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<p>Inspired by Hopfield-type neural networks, this research examines the uniform ultimate boundedness and practical stability of solutions for a class of neural network differential equations. By selecting a suitable Lyapunov function, we provide sufficient conditions for the existence of a globally exponentially stable neighborhood around the origin. Two numerical examples demonstrate the effectiveness and applicability of the proposed results.</p>

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On the Ultimate Boundedness and Stability Properties of a Class of Neural Network-Based Dynamical Systems

  • 期刊论文

  • 发表日期：

    2026年2月20日

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  • Hanen Damak,
  • Mohamed Ali Hammami

摘要

Inspired by Hopfield-type neural networks, this research examines the uniform ultimate boundedness and practical stability of solutions for a class of neural network differential equations. By selecting a suitable Lyapunov function, we provide sufficient conditions for the existence of a globally exponentially stable neighborhood around the origin. Two numerical examples demonstrate the effectiveness and applicability of the proposed results.