对于中性类型多重延迟科恩-格罗斯伯格神经网络的稳定性,有了新的条件
1Department of Electrical and Electronics Engineering, Faculty of Engineering, Bursa Uludag University, Bursa, Turkey.
PloS one
|March 9, 2026
概括
研究人员为复杂的Cohen-Grossberg神经网络开发了新的稳定性条件,具有多次延迟. 这些代数条件保证了全球的非对称稳定性,为现有的人工神经网络分析方法提供了替代方案.
科学领域:
- 人工智能的人工智能
- 计算神经科学是一种神经科学.
- 控制理论 控制理论
背景情况:
- 科恩-格罗斯伯格神经网络在人工智能和计算神经科学中至关重要.
- 这些网络中的多个延迟参数引入了复杂的动态,可能导致不稳定和混乱的行为.
- 确保系统稳定对于可靠的神经网络应用程序至关重要.
研究的目的:
- 为了研究中性类型Cohen-Grossberg神经网络的稳定性,具有多个延迟.
- 为这些复杂的系统开发新的,易于验证的稳定性条件.
- 为文献中现有的稳定性标准提供替代方案.
主要方法:
- 利用利亚普诺夫函数和利亚普诺夫定理来推导稳定性条件.
- 制定了新的稳定性条件作为代数方程,以便进行简单的分析.
- 运用数学方法和软件工具来检查衍生条件.
主要成果:
- 建立了新的,足够的条件,保证了研究的神经网络的全球对称稳定性.
- 提出的条件以易于计算的代数形式呈现.
- 通过详细的数值示例证明了新条件的有效性,显示了对现有标准的优越性.
结论:
- 开发的代数稳定性条件有效地确保了中性类型的科恩-格罗斯伯格神经网络具有多次延迟的全局非对称稳定性.
- 这些发现为分析复杂的人工神经网络的稳定性提供了有价值的替代方案.
- 拟议的方法简化了稳定性分析,使其通过标准数学工具可访问.
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