$ \mathcal{L}_{2}-\mathcal{L}_{\infty} $ 对具有非必要可微分时间变化延迟的记忆性NN的控制
1School of Computer Science and Technology, Anhui University of Technology, Ma'anshan 243032, China.
Mathematical biosciences and engineering : MBE
|July 28, 2023
概括
这项研究引入了对具有时间变化的延迟的记忆神经网络 (MNN) 的强有力的控制. 它使用基于Lyapunov的新方法和控制器设计的线性矩阵不等式来确保稳定性.
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:
- 记忆神经网络 (MNN) 对于复杂的计算至关重要.
- 随时间变化的MNN延迟带来了稳定性挑战.
- 强大的控制对于可靠的MNN性能至关重要.
研究的目的:
- 设计一个输出反控制器,用于MNN与非可区分的时间变化延迟.
- 为了确保MNN系统的稳定性.
- 为控制器开发一个系统的设计方法.
主要方法:
- 使用莱普诺夫函数来进行稳定性分析.
- 应用贝塞尔-莱根德尔不等式和凸组合不等式.
- 在控制器合成中使用线性矩阵不等式 (LMI).
- 在MNN动态中脱非线性项.
主要成果:
- 建立了一个新的标准 $\mathcal{L}_2 -\mathcal{L}_{\infty}$ 延迟的mnns的稳定性.
- 为输出反控制器提供了一个基于LMI的具体设计方案.
- 提出的方法通过两个说明性示例来验证.
结论:
- 开发的 $\mathcal{L}_2 -\mathcal{L}_{\infty}$ 稳定性标准对于延迟的 MNN 有效.
- 基于LMI的控制器设计确保了系统的稳定性和性能.
- 这项研究有助于对复杂的神经网络系统的强有力的控制.
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