对于具有随机参数和混合时间延迟的复杂值神经网络的稳定性和同步性
Yufei Liu1,2, Bo Shen1,2, Jie Sun1,2
1College of Information Science and Technology, Donghua University, Shanghai, 201620 China.
Cognitive neurodynamics
|October 3, 2023
概括
本研究介绍了具有随机参数和时间延迟的复杂值神经网络 (CVNNs). 它在这些先进的神经网络模型中建立了稳定性和同步性的条件.
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:
- 复杂值神经网络 (CVNN) 为处理复杂数据提供了增强的能力.
- 随机参数和混合时间延迟对于现实的CVNN建模至关重要.
研究的目的:
- 提出一个包含随机参数和混合时间延迟的CVNN类.
- 调查这些拟议的CVNN的稳定性和同步性.
主要方法:
- 利亚普诺夫稳定理论用于推导非对称稳定性的条件.
- 使用Kronecker产品开发的矩阵不等式,以确保同步.
主要成果:
- 建立了CVNN的平均平方对象稳定性的足够条件.
- 可行的矩阵不等式保证了结合相同的CVNN的同步.
结论:
- 提出的理论框架有效地解决了复杂的CVNN中的稳定性和同步性.
- 数字示例验证了衍生结果的实际适用性和有效性.
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