模糊惰性细胞神经网络的采样数据同步及其在安全通信中的应用
Sasikala Subramaniam1, Prakash Mani1
1Department of Mathematics, Vellore Institute of Technology, Vellore, 632014, Tamilnadu, India.
概括
本研究介绍了模糊惰性细胞神经网络 (FICNNs) 的采样数据控制 (SDC) 方案,以实现同步. 拟议的方法确保了网络稳定性,并证明了安全图像加密的应用.
科学领域:
- 神经网络的神经网络的神经网络
- 控制系统 控制系统
- 模糊的逻辑 模糊的逻辑
背景情况:
- 细胞神经网络 (CNN) 可以被建模为二级系统 (ICNN),以捕获时间依赖的响应.
- 模糊细胞神经网络 (FCNNs) 集成模糊逻辑以基于规则的信息处理.
- 模糊惰性细胞神经网络 (FICNNs) 将这些概念结合起来,实现复杂的动态.
研究的目的:
- 设计一个采样数据控制 (SDC) 方案,以实现模糊惰性细胞神经网络 (FICNNs) 的同步.
- 提出一个由用户控制的FICNN模型,保持原始FICNN模型的动态特性.
- 通过同步,确保驱动和响应系统的动态特性.
主要方法:
- 利用利亚普诺夫稳定理论,在线性矩阵不等式 (LMI) 中推导出足够的稳定条件.
- 将时间延迟信息纳入稳定性条件,使用具有下限和上限的二次函数,通过负判定定理 (NDL) 进行评估.
- 通过分析驱动和响应系统之间的错误模型,开发同步方法.
主要成果:
- 对于FICNNs的同步,已经获得了足够的稳定性条件,保证了错误模型的趋同.
- 拟议的采样数据控制方案有效地确保了驱动响应系统的同步.
- 数字模拟验证了理论框架,并证明了同步方法的有效性.
结论:
- 开发的采样数据控制方案为同步FICNN提供了可靠的方法.
- 导出的稳定性条件,包括时间延迟,提供了更全面的分析.
- 当同步时,FICNN模型显示出作为图像加密和解密的安全加密系统的承诺.
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