在不连续神经网络的固定时间同步的时间成本和能源成本之间的权衡分析
Qiaokun Kang1, Guoquan Ren1, Qintao Gan1
1Shijiazhuang Campus, Army Engineering University of PLA, Shijiazhuang 050003, China.
概括
本研究引入了一种新的方法,用于在不连续神经网络 (DNN) 中的固定时间同步 (FXTS),平衡时间和能源成本. 它建立了更准确的结算时间界限,并提供了同步的条件,通过遗传算法和数值示例进行验证.
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:
- 不连续神经网络 (DNN) 在同步方面存在挑战,原因是时间变化的延迟和参数不匹配.
- 现有的固定时间同步 (FXTS) 方法通常会产生保守的结果和不太准确的结算时间 (ST) 估计.
研究的目的:
- 在DNN中对FXTS进行时间和能源成本之间的权衡分析.
- 为DNN开发一个不那么保守的固定时间稳定定理.
- 在同步过程中估计能源成本并优化控制参数.
主要方法:
- 为DNN建立一个全面的固定时间稳定定定理.
- 在DNN中为FXTS推导出足够的条件,时间延迟不同,参数不匹配.
- 利用遗传算法通过优化控制参数来平衡时间和能源成本.
主要成果:
- 提出了一种新的,不那么保守的固定时间稳定性定理.
- 理论上已经证明了结算时间 (ST) 的更准确的上限.
- 获得了FXTS的足够条件,以及对能源成本上限的估计.
结论:
- 提出的方法为DNN中的FXTS提供了准确和可行的解决方案.
- 遗传算法有效地平衡了时间和能源成本.
- 数字示例验证了理论发现和控制机制的有效性.
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