在多个随机抽样下,增量时间变化的延迟马科维亚跳跃神经网络的平均平方同步
IEEE transactions on neural networks and learning systems
|March 18, 2025
概括
这项研究使用多个随机抽样解决了加法时间变化的延迟马科维亚跳跃神经网络的平均平方同步问题. 拟议的方法提高了安全图像加密性能.
科学领域:
- 控制理论 控制理论
- 人工智能的人工智能
- 网络安全 网络安全
背景情况:
- 添加时间变化的延迟马科维亚跳跃神经网络 (ATVMJNNs) 提出了复杂的同步挑战.
- 现有的方法往往受到模型保守主义和有限的适用性的影响.
研究的目的:
- 为了解决ATVMJNNs的平均平方非对称同步.
- 将这些进步应用于安全的图像加密 (SIE).
- 在同步分析中减少模型保守主义.
主要方法:
- 开发一种模式依赖的不连续的利亚普诺夫-克拉索夫斯基函数 (DLKF).
- 介绍了基于松散矩阵的辅助积分不等式 (ASMBII) 用于积分项近似.
- 建立一个多重随机抽样框架.
- 使用线性矩阵不等式 (LMIs) 来推导稳定性标准.
主要成果:
- 实现了不那么保守的平均平方对比稳定性的标准.
- 在数值验证中表现出卓越的性能.
- 通过实用的安全图像加密应用程序验证了有效性.
结论:
- 提出的方法有效地解决了ATVMJNN的同步问题.
- 与现有方法相比,这些技术提供了更高的性能.
- 该研究为理论分析和安全图像加密的实际应用提供了强大的框架.
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