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<p>This paper explores global polynomial synchronization (GPS) for uncertain complex-valued coupled memristive neural networks (CVCMNNs) with proportional delays via adaptive event-triggered sliding mode controller (SMC) design, including degeneration from heterogeneous to homogeneous cases. First, interval matrix theory is extended to the complex domain to handle complex-valued memristive properties, eliminating the complexity of real-imaginary decomposition. Second, integrating advantages of adaptive control, event-triggered control, and sliding mode control, we design an adaptive event-triggered SMC and construct a Lyapunov functional to derive conditions ensuring GPS. Finally, numerical simulations demonstrate the effectiveness of theoretical results with applications to image encryption.</p>

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Synchronization of uncertain complex-valued coupled memristive neural networks with proportional delays via event-triggered sliding mode control for image encryption

  • 期刊论文

  • 发表日期：

    2026年2月20日

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  • Yuxian Guo,
  • Liqun Zhou

摘要

This paper explores global polynomial synchronization (GPS) for uncertain complex-valued coupled memristive neural networks (CVCMNNs) with proportional delays via adaptive event-triggered sliding mode controller (SMC) design, including degeneration from heterogeneous to homogeneous cases. First, interval matrix theory is extended to the complex domain to handle complex-valued memristive properties, eliminating the complexity of real-imaginary decomposition. Second, integrating advantages of adaptive control, event-triggered control, and sliding mode control, we design an adaptive event-triggered SMC and construct a Lyapunov functional to derive conditions ensuring GPS. Finally, numerical simulations demonstrate the effectiveness of theoretical results with applications to image encryption.