前馈网络的一种高精度鲁棒在线贯序学习算法  

An Accurate and Robust Online Sequential Learning Algorithm for Feedforward Networks

作　　者：卢诚波[1] 梅颖[1] 

机构地区：[1]丽水学院工程与设计学院,浙江丽水323000

出　　处：《上海交通大学学报》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

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.

关 键 词：单隐层前馈神经网络 在线贯序算法 极限学习机 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] TP311[自动化与计算机技术—控制科学与工程]