在参数不确定性下的多稳定随机神经网络多同步的合冲动控制
IEEE transactions on neural networks and learning systems
|March 10, 2026
概括
本研究针对具有时间延迟和不确定性的多稳定随机神经网络 (MSNN) 的多重同步. 一种新的冲动控制策略确保了同步,降低了控制成本,并在复杂的网络动态中验证了有效性.
科学领域:
- 神经科学是一个神经科学.
- 控制理论 控制理论
- 应用数学 应用数学 应用数学
背景情况:
- 多稳定随机神经网络 (MSNN) 呈现出与现实的神经环境相关的复杂动态.
- 时间变化的延迟和参数不确定性在控制MSNN方面带来了重大挑战.
- 在复杂网络中实现同步对于理解新出现的行为至关重要.
研究的目的:
- 在具有挑战性的条件下研究多稳定的随机神经网络 (MSNN) 的多重异步.
- 开发一个具有成本效益的控制策略,以实现MSNN系统中的同步.
- 推导出适用于固定和交换网络拓的多同步化的足够条件.
主要方法:
- 构建一个MSNN模型,包括时间变化的延迟和参数不确定性.
- 采用合的冲动控制策略来管理网络同步.
- 开发一个利亚普诺夫函数和应用平均冲动间隔概念.
- 根据固定和开关网络拓学的分析.
主要成果:
- 成功地获得了实现MSNN多重同步的足够条件.
- 建议的冲动控制策略有效降低了控制成本.
- 通过数值模拟证实了控制方案的有效性.
结论:
- 该研究为分析和控制复杂MSNN中的多同步提供了一个强大的框架.
- 开发的冲动控制方法为动态神经网络中的同步提供了一种高效的方法.
- 这些发现有助于在随机神经系统中对同步的理论理解和实际应用.
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