基于memristor的惯性复杂值神经网络的同步,延迟时间可变
Pan Wang1, Xuechen Li1, Qianqian Zheng1
1School of Science, Xuchang University, Xuchang 461000, China.
Mathematical biosciences and engineering : MBE
|March 8, 2024
概括
这项研究探讨了与时间延迟同步复杂值的memristor神经网络. 新的控制方法确保了指数级同步,适应性方法为这些神经网络提供了实际实施.
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:
- 基于memristor的神经网络 (MNN) 提供了先进的计算能力.
- 神经网络中的同步对于信息处理至关重要.
- 随时变化的延迟在网络稳定性和控制方面带来了重大挑战.
研究的目的:
- 为了研究以时间变化延迟的惰性复杂值的基于memristor的神经网络 (ICVMNNs) 的指数同步.
- 开发一种新的控制方案,以实现强大的同步.
- 探索适应性同步方法的实际应用.
主要方法:
- 使用非分离和非减少方法进行分析.
- 构建了全面的利亚普诺夫函数来导出稳定性条件.
- 设计了一个新的自适应控制方案,以促进同步.
主要成果:
- 确定了ICVMNNs指数级同步的足够条件.
- 证明了拟议的控制方案的有效性.
- 通过数值示例验证了理论发现.
结论:
- 提出的方法有效地实现了ICVMNNs中的指数级同步,时间延迟不同.
- 适应性同步策略是实用的和可实施的.
- 这项研究有助于理解和控制基于memristor的复杂神经系统.
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