动态memristor中性静态细胞神经网络的平均平方指数稳定性 随时间变化的延迟
IEEE transactions on cybernetics
|March 4, 2026
概括
这项研究分析了流电荷域中的动态memristor中性静态细胞神经网络 (DM-NSDCNNs) 的稳定性. 新的方法确保了不同类型的memristor的稳定性,验证了系统可靠性.
科学领域:
- 计算神经科学是一种神经科学.
- 非线性动力学是一种非线性动力学.
- 随机系统 随机系统 随机系统
背景情况:
- 动态memristor中性随机细胞神经网络 (DM-NSDCNNs) 在流电荷领域提供优势,包括零稳定状态功耗.
- 基于memristor的神经网络对于高级计算至关重要,但它们的稳定性分析需要专门的技术.
- 现有的神经网络分析通常集中在电压-电流域,限制了对基于memristor的系统的适用性.
研究的目的:
- 为了研究DM-NSDCNNs的平均平方指数稳定性与时间变化的延迟.
- 开发针对基于memristor的神经网络的新型稳定性分析技术.
- 通过对不同memristor构成关系的数值模拟来验证拟议的方法.
主要方法:
- 在流电荷领域的分析,不同于传统的电压电流领域的研究.
- 将比较原理和 reductio ad absurdum 应用到零碎的线性记忆器关系上.
- 运用利亚普诺夫的功能技术进行随机分析与立方非线性memristor关系.
主要成果:
- 导出DM-NSDCNN的稳定性标准,这些DM-NSDCNN具有片式线性memristor构成关系.
- 建立 DM-NSDCNN 与立方非线性记忆器构成关系的稳定性标准.
- 数字示例证实了开发的稳定性分析技术的有效性和潜力.
结论:
- 提出的方法有效地确定了DM-NSDCNNs的平均平方指数稳定性.
- 流电荷域分析为基于memristor的神经网络提供了一个强大的框架.
- 这项研究促进了基于memristor的神经网络稳定性的理解和应用.
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