基于采样数据的控制稳定模糊的惯性四次数值延迟神经网络与参数不确定性
Ziye Zhang1, Shuwen Lv1, Runan Guo2
1College of Mathematics and Systems Science, Shandong University of Science and Technology, Qingdao, 266590, China.
这项研究稳定了模糊的惯性四次数值神经网络 (FIQVNNs),使用一种新的非脆弱的采样数据控制器来缓解延迟和不确定性. 这些发现为复杂的神经网络系统提供了新的稳定性标准.
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:
- 模糊的惯性四等值神经网络 (FIQVNNs) 是复杂的系统,由于时间变化的延迟和参数不确定性,容易发生不稳定.
- 当前的控制方法在处理这种不确定性时经常表现为保守主义.
研究的目的:
- 为了实现FIQVNNs的指数稳定,具有时间变化的延迟和参数不确定性.
- 为增强稳定性保证设计一种新型非脆弱的采样数据控制器.
- 为了减少与四次元域稳定性分析相关的保守主义.
主要方法:
- 一个非脆弱的采样数据控制器的设计,具有时间延迟期限.
- 将改进的互凸不等式扩展到四边子域.
- 构建利亚普诺夫-克拉索夫斯基函数 (LKF) 和应用先进的不等式技术.
- 为稳定性分析制定精细的分析框架.
主要成果:
- 为FIQVNNs获得足够的稳定性条件.
- 条件以线性矩阵不等式 (LMI) 的形式呈现.
- 拟议的控制者有效地确保了所考虑的FIQVNNs的稳定性.
结论:
- 该研究成功地提出了一种稳定FIQVNNs的新方法.
- 开发的基于LMI的条件为稳定性分析提供了标准.
- 数字模拟和应用示例验证了拟议方法的有效性.
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