基于观察者的事件触发的故障耐受同步,用于受多次故障影响的记忆神经网络
IEEE transactions on neural networks and learning systems
|August 13, 2025
概括
这项研究涉及使用新型故障观察器和事件触发控制在多次故障下进行记忆神经网络 (MNN) 同步. 实现了有限时间和固定时间同步,提高了MNN的弹性.
科学领域:
- 控制系统工程 控制系统工程
- 计算神经科学是一种神经科学.
- 人工智能的人工智能
背景情况:
- 记忆神经网络 (MNN) 对于先进的计算至关重要,但容易发生多次故障.
- 在故障条件下确保MNNs的可靠运行是一个重大挑战.
研究的目的:
- 为了研究在多个,合故障下MNNs的同步问题.
- 开发耐故障的同步策略,以实现强大的MNN性能.
主要方法:
- 介绍了MNN的通用故障模型,包括各种故障类型.
- 设计了使用中间变量和状态/输出反的故障函数观察器.
- 开发了基于Halanay类型不平等的事件触发,容错的同步方案.
主要成果:
- 成功构建故障函数观察器以检测和估计多个故障.
- 通过设计的控制方案实现了有限时间和固定时间同步/准同步.
- 通过模拟和比较实验证明了拟议方法的有效性.
结论:
- 开发的故障观察器和事件触发同步策略有效地确保了MNN的可靠同步,尽管出现了多次故障.
- 提出的方法为记忆神经网络提供了强大的耐故障控制解决方案.
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