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Non-fragile projective synchronization of delayed discrete-time neural networks via generalized weighted summation inequality

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  • Sri Raja Priyanka, K.
  • Nagamani, G.

Abstract

This paper explores the problem of exponential projective synchronization (EPS) for delayed discrete-time uncertain neural networks (NNs) subject to non-fragile state feedback control. To attain less conservative results, some generalized weighted summation inequalities are proposed, which encompass Jensen-based summation inequality (JBSI) as its special case. By constructing suitable Lyapunov-Krasovskii functional (LKF), sufficient conditions are derived in terms of linear matrix inequalities (LMIs) to ensure the EPS via newly introduced weighted summation inequalities. Finally, numerical examples are provided, supported by simulation results through MATLAB software that ensures the efficiency of the desired theoretical outcomes and the comparative study in terms of maximum allowable upper bound (MAUB) evaluates the effectiveness of the established results by enhancing the feasible region.

Suggested Citation

  • Sri Raja Priyanka, K. & Nagamani, G., 2024. "Non-fragile projective synchronization of delayed discrete-time neural networks via generalized weighted summation inequality," Applied Mathematics and Computation, Elsevier, vol. 479(C).
    • Handle: RePEc:eee:apmaco:v:479:y:2024:i:c:s0096300324003461
    DOI: 10.1016/j.amc.2024.128885
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    References listed on IDEAS

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