基于四次数值的基于memristor的Cohen-Grossberg神经网络与时间变化的延迟的指数同步:规范方法
Yanzhao Cheng1, Yanchao Shi1,2, Jun Guo3
1School of Science, Southwest Petroleum University, Chengdu, 610500 China.
Cognitive neurodynamics
|August 6, 2024
概括
这项研究实现了对具有时间延迟的复杂四次数值神经网络的指数级同步. 一种新的方法简化了分析,并扩大了基于memristor的科恩-格罗斯伯格模型的适用性.
科学领域:
- 计算神经科学是一种神经科学.
- 复杂系统动力学 复杂系统动力学
- 非线性控制理论 不线性控制理论
背景情况:
- 科恩-格罗斯伯格神经网络 (CGNNs) 是神经科学和人工智能的基本模型.
- 基于memristor的CGNN提供了增强的计算能力,但由于其复杂的动态和时间变化的延迟,它存在同步挑战.
- 在这些网络中实现指数级同步对于可靠的信息处理和安全的通信至关重要.
研究的目的:
- 研究和建立基于四次数值的基于memristor的科恩-格罗斯伯格神经网络 (QMCGNNs) 的指数同步条件,时间延迟可变.
- 开发一种新的控制策略,保证指数级同步.
- 与现有方法相比,提出一种简化和更普遍的分析方法.
主要方法:
- 利用差分包容理论和设置值地图理论来处理QMCGNNs的不连续性.
- 将不连续的QMCGNN转换为具有间隔参数的不确定系统.
- 设计一种新的控制器,并使用不平等技术来推导同步标准.
- 引入一种改进的单一规范方法,用于直接分析方法,避免分解技术.
主要成果:
- 为了在QMCGNNs中实现具有时间变化的延迟的指数级同步,我们得出了几个标准.
- 拟议的方法对激活函数的限制较小,并简化了Lyapunov分析过程.
- 数字模拟验证了衍生标准和拟议的控制策略的有效性.
结论:
- 这项研究成功地证明了QMCGNNs的指数同步,使用一种新的直接分析方法,具有时间变化的延迟.
- 开发的方法比以前的技术更普遍,计算效率更高.
- 这些发现有助于理论理解和复杂神经网络同步的实际应用.
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