用Soboleva激活函数对一般化注意力缺陷障碍模型的动态分析
L Moysis1, M Lawnik2, K F Kollias1
1Department of Electrical and Computer Engineering, University of Western Macedonia, 50100 Kozani, Greece.
Chaos (Woodbury, N.Y.)
|August 1, 2025
概括
这项研究通过引入新的参数来修改注意力缺陷障碍 (ADD) 建模的混乱神经网络. 改进后的模型展示了复杂的行为,为理解行为障碍提供了新的途径.
科学领域:
- 计算神经科学是一种神经科学.
- 动态系统理论 动态系统理论
- 数学心理学 数学心理学
背景情况:
- 现有的单维混乱神经网络模型为复杂的现象提供了有限的灵活性.
- 像注意力缺陷障碍 (ADD) 这样的行为障碍的建模需要复杂的计算框架.
研究的目的:
- 为了研究注意力缺陷障碍 (ADD) 的修改后的两神经元混乱神经网络模型.
- 探索新引入的控制参数对模型动态的影响.
主要方法:
- 取代了原来的激活函数,用索博莱瓦的高压触角函数.
- 使用相位图,分叉图和利亚普诺夫指数计算分析了系统行为.
主要成果:
- 修改后的模型显示了对原始系统的显著概括.
- 观测到复杂的动态现象,包括周期翻倍,混乱,危机,反同质性,共存的吸引物和.
- 证明了四个新的控制参数对系统动态的影响.
结论:
- 修改后的混乱神经网络为模拟行为障碍提供了更灵活的框架.
- 引入新的激活功能和参数为神经疾病的复杂动态提供了新的见解.
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