关于分数级多维值记忆神经网络的多态性统一分析
Jiarui Wang1, Song Zhu1, Chaoxu Mu2
1School of Mathematics, China University of Mining and Technology, Xuzhou, 221116, China.
概括
这项研究统一了分数顺序多维价值记忆神经网络 (FOMVMNNs) 与延迟中的多稳定性的分析. 建立了多稳定性和平衡点的新标准,提供了不那么保守和更容易验证的条件.
科学领域:
- 神经网络的神经网络的神经网络
- 非线性动力学是一种非线性动力学.
- 分数微积分的计算.
背景情况:
- 记忆神经网络 (MNN) 呈现出复杂的动态.
- 在MNN中的多稳定性对于高级计算至关重要.
- 分数顺序系统提供了增强的建模功能.
研究的目的:
- 为分数级多维值记忆神经网络 (FOMVMNNs) 开发一个统一的模型.
- 分析FOMVMNNs中的多稳定性和平衡点数量,具有无限的变化时间延迟.
- 为FOMVMNNs的多稳定性建立不那么保守的标准.
主要方法:
- 开发一个统一的分数顺序模型,包括实数,复数和四次数值.
- 使用统一方法分析平衡点.
- 构建与1-规范的利亚普诺夫函数来导出多稳定性标准.
- 对稳定平衡点的吸引盆地的估计.
主要成果:
- 获得了足够的条件来确定平衡点的数量.
- 统一的多元稳定性标准得出,证明不那么保守,更容易验证.
- 提出的方法通过两个数值模拟示例来验证.
结论:
- 统一分析为理解FOMVMNNs提供了一个强大的框架.
- 由此产生的标准提高了记忆神经网络的可预测性和设计.
- 这项研究有助于复杂动态系统及其应用的进步.
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