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基于四次数值的基于memristor的神经网络的固定时间同步,具有混合延迟

Yanlin Zhang¹, Liqiao Yang¹, Kit Ian Kou¹

¹Department of Mathematics, Faculty of Science and Technology, University of Macau, Macau, 999078, China.

Neural networks : the official journal of the International Neural Network Society

|June 12, 2023

概括

这项研究实现了复杂的四次数值神经网络的固定时间同步 (FXTSYN),具有memristors和混合延迟. 新的方法确保快速和准确的同步,通过模拟来证明.

科学领域:

复杂的系统复杂的系统.
神经网络的神经网络的神经网络
控制理论控制理论

背景情况:

研究复杂神经网络中的同步对于先进的计算至关重要.
基于memristor的神经网络提供了独特的计算特性.
混合延迟在同步分析中带来了重大挑战.

研究的目的:

为了实现固定时间同步 (FXTSYN) 单边系数四次数值的基于memristor的神经网络 (UCQVMNNs) 混合延迟.
为快速同步开发新的控制策略.
提供明确的结算时间计算.

主要方法:

一种直接的分析方法,使用一个规范的平滑度.
对不连续系统应用集值映射和微分包容定理.
新型非线性控制器和莱普诺夫函数的设计.
利用不平等技术和新的FXTSYN理论.

主要成果:

建立了在UCQVMNN中实现FXTSYN的标准,延迟时间不同.
对同步的精确结算时间的明确计算.
通过数值模拟来证明拟议方法的有效性和适用性.

结论:

提出的直接分析方法有效地实现了UCQVMNNs的FXTSYN.
开发的控制策略确保了快速而准确的同步.
数字模拟验证了理论发现和实际应用.

关键词:

固定时间同步.基于memristor的神经网络的神经网络.非线性控制器的非线性控制器夸特里昂的价值被认为是夸特里昂价值.单边系数单边系数

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