离散时间变量顺序分数神经网络的投影同步与时间变化的延迟
概括
本研究探讨了离散时间变量顺序分数神经网络 (DVFNNs) 中的投射和完全同步 (CS). 新的变量级分数不等式和离散时间的 VF Halanay 不等式导致了同步标准.
科学领域:
- 分数微积分的计算.
- 神经网络的神经网络的神经网络
- 非线性动力学是一种非线性动力学.
背景情况:
- 分数顺序系统比整数顺序系统提供了增强的建模功能.
- 神经网络中的同步对于复杂的信息处理至关重要.
- 时间变化的延迟在分析系统稳定性和同步性方面带来了重大挑战.
研究的目的:
- 为了研究投射同步和完全同步 (CS) 在离散时间变量顺序的分数神经网络 (DVFNNs) 与时间变化的延迟.
- 开发新的变量级分数 (VF) 不等式和离散时间 VF Halanay 不等式.
- 在考虑的DVFNNs中建立足够的标准来实现投射同步和CS.
主要方法:
- 使用纳布拉拉拉普拉斯变换和米塔格-莱弗勒函数属性开发新的变量级分数不等式 (VF).
- 在离散时间意义上严格证明变量级分数哈拉奈不等式.
- 使用已确定的 VF 不等式和混合控制器推导同步标准.
主要成果:
- 引入了两个新的VF不等式,扩展了现有的常量分数 (CF) 不等式.
- 证明了离散时间的 VF Halanay 不等式,为稳定性分析提供了一个关键工具.
- 在DVFNNs中获得足够的条件来实现投射同步和完全同步.
结论:
- 衍生出的标准确保在离散时间变量顺序的分数神经网络中实现投射和完全同步.
- 数字模拟证实了拟议的同步方法的有效性.
- 该研究展示了图像加密的实际应用,突出了DVFNNs的潜力.
相关概念视频
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