在参数不确定性下的模糊惯性神经网络的有限/固定时间同步的双机制自适应控制
Junshuang Zhou1, Guici Chen2, Song Zhu3
1College of Sciences, Wuhan University of Science and Technology, Wuhan, 430065, China.
概括
本研究探讨了使用两个自适应控制方法对模糊惯性神经网络 (FINNs) 的有限/固定时间同步. 它提出了一个双机制框架,用于在参数不确定性下进行强大的,时间受限的同步.
科学领域:
- 控制系统工程 控制系统工程
- 计算神经科学是一种神经科学.
- 人工智能的人工智能
背景情况:
- 模糊惯性神经网络 (FINNs) 是具有惯性动态,非线性和时间延迟的复杂系统.
- 在参数不确定性下,在FINNs中实现同步是具有挑战性的.
- 现有的方法可能无法同时提供高精度和计算效率.
研究的目的:
- 调查 FINNs 与参数不确定性的有限和固定时间同步.
- 开发和分析两个互补的自适应控制机制:减少和非减少方法.
- 为了比较这些方法在准确性,收速度和效率方面的表现.
主要方法:
- 为FINNs设计自适应同步控制器.
- 运用利亚普诺夫稳定理论和有限/固定时间稳定定理.
- 开发非减少 (保持全动态) 和减少 (简化动态) 控制方法.
主要成果:
- 非减小方法通过保持完整的惯性动力学来确保高同步精度.
- 简化方法通过系统简化实现更快的融合和计算效率.
- 这两种方法都为不确定的FINN提供了强大的,时间受限的同步标准.
结论:
- 建立了一种双重机制框架,以确保不确定的FINN在时间上受到严格限制的同步.
- 减少和非减少方法之间的选择取决于所需的精度和效率之间的平衡.
- 该研究提高了FINN同步的实时性能和可预测性.
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