基于观察者的弹性消散性控制用于基于离散时间记忆器的神经网络,具有无限或有限的时间变化延迟
Kairong Tu1, Yu Xue2, Xian Zhang2
1School of Mathematical Science, Heilongjiang University, Harbin 150080, PR China.
概括
本研究引入了基于观察者的弹性控制,用于基于离散时间memristor的神经网络 (DTMBNNs) 具有时间变化的延迟. 新的方法确保了系统的稳定性和消散性,减少了计算复杂性.
科学领域:
- 控制理论 控制理论
- 神经网络的神经网络的神经网络
- 非线性系统是非线性系统.
背景情况:
- 基于memristor的神经网络 (MnN) 对于复杂的计算至关重要.
- 基于观察者的控制对于无法测量状态和时间延迟的系统至关重要.
- 在具有时间变化的延迟的离散时间系统中确保弹性消散性是具有挑战性的.
研究的目的:
- 为基于离散时间memristor的神经网络 (DTMBNNs) 开发基于观察者的弹性消散性控制策略.
- 解决具有无限制和有限制的时间变化延迟的系统.
- 为了减少理论分析和计算负载的复杂性.
主要方法:
- 一个Luenberger观察员和一个基于观察者的弹性控制器的设计.
- 构建一个包含错误系统和DTMBNNs与控制器的增强系统.
- 开发一种基于系统解决方案的新型估计方法,在没有莱普诺夫-克拉索夫斯基函数 (LKFs) 的情况下导出扩展指数散射条件.
- 提出一种算法,在衍生条件内解决非线性不等式.
主要成果:
- 为DTMBNNs建立了一个新的足够扩展的指数散射条件,具有无限的时间变化延迟.
- 对于具有有限的时间变化延迟的DTMBNNs,也得出了类似的条件.
- 提出的方法显著降低了理论推导的复杂性和计算工作量.
- 模拟示例验证了开发的控制策略的有效性.
结论:
- 该研究成功地提出了一种有效的基于观察者的弹性消散性控制,用于DTMBNNs的时间延迟变化.
- 这种基于新型系统解决方案的方法为传统的基于LKF的方法提供了一个计算效率高的替代方案.
- 这些发现有助于在实际应用中对复杂的神经网络系统进行可靠的控制.
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