11063-020-10338-0

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