强大的控制不确定的时间延迟马科维斯跳跃四次数值神经网络,受部分已知的过渡概率的影响:直接四次数方法
Jinlong Shu1, Baowei Wu1, Lianglin Xiong2
1School of Mathematics and Statistics, Shaanxi Normal University, Xi'an, 710119 China.
Cognitive neurodynamics
|June 2, 2023
概括
这项研究引入了一种新的直接四分离子方法,用于对不确定的时间延迟马科维亚跳跃四分离子值神经网络 (MJQVNNs) 的强有力的随机稳定. 它在不确定性和干扰下确保了系统的稳定性和性能.
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
- 四边子数学的数学
背景情况:
- 在复杂的系统中处理不确定性和时间延迟是具有挑战性的.
- 马科维亚跳跃系统需要专门的控制策略.
- 四次数值的神经网络在处理高维数据方面具有优势.
研究的目的:
- 开发一种强大的随机稳定方法,用于不确定时间延迟的马科维亚跳跃四分数值神经网络 (MJQVNNs).
- 为了处理具有部分已知的过渡概率的系统.
- 为了减少输入干扰对受控输出的影响.
主要方法:
- 建议采用直接的四子方法,避免系统分解.
- 实值形不等式扩展到四次元域,以准确地估计利亚普诺夫-克拉索夫斯基函数导数.
- 设计了模式依赖状态反控制器.
主要成果:
- 对于具有允许不确定性的MJQVNNs,我们得出了强大的随机稳定条件.
- 提出的方法有效地将输入干扰的影响降低到指定的性能水平.
- 与传统方法相比,直接四边形方法简化了分析.
结论:
- 拟议的直接四子方法为MJQVNNs的强有力的随机稳定提供了一种有效的方法.
- 该技术在存在不确定性和时间延迟的情况下确保了系统的稳定性和性能.
- 数字示例验证了开发的控制策略的有效性.
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