滞后的投影同步离散时间分数顺序四次数值的神经网络与时间延迟
Yan He1, Weiwei Zhang2, Hai Zhang3
1School of Mathematics and Physics, Anqing Normal University, Anqing 246133, China.
概括
这项研究涉及时延迟的分数顺序四次数值神经网络 (DTFO QVNNs) 中的滞后投影同步 (LPS). 新的标准确保使用Lyapunov方法和自适应控制器实现LPS.
科学领域:
- 复杂系统动力学 复杂系统动力学
- 计算神经科学是一种神经科学.
- 分数微积分的计算.
背景情况:
- 分数顺序系统比整数顺序系统提供了增强的建模功能.
- 四次值神经网络 (QVNNs) 适用于处理多维数据.
- 时间延迟在神经网络中很常见,可能会影响系统的稳定性和同步性.
研究的目的:
- 对具有时间延迟的离散时间分数顺序四次数值神经网络 (DTFO QVNNs) 进行延迟投影同步 (LPS) 的研究.
- 设计有效的线性和自适应反控制器来实现LPS.
- 建立严格的标准,以保证LPS在所考虑的系统.
主要方法:
- 构建一个DTFO QVNN系统,包括时间延迟.
- 开发使用信号功能的线性和自适应反控制器.
- 运用利亚普诺夫的直接方法,分数微分不等式技术和拉祖米金定理.
主要成果:
- 推导出足够性标准,以确保实现LPS.
- 证明拟议控制器的有效性.
- 通过数值模拟验证理论发现.
结论:
- 提出的方法成功地在DTFO QVNNs中实现了LPS,并且有时间延迟.
- 理论标准为分析这些复杂系统中的同步提供了一个强大的框架.
- 数字模拟证实了结果的实际适用性和理论有效性.
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