探索分叉,混沌和分数非线性动态模型的新型光学dromion解决方案
Sara Salem Alzaid1, Badr Saad T Alkahtani2
1Department of Mathematics, King Saud University, P.O. Box 2455, 11451, Riyadh, Saudi Arabia.
Scientific reports
|July 2, 2025
概括
本研究引入了一个时空分数单元神经元模型 (TSFSNM),用于分析各种科学领域的非线性动态. 分数建模技术揭示了神经元通信信号作为单元的洞察力.
科学领域:
- 数学物理学的数学物理.
- 非线性动力学是一种非线性动力学.
- 神经科学 神经科学
- 生物科学 生物科学
- 流体力学 流体力学 流体力学
- 等离子体物理学的物理学
- 地质化学 地质化学
背景情况:
- 复杂的非线性现象在各种科学和工程学科中普遍存在.
- 了解生物系统中的信号传播,就像神经元通信一样,需要先进的建模.
- 现有的模型可能无法完全捕捉这些系统中观察到的复杂的时空动态.
研究的目的:
- 探索时空分数单元神经元模型 (TSFSNM) 用于分析非线性动态.
- 应用先进的分数建模技术来构建神经元信号的分析解决方案.
- 通过分叉和灵敏度分析来研究分数顺序参数对系统行为的影响.
主要方法:
- 使用修改后的萨达尔子方程 (MSSE) 方法.
- 采用了改进的F扩展方法来构建分析解决方案.
- 进行了分叉和灵敏度分析,以了解参数的影响.
主要成果:
- 为代表神经元信号的单独声脉冲构建了明确的分析解决方案.
- 模拟这些脉冲为单子,以捕捉神经元通信的局部动态.
- 证明了分数顺序参数对系统行为的影响.
结论:
- 时空分数单元神经元模型 (TSFSNM) 有效地描述了复杂的波现象.
- 分数建模为神经元通信和其他非线性系统提供了重要的见解.
- 这些发现凸显了分数单子模型在各种科学和工程应用中的重要性.
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