模糊中性类型惯性神经网络的固定时间稳定,具有比例延迟
1School of Mathematics Science, Tianjin Normal University, Tianjin, 300387, China.
ISA transactions
|November 2, 2023
概括
本研究针对具有比例延迟的模糊中性类型惯性神经网络的固定时间稳定. 新的标准为这些复杂系统提供了更精确的结算时间计算.
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
- 非线性系统是非线性系统.
背景情况:
- 研究带有比例延迟 (PD) 的模糊中性类型惯性神经网络 (FNTINNs).
- 解决了在这些系统中实现固定时间稳定 (FTS) 的挑战.
研究的目的:
- 为具有PD的FNTINNs的FTS开发新的反控制器.
- 建立使用Lyapunov函数 (LFs) 的FTS分析的新标准.
- 与现有方法相比,提高结算时间 (ST) 计算的准确性.
主要方法:
- 基于固定时间稳定定理的两个反控制器的设计.
- 构建适当的Lyapunov函数 (LFs) 来推导稳定性标准.
- 分析含有比例延迟的模糊中性类型惯性神经网络.
主要成果:
- 成功地推导出了两个不同的标准来实现FTS在FNTINNs与PDs.
- 提出的理论结论允许更精确的上部结算时间 (ST) 估计.
- 通过两个模拟示例验证,证明结果的实际适用性.
结论:
- 这项研究提供了有效的方法,以固定时间稳定PDs的FNTINNs.
- 开发的标准提高了神经网络控制中定位时间预测的准确性.
- 这些发现有助于推进复杂动态系统的稳定性分析.
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