具有适应性学习参数的时间延迟竞争性神经网络在时空离散框架中的异质边界同步
Tianwei Zhang1, Shaobin Rao2, Jianwen Zhou1
1School of Mathematics and Statistics, Yunnan University, Yunnan, Kunming 650500, China.
概括
这项研究使用自适应式学习参数和边界控制实现了异质时间延迟竞争神经网络的指数级同步. 该方法通过数值示例进行验证.
科学领域:
- 计算神经科学是一种神经科学.
- 人工智能的人工智能
- 控制理论 控制理论
背景情况:
- 主-奴隶时间延迟竞争神经网络 (STD-CNNs) 对于复杂的时空处理至关重要.
- STD-CNNs中的异质结构在实现同步行为方面带来了挑战.
- 适应性学习参数是管理网络动态的关键.
研究的目的:
- 研究具有异质结构的时间延迟的STD-CNNs的指数级同步.
- 在奴隶网络中开发基于自适应学习参数的控制策略.
- 将同步方法扩展到同质的STD-CNNs.
主要方法:
- 在奴隶STD-CNNs中设计一个自适应学习参数.
- 实施边界控制以强制执行同步.
- 使用线性矩阵不等式 (LMI) 进行数学分析和解决方案导出.
- 用时间变化的学习参数建模同步问题.
主要成果:
- 实现了异质时间延迟的STD-CNNs的指数级同步.
- 证明了自适应式学习参数和边界控制的有效性.
- 确认同质的STD-CNNs也可以通过边界控制实现指数级同步.
- 通过数值示例验证了拟议的方法.
结论:
- 拟议的自适应学习参数和边界控制策略有效地解决了异质时间延迟的STD-CNNs的指数级同步问题.
- 基于LMI的方法提供了一种可行和可计算的同步方法.
- 这项工作有助于理解和控制复杂的神经网络动态.
相关概念视频
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