对延迟马尔科夫跳跃神经网络的异步自适应事件触发故障检测:一种延迟变化依赖的方法
Wen-Juan Lin1, Qingzhi Wang1, Guoqiang Tan2
1School of Automation, Qingdao University, Qingdao, 266071, China; Shandong Key Laboratory of Industrial Control Technology, Qingdao University, Qingdao, 266071, China.
概括
这项研究引入了一种新的故障检测方法,用于具有时间变化的延迟的离散时间马尔科夫跳跃神经网络 (MJNNs). 该方法使用自适应事件触发的H∞波器来提高检测准确性和可靠性.
科学领域:
- 控制系统工程 控制系统工程
- 网络系统分析 网络系统分析
- 检测和诊断故障的检测和诊断.
背景情况:
- 马尔科夫跳跃神经网络 (MJNNs) 是复杂的系统,容易发生故障.
- 在MJNN中,时间变化的延迟和不匹配的模式对故障检测构成重大挑战.
- 现有的方法可能无法充分解决延迟和系统模式的动态性质.
研究的目的:
- 开发一个强大的故障检测策略,以离散时间的MJNN与时间变化的延迟和不匹配的模式.
- 为增强故障检测设计一个自适应事件触发和异步的H∞波器.
- 确保拟议的方法依赖于延迟极限和延迟变化速率.
主要方法:
- 对于故障检测,采用了依赖于延迟变化的方法.
- 构建了一个自适应事件触发和异步的H∞波器.
- 使用延迟产品类型的Lyapunov-Krasovskii (L-K) 函数与延迟依赖矩阵和矩阵多项式不等式.
- 有界实数 (BRL) 是为了保证过器的存在而得出的.
主要成果:
- 提出的边界实数参数 (BRLs) 取决于延迟边界和延迟变化率.
- 确定了合适的适应性事件发生器和过器的存在.
- 模拟结果证明了开发的理论方法的有效性.
结论:
- 延迟变量依赖的方法为离散时间MJNN的故障检测提供了有效的方法.
- 适应性事件触发的H∞过器有效地解决了因时间变化的延迟和不匹配模式所带来的挑战.
- 理论框架通过模拟验证,证实其实际适用性.
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