具有多种激活功能的霍普菲尔德神经网络:可变作用梯度和电磁辐射效应的影响
Bertrand Frederick Boui A Boya1, Lyudmila Klimentyevna Babenko2, Jose De Jesus Rangel-Magdaleno3
1Institute of Computer Technology and Information Security, Southern Federal University, P.O. Box 347922, Taganrog, Russia; Unité de Recherche d'Automatique et d'Informatique Appliquée (UR-AIA), IUT-FV Bandjoun, University of Dschang, P.O. Box 134, Bandjoun, Cameroon.
本研究探讨了电磁辐射如何影响具有各种激活功能的霍普菲尔德神经网络 (HNN). 研究结果揭示了独特的动态行为和加密应用和神经疾病治疗的潜力.
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
- 人工智能的人工智能
背景情况:
- 激活函数及其梯度对于人工神经网络 (ANN) 动态至关重要,模仿生物神经元.
- 霍普菲尔德神经网络 (HNN) 是关联记忆的模型,但它们对外部刺激的反应需要进一步研究.
研究的目的:
- 在变化的电磁辐射下研究具有异质激活功能的HNN的动态.
- 探索网络内部配置和外部干扰之间的相互作用.
- 评估观察到的现象对加密应用和神经疾病治疗的潜力.
主要方法:
- 对HNN动态的计算模拟和理论分析.
- 调查网络对不同电磁辐射模式的反应.
- 通过FPGA实验框架和NIST统计测试进行验证,用于伪随机数生成.
主要成果:
- 在HNN模型中表现出反蒙性,爆发振荡和多滚动吸引器,没有外部干扰.
- 在不同的电磁辐射下观察到的振幅控制和初始偏移增强的独特平面共存.
- 生成的伪随机数字通过了NIST的统计测试,表明了密码的适用性.
结论:
- 像电磁辐射这样的外部干扰显著影响HNN动态和内部配置.
- 该研究为优化关联记忆任务和开发神经系统疾病新型诊断/治疗策略提供了见解.
- 这些发现通过动态系统理论将计算神经科学与医学应用联系起来.
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