对于离散时间严格反系统而言,一个新的神经动态学习框架:基于内部相互作用的重量适应定律.
IEEE transactions on cybernetics
|August 8, 2023
概括
本研究为不确定系统引入了一种新的学习控制 (LC) 框架. 它使神经网络的重量汇聚到一个常数,提高存储和稳定性.
科学领域:
- 控制系统工程 控制系统工程
- 人工智能的人工智能
- 神经网络的神经网络的神经网络
背景情况:
- 不确定的离散时间严格反系统给传统的控制方法带来了挑战.
- 现有的学习控制 (LC) 框架在预测模型中扎着重量趋同.
研究的目的:
- 为不确定的离散时间严格反系统开发基于内部交互的动态学习控制 (LC) 框架.
- 在预测控制模型中解决分歧神经重量趋同的问题.
主要方法:
- 原始系统被转换成一个n步前进的预测模型.
- 预测模型被分解成n个一步前进的子系统 (代理).
- 分布式合作重量适应规律是使用互连拓设计的.
主要成果:
- 提出了一个基于内部重量相互作用的新型神经动态LC框架.
- 该框架确保了最终的统一界限和出色的控制性能.
- 估计的重量汇聚到一个独特的理想常数,而不是多个不同的常数.
结论:
- 开发的LC框架提高了知识存储和利用效率.
- 它提高了不确定动态的控制系统的稳定性.
- 模拟结果验证了拟议框架的有效性和好处.
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