关联的分数级神经网络的同步分析,具有时间变化的延迟
1School of Mathematics and Statistics, Hubei Normal University, Huangshi 435002, China.
Mathematical biosciences and engineering : MBE
|September 7, 2023
概括
本研究介绍了完全同步和Mittag-Leffler同步在分数级神经网络中的方法,其延迟时间可变. 该研究建立了使用固定控制和自适应反进行同步的条件,并通过模拟证实了可行性.
科学领域:
- 神经科学是一个神经科学.
- 控制理论 控制理论
- 动态系统 动态系统
背景情况:
- 分数级神经网络 (FONNs) 是复杂的系统,在各种领域都有应用.
- 在FONN中时间变化的延迟给稳定性和同步分析带来了重大挑战.
- 现有的同步方法可能不高效或不适用于所有FONN配置.
研究的目的:
- 研究与时间变化的延迟相结合的FONNs中的完全同步和Mittag-Leffler同步.
- 开发新的控制策略,以实现这些同步目标.
- 建立足够的同步条件,并通过数值模拟来验证它们.
主要方法:
- 使用克罗内克产品技术和利亚普诺夫直接方法进行完整的同步分析.
- 设计了一个新的自适应反控制器,集成拉祖米金类型的方法和米塔格-莱弗勒稳定性理论.
- 采用固定控制策略,以资源效率为目标,针对网络节点的子集.
主要成果:
- 在固定控制下获得了足够的条件来完全同步FONNs.
- 一个新的自适应反控制器被设计来实现米塔格-莱弗勒同步.
- 提出的方法证明了简化的计算,并通过数值示例证实了可行性.
结论:
- 已建立的同步条件是实际和可实现的.
- 开发的控制策略是有效的同步结合的小数序神经网络与时间变化的延迟.
- 这些发现有助于理解和控制复杂的动态系统.
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