动态分析和FPGA 在电磁辐射下实施新的分数顺序霍普菲尔德神经网络系统
1School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha 410114, China.
Biomimetics (Basel, Switzerland)
|December 22, 2023
概括
分数级神经网络提供先进的大脑记忆模拟. 本研究介绍了一种基于霍普菲尔德神经网络 (HNN) 的新型分数顺序混乱系统,证明了复杂的动态和FPGA实现的可行性.
科学领域:
- 神经科学是一个神经科学.
- 混沌理论 混沌理论
- 电气工程 电气工程
背景情况:
- 分数计算增强了人类大脑时间记忆的神经网络建模.
- 研究分数级神经网络揭示了超越整数级模型的复杂动态.
研究的目的:
- 提出使用霍普菲尔德神经网络 (HNN) 的磁控,基于memristor的,分数顺序的混乱系统.
- 在电磁辐射下分析系统的动态,并验证其实施.
主要方法:
- 使用Adomian分解法 (ADM) 来解决分数顺序系统.
- 进行动态模拟来探索系统行为.
- 在现场可编程门阵列 (FPGA) 上实现分数顺序的HNN系统.
主要成果:
- 该系统表现出丰富的动态,包括混乱,准周期性和方向可控的多滚动吸引器.
- 类似的对称动态行为与改变的辐射参数出现,同时保持恒定的分数顺序.
- 在FPGA的实验验证证证了理论发现.
结论:
- 拟议的分数顺序霍普菲尔德神经网络系统准确地模拟了复杂的类似大脑的记忆效应.
- 系统的动态行为是可控制的,对电磁辐射敏感.
- FPGA的实施证明了这种先进的神经网络模型的实际可行性.
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