通过非减少顺序方法来定时稳定惯性记忆神经网络的有限时间稳定
概括
本研究涉及具有时间延迟的惯性记忆神经网络 (IMNNs) 的有限时间稳定. 新的标准确保IMNN在有限的时间内达到稳定性,消除了先前对延迟的可区分性约束.
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:
- 惯性记忆神经网络 (IMNNs) 对于复杂的计算至关重要.
- 具有时间延迟的IMNN稳定存在重大挑战.
研究的目的:
- 调查IMNNs的有限时间稳定,有限和无限的时间变化延迟.
- 为IMNNs开发一种新的不连续状态反控制器.
主要方法:
- 使用非减小顺序方法进行理论简化.
- 设计了一个不连续状态反控制器,用于直接状态融合.
- 获得了有限时间稳定和估计的结算时间的新标准.
主要成果:
- 实现了各种时间延迟的IMNN的有限时间稳定.
- 消除了对时间延迟在分析中的可区分性需求.
- 建立了在有限时间内保证趋同到零的标准.
结论:
- 拟议的方法有效地稳定了IMNN在有限的时间内,即使有复杂的延迟.
- 这些发现减轻了现有的约束,提供了更广泛的适用性.
- 数字示例验证了理论结果和控制器的有效性.
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