针对随机神经网络的强大的噪声感知算法及其合性质
Yuqi Xiao1, Muideen Adegoke2, Chi-Sing Leung2
1Department of Electrical Engineering, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong, HKSAR, China; State Key Laboratory of Terahertz and Millimeter Waves, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong, HKSAR, China; Shenzhen Key Laboratory of Millimeter Wave and Wideband Wireless Communications, CityU Shenzhen Research Institute, Shenzhen, 518057, China.
概括
本研究引入了一种新的噪声感知随机向量功能链路网络 (NARNN) 算法,以提高随机神经网络 (RNN) 在不完美的条件下 (如重量噪声和数据异常值) 的可靠性. 与现有的强大的RNN方法相比,NARNN算法表现出卓越的性能.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 神经网络的神经网络的神经网络
背景情况:
- 随机神经网络 (RNN),包括随机向量功能链路网络 (RVFL) 和极端学习机器 (ELM),对于单隐层前网络 (SLFNs) 是有效的.
- RNN提供强大的近似能力,但在存在重量噪声和数据异常值时可能不可靠.
- 现有的RNN算法需要在不完美的运行条件下进行增强,以提高其稳定性.
研究的目的:
- 开发一个强大的RNN算法,解决重量噪声和训练数据异常值的综合影响.
- 引入一种新的目标功能和优化方法,以提高RVFL网络性能.
- 将拟议的算法扩展到集体深度RVFL (edRVFL) 网络和故障耐受性.
主要方法:
- 提出了一个新的目标功能,以减轻RVFL网络中重量噪声和异常值的影响.
- 半二次优化方法用于开发噪声感知RNN (NARNN) 算法.
- 讨论了NARNN融合的理论验证及其扩展到整体和容错配置.
主要成果:
- 拟议的NARNN算法有效地优化了针对杂和偏差数据而设计的目标函数.
- 在理论上已经确立了NARNN算法的收性质.
- 实验结果表明,NARNN的性能优于现有的最先进的强大的RNN算法.
结论:
- 对于面临重量噪声和数据异常值的RVFL网络,NARNN算法提供了一个强大的解决方案.
- 该NARNN框架适用于集体深度RVFL网络,并且可以扩展到故障耐受性.
- 拟议的方法在可靠性和性能方面比目前强大的RNN方法有显著的改进.
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