具有不连续激活的完全四次数值延迟神经网络的固定时间稳定:基于指数控制的直接方法
Jinshui Ren1, Ziwei Guo1, Zhen Liu1
1College of Mathematics and System Science, Xinjiang University, Urumqi, 830017, China.
ISA transactions
|October 17, 2025
概括
本研究介绍了一种直接指数控制方法,用于在固定的时间内稳定四次数值的神经网络. 这种方法简化了分析,并减少了时间保守主义,以提高性能.
科学领域:
- 复杂系统与控制理论
- 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:
- 四方值神经网络 (QVNNs) 在建模复杂动态方面具有优势.
- 通过不连续的激活和延迟稳定神经网络仍然具有挑战性.
- 现有的方法通常依赖于分离实值子系统,限制直接分析.
研究的目的:
- 开发一个直接的固定时间稳定方法,用于完全四次数值的神经网络.
- 提出一个新的指数控制方案,直接适用于QVNNs.
- 通过基于非分解的分析,建立简洁的稳定条件.
主要方法:
- 一个直接指数控制方案是为四次数值系统设计的.
- 基于非分解的直接分析方法用于趋同分析.
- 泰勒扩展与完全保留用于优化控制功能和减少保守主义.
主要成果:
- 拟议的指数控制直接稳定QVNN在固定的时间内.
- 非分解分析产生了简化和有效的稳定条件.
- 通过泰勒扩展,在稳定时间估计中减少了保守主义.
结论:
- 直接指数控制方法为QVNNs的固定时间稳定提供了一种有效的方法.
- 非分解分析简化了四次数值系统的理论导数.
- 该方法为控制复杂的神经网络动态提供了一个有希望的方向.
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