适应性间歇性固定控制用于与反应-扩散项目同步延迟非线性记忆神经网络
IEEE transactions on neural networks and learning systems
|January 8, 2024
概括
这项研究引入了用于同步延迟非线性记忆神经网络 (MNN) 的新控制方法. 模糊的自适应固定控制确保了指数级同步,通过间歇性控制策略节省能源.
科学领域:
- 控制理论 控制理论 控制理论
- 非线性系统是非线性系统.
- 计算神经科学是一种计算神经科学.
背景情况:
- 记忆神经网络 (MNN) 对于先进的计算至关重要.
- 延迟的MNN中的同步是复杂的,因为非线性和反应扩散术语.
- 现有的控制方法可能缺乏适应性或能源效率.
研究的目的:
- 为了研究延迟非线性MNN的全球指数同步.
- 开发新的模糊适应性固定控制方案.
- 设计一个节能,定期间歇的固定控制法.
主要方法:
- 运用了利亚普诺夫理论和格林的公式.
- 提出了一个新的模糊自适应固定控制方案.
- 为模糊的MNNs (FMNNs) 开发了一种非周期性间歇性固定控制策略.
主要成果:
- 新的代数标准是为了指数同步而衍生出来的.
- 控制收益可以根据网络节点状态进行调整.
- 间歇控制机制节约了能源.
结论:
- 拟议的模糊自适应固定控制方案有效地在延迟的非线性MNN中实现全球指数同步.
- 间歇性控制策略为FMNN同步提供了一种节能方法.
- 数字模拟验证了理论发现.
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