在离散动态系统中稳定模型的研究,使用洛伦兹吸引器和克罗波托夫-帕霍莫夫神经网络
Ekaterina Antonova Gospodinova1
1Technical University of Sofia, Sofia 1000, Bulgaria.
Entropy (Basel, Switzerland)
|January 28, 2026
概括
这项研究表明,修改后的克罗波托夫-帕霍莫夫神经网络 (MRNN) 可以模拟混乱的洛伦兹吸引器. 该MRNN复制了洛伦茨系统.
科学领域:
- 混沌理论是一个混乱理论.
- 计算神经科学是一种计算神经科学.
- 动态系统是动态系统.
背景情况:
- 洛伦兹吸引子是混沌理论的一个基本模型,表现出复杂,不可预测的动态.
- 神经网络为模拟复杂系统提供了潜力,但模拟混乱吸引器仍然具有挑战性.
研究的目的:
- 建立离散的洛伦兹吸引子和修改的克罗波托夫-帕霍莫夫神经网络 (MRNN) 之间的动态类比.
- 为了证明MRNN作为混乱系统的神经模拟器的能力.
主要方法:
- 利用一个一维的峰值图从洛伦兹系统中提取最大值,保留混乱的流动属性.
- 使用波格达诺夫-赫布学习规则与散射反作为离散非线性运算符制定了MRNN.
- 采用理论分析和数值模拟来比较系统参数和稳定性条件.
主要成果:
- 结果表明,MRNN的参数复制了洛伦兹峰地图的模式层次结构.
- 在地图乘数和MRNN的光谱半径之间理论上建立了等效的稳定性条件.
- 数字图证实了洛伦兹模型控制参数和MRNN网络参数之间的对应.
结论:
- 修改后的克罗波托夫-帕霍莫夫神经网络作为洛伦茨吸引器的可行的神经模拟器.
- 该研究提供了关于自适应神经系统内自我组织和稳定的见解.
- 这项工作是混沌理论和神经网络建模之间的桥梁.
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