基于分解方法的全球米塔格-莱弗勒同步对于分数顺序的克利福德值的神经网络,具有传输延迟和冲动
R Sriraman1, N Manoj1, Anbalagan Pratap2
1Department of Mathematics, College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, 603203, India.
概括
这项研究针对分数顺序的克利福德值神经网络与延迟和冲动的全球Mittag-Leffler同步. 新的条件确保了同步,从而实现了新的彩色图像加密算法.
科学领域:
- 复杂的系统复杂的系统.
- 非线性动力学是一种非线性动力学.
- 计算神经科学是一种神经科学.
背景情况:
- 分数顺序的克利福德值神经网络 (FOCLVNNs) 提供先进的建模功能.
- 传输延迟和冲动是神经网络动态中的关键因素.
- 全球Mittag-Leffler同步 (GMLS) 是协调网络行为的关键概念.
研究的目的:
- 调查FOCLVNNs与传输延迟和冲动的GMLS问题.
- 开发一个新的框架来分析FOCLVNNs的动态.
- 提出基于FOCLVNNs的同步的图像加密算法.
主要方法:
- 将FOCLVNNs分解为多维实值神经网络 (RVNNs).
- 应用利亚普诺夫函数和不等式技术.
- 制定一个状态反控制器用于同步.
主要成果:
- 在FOCLVNN中实现GMLS的新和强大的条件.
- 通过避免复杂的克利福德数乘法来证明简化分析.
- 使用主-奴隶FOCLVNNs.使用彩色图像加密算法的开发和验证.
结论:
- 提出的方法为FOCLVNNs中的GMLS提供了新的见解和实际解决方案.
- 该研究通过数值和图形分析验证了同步条件和加密算法的有效性.
- 这项研究有助于理解复杂的神经网络动态及其应用.
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