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改进了切换条件,用于对离散时间切换延迟神经网络的可达集估计
Wenting Chang1, Libing Wu1, Shuaibing Zhu2
1School of Sciences, University of Science and Technology Liaoning, Anshan, Liaoning 114051, China.
概括
本研究介绍了离散时间交换延迟神经网络 (SDNN) 的综合切换策略,即使在不稳定的子网络中,也可实现可达集估计 (RSE) 的放松条件.
科学领域:
- 控制理论 控制理论
- 计算神经科学是一种神经科学.
- 动态系统 动态系统
背景情况:
- 交换延迟神经网络 (SDNN) 的现有方法使用时间依赖或状态依赖的交换,通常需要子网络稳定性或忽略历史状态信息.
- 可达集合估计 (RSE) 对于分析SDNN的行为至关重要.
研究的目的:
- 在离散时间SDNN中为RSE开发一个更宽松,更少保守的框架.
- 引入一个结合切换策略 (CSS),有效地利用历史神经元状态信息.
- 在SDNN模型中允许不稳定的子网络.
主要方法:
- 为离散时间SDNN制定联合切换策略 (CSS).
- 应用时间依赖的多重利亚普诺夫-克拉索夫斯基函数 (TDMLF) 技术.
- 开发对RSE进行改进的标准.
主要成果:
- 拟议的CSS允许不稳定的子网络,放松了以前的设计约束.
- 采用TDMLF技术来导出确保可达到集合是有限的标准.
- 通过两个模拟示例验证了RSE方法.
结论:
- 新的CSS为离散时间SDNN提供了更灵活,更少保守的RSE方法.
- 该方法有效地限制了可达到的集合,增强了稳定性分析.
- 模拟结果证实了拟议的RSE技术的实用性和改进的性能.
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