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通过可变增益间歇边界控制稳定延迟随机反应-扩散的科恩-格罗斯伯格神经网络

Yili Wang¹, Wu-Hua Chen¹, Shuning Niu¹

¹School of Electrical Engineering, Guangxi University, Nanning, 530004, China.

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

|September 8, 2025

概括

一种新的可变增益间歇边界控制 (VGIBC) 可以稳定延迟的随机神经网络. 这种自适应方法调整控制增益以提高性能,而不是使用恒定增益方法,从而提高网络稳定性.

科学领域:

控制理论控制理论
计算神经科学是一种神经科学.
应用数学应用数学应用数学

背景情况:

随机反应-扩散科恩-格罗斯伯格神经网络 (SRDCGNN) 是复杂的系统,需要强大的稳定方法.
传统的恒定增强间歇边界控制 (CGIBC) 方法在适应动态操作条件方面存在局限性.

研究的目的:

引入一种新的可变增益间歇边界控制 (VGIBC) 方法来稳定SRDCGNN.
通过根据运行时间动态调整控制增益来提高控制系统的适应性.

主要方法:

一个零碎的插值方法设计了跨工作间隔的时间变化的控制收益.
一个零碎的Lyapunov函数被用来分析间歇控制网络的切换动态.
基于Razumikhin的不同的溶液估计技术应用于活跃和休息控制时期.

主要成果:

导出了新的平均平方间歇性稳定标准,与CGIBC相比显示了较低的保守主义.
一个凸起的优化程序确定了最佳的控制增益函数,最大限度地减少控制率.
通过两个数值示例来验证VGIBC战略的有效性.

结论:

拟议的VGIBC方法为稳定延迟随机反应-扩散科恩-格罗斯伯格神经网络提供了更适应和更少的保守方法.
动态增益调整提高了对工作间隔长度变化的弹性.
这些发现为神经网络控制理论和应用提供了宝贵的进步.

关键词:

科恩·格罗斯伯格神经网络的神经网络间歇性边界控制间歇性边界控制随机反应-扩散系统随机反应-扩散系统时间变化的延迟.变量增益可以变化.

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