不对称神经网络中的混乱和多层吸引器与离散的分数记忆器相结合
Shaobo He1, D Vignesh2, Lamberto Rondoni3
1School of Automation and Electronic Information, Xiangtan University, Xiangtan, 411105, China.
概括
这项研究引入了一种使用分数差异记忆器的新型不对称神经网络模型. 这项研究强调了memristor属性如何影响计算智能系统中的复杂动态和混乱行为.
科学领域:
- 计算智能是一种计算智能.
- 非线性动力学是一种非线性动力学.
- 基于memristor的计算系统
背景情况:
- 记忆器对于先进的计算架构至关重要.
- 分数顺序系统提供复杂的动态.
- 神经网络是人工智能的基础.
研究的目的:
- 提出一种新型的不对称神经网络模型,包括分数差异记忆器.
- 分析拟议系统的动态行为和稳定性.
- 调查memristor特征对神经网络动态的影响.
主要方法:
- 开发一种结合的神经网络模型,使用两种类型的分数差异记忆器 (超标触角和正弦函数).
- 在平衡点分析系统稳定性,分叉分析和计算最大的利亚普诺夫指数.
- 数字模拟来演示memristor状态演变,多层吸引器和混乱行为.
主要成果:
- 该模型表现出复杂的动态,包括共存的状态变量和多层吸引器在使用正弦记忆器时,取决于初始条件.
- 记忆传导 (突触重量) 显著影响系统的复杂动态特征.
- 数字模拟验证了分析结果,证实了状态变量的混乱反应.
结论:
- 分数差异记忆器的集成为新的计算智能模型提供了一条途径.
- 记忆器特性,特别是记忆导,是神经网络系统复杂性和混乱行为的关键决定因素.
- 这项研究验证了基于memristor的神经网络在产生复杂和不可预测的动态方面的潜力.
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