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<p>This article investigates the stabilization problem for the modelled memristive bidirectional associative memory neural networks (MBAMNNs) with time-varying delay using two distinct actuators. To be more specific, the structural mechanism of MBAMNNs can be integrated into genetic regulatory networks (GRNs), with memory encoded in gene expression to critically assess its potential application for delivering a more precise and robust representation. The performance of control signals and actuator fluctuations are assessed for both MBAMNNs and and memristive GRNs (MGRNs) model, with a fault-tolerant configuration achieved through the state feedback control design. Based on the Lyapunov stability theory and differential inclusion theory, the global asymptotic stability criteria is obtained in terms of linear matrix inequalities (LMIs) by utilizing the Wirtinger’s inequality. To mitigate actuator fluctuations, a specific frequency range is determined by the fault-tolerant model, enabling the estimation of controller gain matrices are then computed using MATLAB based on both known and unknown actuator cases for the effective signal frequency transmission. Finally, the numerical simulation is demonstrated to validate the theoretical findings, which shows the effectiveness of the reliable state feedback controller by exhibiting feasible results.</p>

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Fault tolerant control design for memristive bidirectional associative memory neural networks and its application in genetic regulatory networks

  • 期刊论文

  • 发表日期：

    2026年3月7日

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  • R. Suvetha,
  • Y. -K. Ma,
  • P. Prakash

摘要

This article investigates the stabilization problem for the modelled memristive bidirectional associative memory neural networks (MBAMNNs) with time-varying delay using two distinct actuators. To be more specific, the structural mechanism of MBAMNNs can be integrated into genetic regulatory networks (GRNs), with memory encoded in gene expression to critically assess its potential application for delivering a more precise and robust representation. The performance of control signals and actuator fluctuations are assessed for both MBAMNNs and and memristive GRNs (MGRNs) model, with a fault-tolerant configuration achieved through the state feedback control design. Based on the Lyapunov stability theory and differential inclusion theory, the global asymptotic stability criteria is obtained in terms of linear matrix inequalities (LMIs) by utilizing the Wirtinger’s inequality. To mitigate actuator fluctuations, a specific frequency range is determined by the fault-tolerant model, enabling the estimation of controller gain matrices are then computed using MATLAB based on both known and unknown actuator cases for the effective signal frequency transmission. Finally, the numerical simulation is demonstrated to validate the theoretical findings, which shows the effectiveness of the reliable state feedback controller by exhibiting feasible results.