基于内存状态反控制的延迟冲动隐性混合神经网络的主-奴隶同步
Zekun Wang1, Guangming Zhuang1, Xiangpeng Xie2
1School of Mathematical Sciences, Liaocheng University, Liaocheng Shandong 252059, PR China.
概括
这项研究通过使用记忆状态反控制实现了对延迟冲动神经网络的H∞主-奴隶同步. 该方法确保了系统的稳定性和性能,即使有时间变化的延迟.
科学领域:
- 控制理论 控制理论
- 神经网络的神经网络的神经网络
- 系统工程 系统工程
背景情况:
- 主-奴隶同步对于复杂的系统至关重要.
- 延迟的冲动性混合神经网络带来了重要的控制挑战.
- 确保在不确定性下的H∞性能是关键要求.
研究的目的:
- 为了研究延迟冲动隐性混合神经网络的H∞主-奴隶同步.
- 开发一个强大的记忆状态反控制策略.
- 为了实现保证的性能指数和系统可接受性.
主要方法:
- 一个随机的冲动时间依赖的Lyapunov-Krasovskii函数的开发.
- 记忆状态反控制的应用.
- 在控制器设计中使用线性矩阵不等式 (LMI).
- 采用自由权重矩阵技术来处理时间变化的延迟.
主要成果:
- 闭环系统实现了随机可接受性和规定的H∞性能.
- 一个模式依赖的内存状态反同步控制器成功设计.
- 该方法有效地放松了对时间变化延迟的导数的约束.
- 在遗传调节网络上的模拟结果验证了该方法.
结论:
- 拟议的内存状态反控制有效地解决了针对目标神经网络的H∞主-奴隶同步问题.
- 开发的Lyapunov-Krasovskii功能和控制设计技术为类似的复杂系统提供了通用的框架.
- 这些发现在生物经济系统和其他需要同步复杂动态的领域都有潜在的应用.
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