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<p>This paper investigates the problem of achieving asymptotic synchronization in semi-Markovian jumping fractional-order neural networks (SMJFONNs), characterized by dual inertial dynamics and time-varying as well as proportional delays, under a quantized aperiodic intermittent control (QAIC) mechanism. By employing the properties of the Riemann–Liouville (RL) fractional derivative along with suitable variable substitutions, the proposed inertial neural network model is transformed into a standard fractional-order system. A novel QAIC strategy is then developed by integrating intermittent control with quantization, and sufficient conditions for synchronization are established through a Lyapunov–Krasovskii functional (LKF) and linear matrix inequalities (LMIs). Numerical simulations verify the effectiveness of the proposed method, showing notably faster convergence and a 32.7% reduction in synchronization time compared with the controller in Zhang et al. Circuits Syst Signal Process, 2021. <a href="https://doi.org/10.1007/s00034-021-01717-6">https://doi.org/10.1007/s00034-021-01717-6</a>.</p>

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Synchronization Analysis of Semi-Markovian Jumping Dual Inertial Fractional-Order Neural Networks with Time Varying and Proportional Delay via Quantized Aperiodic Intermittent Control

  • 期刊论文

  • 发表日期：

    2025年10月16日

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  • Pavithra Ganeshan,
  • Dharani Shanmugavel

摘要

This paper investigates the problem of achieving asymptotic synchronization in semi-Markovian jumping fractional-order neural networks (SMJFONNs), characterized by dual inertial dynamics and time-varying as well as proportional delays, under a quantized aperiodic intermittent control (QAIC) mechanism. By employing the properties of the Riemann–Liouville (RL) fractional derivative along with suitable variable substitutions, the proposed inertial neural network model is transformed into a standard fractional-order system. A novel QAIC strategy is then developed by integrating intermittent control with quantization, and sufficient conditions for synchronization are established through a Lyapunov–Krasovskii functional (LKF) and linear matrix inequalities (LMIs). Numerical simulations verify the effectiveness of the proposed method, showing notably faster convergence and a 32.7% reduction in synchronization time compared with the controller in Zhang et al. Circuits Syst Signal Process, 2021. https://doi.org/10.1007/s00034-021-01717-6.