延迟马科维跳跃神经网络的扩展散散性分析通过基于延迟功能的利亚普诺夫功能性神经网络
Guoqiang Kong1, Liangdong Guo1
1School of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan, 114051, PR China.
概括
这项研究引入了一种新方法,用于分析延迟马科维亚跳跃神经网络中的扩展消散性,其过渡速率未知. 这种方法提供了更灵活的约束,导致不那么保守的稳定性标准.
科学领域:
- 控制理论 控制理论
- 人工智能的人工智能
- 网络科学 网络科学
背景情况:
- 马科维斯跳跃神经网络 (MJNNs) 对于模拟突变系统至关重要.
- 延迟的MJNN与部分未知的过渡率 (PUTR) 构成重要的控制挑战.
- 确保系统的稳定性和性能,以及扩展的消散性,对于可靠的操作至关重要.
研究的目的:
- 为了研究延迟MJNN与PUTRs的扩展消散性问题.
- 开发新的方法,克服传统方法的局限性.
- 为这些复杂的系统推导出不那么保守的稳定性标准.
主要方法:
- 构建了一个基于延迟函数的新型Lyapunov-Krasovskii函数 (LKF),将矩阵的约束转移到延迟函数.
- 提出了一个更一般的互凸不等式 (RCI),放松了矩阵对称性要求.
- 这些方法被结合起来,得出新的延迟依赖性标准.
主要成果:
- 拟议的LKF提供了增强的设计灵活性和可扩展性.
- 一般化的 RCI 允许在系统矩阵上设置不那么严格的条件.
- 导出了不那么保守的依赖延迟的标准,确保了延迟的MJNN与PUTR的扩展消散性.
结论:
- 新的方法提供了一个更有效的框架,用于分析延迟的MJNN与PUTRs.
- 与现有方法相比,衍生出的标准显示了性能改善和减少了保守主义.
- 数字示例验证了拟议方法的优越性和实际适用性.
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