摘要
基于离散傅里叶变换-极限学习机(DFT-ELM)提出了一种新的单隐层前馈神经网络在线贯序学习算法,命名为"在线贯序-离散傅里叶变换-极限学习机"(OS-DFT-ELM).该算法能够逐个或逐段学习数据,随着新数据的逐渐到达,单隐层前馈神经网络的内权矩阵和外权矩阵得到逐步调整.该算法与在线贯序-极限学习机(OS-ELM)相比,具有更高的精度和鲁棒性.同时,通过实验和分析,表明OS-DFT-ELM具有优良性能.
In this paper,a kind of accurate and robust online sequential learning algorithm was proposed for single hidden layer feedforward networks.The algorithm is referred to as online sequential discrete Fourier transform-extreme learning machine（OS-DFT-ELM）.This approach is able to learn data one-by-one or chunk-by-chunk.During the growth of the data,input weights and output weights are adjusted incrementally.The proposed algorithm has a higher degree of accuracy and robustness compared to the approach referred to as online sequential-extreme learning machine（OS-ELM）.Two simulation examples were presented to show the excellent performance of the proposed approach.
出处
《上海交通大学学报》
EI
CAS
CSCD
北大核心
2015年第8期1137-1143,共7页
Journal of Shanghai Jiaotong University
基金
国家自然科学基金项目(11171137)
浙江省自然科学基金项目(LY13A010008)资助
关键词
单隐层前馈神经网络
在线贯序算法
极限学习机
single hidden layer feedforward networks
online sequential learning machine
extreme learning machine
