通过新的参数干扰来延迟分数级BAM神经网络的分叉
Chengdai Huang1, Huanan Wang1, Heng Liu2
1School of Mathematics and Statistics, Xinyang Normal University, Xinyang 464000, China.
概括
本研究探讨了使用一种新的自我调节参数方法在分数顺序的双向联想性记忆神经网络 (FOBAMNN) 中的分叉. 调查结果显示,参数增加提高了系统稳定性,并引入了一种有效的方法来计算分叉关键点.
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
背景情况:
- 分数级双向联想记忆神经网络 (FOBAMNN) 呈现出复杂的动态.
- 两叉分析对于理解神经网络的稳定性和行为至关重要.
研究的目的:
- 用一个自我调节参数来研究FOBAMNN中的分叉.
- 开发一种有效的方法来计算分叉关键点.
主要方法:
- 基于自我调节参数的两叉分析,与时间延迟参数不同.
- 隐式数组方法的应用,以解决双叉参数的四方方方程.
主要成果:
- 通过使用自我调节参数,为FOBAMNN建立了新的分叉结果.
- 证明更大的自我调节参数可以提高系统的稳定性.
- 开发了一种有效的隐性阵列方法来计算分叉临界点,其性能优于法拉利的方法.
结论:
- 自调节参数为FOBAMNN分叉和稳定性提供了新的视角.
- 隐式数组方法为神经网络中的分支分析提供了一个计算效率高的工具.
- 这些发现丰富了对连续神经网络动态的理解,并为双叉点计算提供了可概括的技术.
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