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A New LMI Approach to Finite and Fixed Time Stabilization of High-Order Class of BAM Neural Networks with Time-Varying Delays

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Abstract

This article deals with the finite time stabilization (FTSB) and fixed time stabilization (FXTSB) problems for a high-order class of bidirectional associative memories neural networks (NNs) with time varying delay. Compared with the previous studies, some new kinds of controllers are designed to stabilize in finite time and fixed time the considered NNs. Based on finite time and fixed time stability theory, we derive new sufficient conditions which ensure the FTSB and the FXTSB. Meanwhile, the gains of the controllers proposed could be constructed by solving linear matrix inequalities. Then, the settling time for the FXTSB is estimated and a high-precision of these time is obtained. Finally, two numerical examples with graphical illustrations are given to appear the effectiveness of our theoretical main results.

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Authors and Affiliations

  1. Faculty of Sciences of Bizerta, Department of Mathematics, Research Units of Mathematics and Applications UR13ES47, University of Carthage, 7021, Zarzouna, Bizerta, Tunisia

    Chaouki Aouiti & Foued Miaadi

  2. School of Mathematical Sciences, Shandong Normal University, Ji’nan, 250014, Shandong, China

    Xiaodi Li

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  1. Chaouki Aouiti
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  2. Xiaodi Li
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  3. Foued Miaadi
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Correspondence to Chaouki Aouiti.

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Aouiti, C., Li, X. & Miaadi, F. A New LMI Approach to Finite and Fixed Time Stabilization of High-Order Class of BAM Neural Networks with Time-Varying Delays. Neural Process Lett 50, 815–838 (2019). https://doi.org/10.1007/s11063-018-9939-9

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