使用卡普托-法布里齐奥演算子和辐射基函数神经网络建模非线性变量级分数混乱系统
Shah Sawar1, Muhammad Ayaz1, Musaad S Aldhabani2
1Department of Mathematics, Government Post Graduate College Dargai, Malakand, KPK, Pakistan.
Scientific reports
|February 9, 2026
概括
这项研究引入了混乱模型的可变分数顺序,提高了准确性和现实性. 射线基函数神经网络 (RBFNN) 能够有效地为现实世界的应用模型这些复杂的系统.
科学领域:
- 非线性动力学是一种非线性动力学.
- 分数微积分的计算.
- 计算智能是一种计算智能.
背景情况:
- 传统的分数混沌模型经常使用恒定分数顺序,限制了它们代表不断变化的系统内存的能力.
- 现实世界的动态系统表现出复杂的行为,需要更具适应性的建模方法.
研究的目的:
- 使用可变分数顺序方法研究非线性分数混乱模型的特征.
- 为了提高动态应用的混乱行为表示的准确性和现实性.
- 为了利用辐射基础功能神经网络 (RBFNN) 进行高效的建模和预测.
主要方法:
- 使用卡普托-法布里齐奥分数导数来描述系统动态.
- 利用辐射基础功能神经网络 (RBFNN) 进行学习和预测.
- 嵌入了可变的分数导数,以允许演变的记忆效应.
- 执行相位空间重建以分析不同分数顺序的系统演变.
主要成果:
- 与固定顺序模型相比,可变分数顺序模型显示出更高的灵活性和精度.
- 达到了低于[公式:参见文本]的最小误差值,证实了模型的可靠性和稳定性.
- 随着计算成本的降低,RBFNN提供了对复杂混乱行为的高效预测.
结论:
- 可变分数顺序建模为非线性混乱系统提供了更高的准确性和现实性.
- 拟议的框架是高效的,为研究和预测混乱动态提供了一种新的方法.
- 潜在的应用包括安全通信,控制系统和信号处理.
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